storj/go.sum

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cloud.google.com/go v0.26.0/go.mod h1:aQUYkXzVsufM+DwF1aE+0xfcU+56JwCaLick0ClmMTw=
cloud.google.com/go v0.34.0/go.mod h1:aQUYkXzVsufM+DwF1aE+0xfcU+56JwCaLick0ClmMTw=
cloud.google.com/go v0.37.4/go.mod h1:NHPJ89PdicEuT9hdPXMROBD91xc5uRDxsMtSB16k7hw=
pkg/process: Now that we are trying to identify the root cause of the satellite load limitations (i.e. currently the satellite has a max ability of 400 rps for uploads and we need this to be higher), we are using the golang diagnostic tools to collect insight into what the bottlenecks are. We currently have a debug endpoint to gather some cpu and mem data, but it could be useful to have continuous profiling. GCP stackdriver has support for continuous profiling so lets set that up and see if it is helpful to gather more data. This PR adds support for [GCP continuous profiler](https://cloud.google.com/profiler) which allows enabling continuous cpu/mem profiling and the stats are sent to stackdriver in google cloud console. To enable the continuous profiling for a storj component, do the following: - prereq: the workload must be running in GKE and have Stackdriver Profiling IAM role permissions - provide the config flag `debug.profilename` in the config.yaml file for the workload (i.e. satellite api process, etc). The profilename should be the workload name, for example "satellite-api". - once the above config flag is provided, the profiler will be initialized and profiling stats will automatically be sent to GCP project where the workload is running and viewable in the Stackdriver Profile page in the console The current implementation assumes the workload is running in GKE, however if we find if useful we can add support to enable this from anywhere. But for simplicity, its configured this way assuming the main goal is to enable in production systems. Change-Id: Ibf8ebe2df7bf06fdd4951ee6a1e48854dd36ad47
2020-02-25 16:46:12 +00:00
cloud.google.com/go v0.38.0/go.mod h1:990N+gfupTy94rShfmMCWGDn0LpTmnzTp2qbd1dvSRU=
cloud.google.com/go v0.44.1/go.mod h1:iSa0KzasP4Uvy3f1mN/7PiObzGgflwredwwASm/v6AU=
cloud.google.com/go v0.44.2/go.mod h1:60680Gw3Yr4ikxnPRS/oxxkBccT6SA1yMk63TGekxKY=
cloud.google.com/go v0.45.1/go.mod h1:RpBamKRgapWJb87xiFSdk4g1CME7QZg3uwTez+TSTjc=
cloud.google.com/go v0.46.3/go.mod h1:a6bKKbmY7er1mI7TEI4lsAkts/mkhTSZK8w33B4RAg0=
cloud.google.com/go v0.52.0 h1:GGslhk/BU052LPlnI1vpp3fcbUs+hQ3E+Doti/3/vF8=
cloud.google.com/go v0.52.0/go.mod h1:pXajvRH/6o3+F9jDHZWQ5PbGhn+o8w9qiu/CffaVdO4=
cloud.google.com/go/bigquery v1.0.1 h1:hL+ycaJpVE9M7nLoiXb/Pn10ENE2u+oddxbD8uu0ZVU=
pkg/process: Now that we are trying to identify the root cause of the satellite load limitations (i.e. currently the satellite has a max ability of 400 rps for uploads and we need this to be higher), we are using the golang diagnostic tools to collect insight into what the bottlenecks are. We currently have a debug endpoint to gather some cpu and mem data, but it could be useful to have continuous profiling. GCP stackdriver has support for continuous profiling so lets set that up and see if it is helpful to gather more data. This PR adds support for [GCP continuous profiler](https://cloud.google.com/profiler) which allows enabling continuous cpu/mem profiling and the stats are sent to stackdriver in google cloud console. To enable the continuous profiling for a storj component, do the following: - prereq: the workload must be running in GKE and have Stackdriver Profiling IAM role permissions - provide the config flag `debug.profilename` in the config.yaml file for the workload (i.e. satellite api process, etc). The profilename should be the workload name, for example "satellite-api". - once the above config flag is provided, the profiler will be initialized and profiling stats will automatically be sent to GCP project where the workload is running and viewable in the Stackdriver Profile page in the console The current implementation assumes the workload is running in GKE, however if we find if useful we can add support to enable this from anywhere. But for simplicity, its configured this way assuming the main goal is to enable in production systems. Change-Id: Ibf8ebe2df7bf06fdd4951ee6a1e48854dd36ad47
2020-02-25 16:46:12 +00:00
cloud.google.com/go/bigquery v1.0.1/go.mod h1:i/xbL2UlR5RvWAURpBYZTtm/cXjCha9lbfbpx4poX+o=
cloud.google.com/go/datastore v1.0.0 h1:Kt+gOPPp2LEPWp8CSfxhsM8ik9CcyE/gYu+0r+RnZvM=
pkg/process: Now that we are trying to identify the root cause of the satellite load limitations (i.e. currently the satellite has a max ability of 400 rps for uploads and we need this to be higher), we are using the golang diagnostic tools to collect insight into what the bottlenecks are. We currently have a debug endpoint to gather some cpu and mem data, but it could be useful to have continuous profiling. GCP stackdriver has support for continuous profiling so lets set that up and see if it is helpful to gather more data. This PR adds support for [GCP continuous profiler](https://cloud.google.com/profiler) which allows enabling continuous cpu/mem profiling and the stats are sent to stackdriver in google cloud console. To enable the continuous profiling for a storj component, do the following: - prereq: the workload must be running in GKE and have Stackdriver Profiling IAM role permissions - provide the config flag `debug.profilename` in the config.yaml file for the workload (i.e. satellite api process, etc). The profilename should be the workload name, for example "satellite-api". - once the above config flag is provided, the profiler will be initialized and profiling stats will automatically be sent to GCP project where the workload is running and viewable in the Stackdriver Profile page in the console The current implementation assumes the workload is running in GKE, however if we find if useful we can add support to enable this from anywhere. But for simplicity, its configured this way assuming the main goal is to enable in production systems. Change-Id: Ibf8ebe2df7bf06fdd4951ee6a1e48854dd36ad47
2020-02-25 16:46:12 +00:00
cloud.google.com/go/datastore v1.0.0/go.mod h1:LXYbyblFSglQ5pkeyhO+Qmw7ukd3C+pD7TKLgZqpHYE=
cloud.google.com/go/pubsub v1.0.1 h1:W9tAK3E57P75u0XLLR82LZyw8VpAnhmyTOxW9qzmyj8=
pkg/process: Now that we are trying to identify the root cause of the satellite load limitations (i.e. currently the satellite has a max ability of 400 rps for uploads and we need this to be higher), we are using the golang diagnostic tools to collect insight into what the bottlenecks are. We currently have a debug endpoint to gather some cpu and mem data, but it could be useful to have continuous profiling. GCP stackdriver has support for continuous profiling so lets set that up and see if it is helpful to gather more data. This PR adds support for [GCP continuous profiler](https://cloud.google.com/profiler) which allows enabling continuous cpu/mem profiling and the stats are sent to stackdriver in google cloud console. To enable the continuous profiling for a storj component, do the following: - prereq: the workload must be running in GKE and have Stackdriver Profiling IAM role permissions - provide the config flag `debug.profilename` in the config.yaml file for the workload (i.e. satellite api process, etc). The profilename should be the workload name, for example "satellite-api". - once the above config flag is provided, the profiler will be initialized and profiling stats will automatically be sent to GCP project where the workload is running and viewable in the Stackdriver Profile page in the console The current implementation assumes the workload is running in GKE, however if we find if useful we can add support to enable this from anywhere. But for simplicity, its configured this way assuming the main goal is to enable in production systems. Change-Id: Ibf8ebe2df7bf06fdd4951ee6a1e48854dd36ad47
2020-02-25 16:46:12 +00:00
cloud.google.com/go/pubsub v1.0.1/go.mod h1:R0Gpsv3s54REJCy4fxDixWD93lHJMoZTyQ2kNxGRt3I=
cloud.google.com/go/storage v1.0.0 h1:VV2nUM3wwLLGh9lSABFgZMjInyUbJeaRSE64WuAIQ+4=
pkg/process: Now that we are trying to identify the root cause of the satellite load limitations (i.e. currently the satellite has a max ability of 400 rps for uploads and we need this to be higher), we are using the golang diagnostic tools to collect insight into what the bottlenecks are. We currently have a debug endpoint to gather some cpu and mem data, but it could be useful to have continuous profiling. GCP stackdriver has support for continuous profiling so lets set that up and see if it is helpful to gather more data. This PR adds support for [GCP continuous profiler](https://cloud.google.com/profiler) which allows enabling continuous cpu/mem profiling and the stats are sent to stackdriver in google cloud console. To enable the continuous profiling for a storj component, do the following: - prereq: the workload must be running in GKE and have Stackdriver Profiling IAM role permissions - provide the config flag `debug.profilename` in the config.yaml file for the workload (i.e. satellite api process, etc). The profilename should be the workload name, for example "satellite-api". - once the above config flag is provided, the profiler will be initialized and profiling stats will automatically be sent to GCP project where the workload is running and viewable in the Stackdriver Profile page in the console The current implementation assumes the workload is running in GKE, however if we find if useful we can add support to enable this from anywhere. But for simplicity, its configured this way assuming the main goal is to enable in production systems. Change-Id: Ibf8ebe2df7bf06fdd4951ee6a1e48854dd36ad47
2020-02-25 16:46:12 +00:00
cloud.google.com/go/storage v1.0.0/go.mod h1:IhtSnM/ZTZV8YYJWCY8RULGVqBDmpoyjwiyrjsg+URw=
dmitri.shuralyov.com/gpu/mtl v0.0.0-20190408044501-666a987793e9/go.mod h1:H6x//7gZCb22OMCxBHrMx7a5I7Hp++hsVxbQ4BYO7hU=
github.com/Azure/go-ansiterm v0.0.0-20170929234023-d6e3b3328b78 h1:w+iIsaOQNcT7OZ575w+acHgRric5iCyQh+xv+KJ4HB8=
github.com/Azure/go-ansiterm v0.0.0-20170929234023-d6e3b3328b78/go.mod h1:LmzpDX56iTiv29bbRTIsUNlaFfuhWRQBWjQdVyAevI8=
2018-10-06 18:57:53 +01:00
github.com/BurntSushi/toml v0.3.1 h1:WXkYYl6Yr3qBf1K79EBnL4mak0OimBfB0XUf9Vl28OQ=
github.com/BurntSushi/toml v0.3.1/go.mod h1:xHWCNGjB5oqiDr8zfno3MHue2Ht5sIBksp03qcyfWMU=
pkg/process: Now that we are trying to identify the root cause of the satellite load limitations (i.e. currently the satellite has a max ability of 400 rps for uploads and we need this to be higher), we are using the golang diagnostic tools to collect insight into what the bottlenecks are. We currently have a debug endpoint to gather some cpu and mem data, but it could be useful to have continuous profiling. GCP stackdriver has support for continuous profiling so lets set that up and see if it is helpful to gather more data. This PR adds support for [GCP continuous profiler](https://cloud.google.com/profiler) which allows enabling continuous cpu/mem profiling and the stats are sent to stackdriver in google cloud console. To enable the continuous profiling for a storj component, do the following: - prereq: the workload must be running in GKE and have Stackdriver Profiling IAM role permissions - provide the config flag `debug.profilename` in the config.yaml file for the workload (i.e. satellite api process, etc). The profilename should be the workload name, for example "satellite-api". - once the above config flag is provided, the profiler will be initialized and profiling stats will automatically be sent to GCP project where the workload is running and viewable in the Stackdriver Profile page in the console The current implementation assumes the workload is running in GKE, however if we find if useful we can add support to enable this from anywhere. But for simplicity, its configured this way assuming the main goal is to enable in production systems. Change-Id: Ibf8ebe2df7bf06fdd4951ee6a1e48854dd36ad47
2020-02-25 16:46:12 +00:00
github.com/BurntSushi/xgb v0.0.0-20160522181843-27f122750802/go.mod h1:IVnqGOEym/WlBOVXweHU+Q+/VP0lqqI8lqeDx9IjBqo=
github.com/Microsoft/go-winio v0.4.11 h1:zoIOcVf0xPN1tnMVbTtEdI+P8OofVk3NObnwOQ6nK2Q=
github.com/Microsoft/go-winio v0.4.11/go.mod h1:VhR8bwka0BXejwEJY73c50VrPtXAaKcyvVC4A4RozmA=
github.com/Nvveen/Gotty v0.0.0-20120604004816-cd527374f1e5/go.mod h1:lmUJ/7eu/Q8D7ML55dXQrVaamCz2vxCfdQBasLZfHKk=
github.com/OneOfOne/xxhash v1.2.2/go.mod h1:HSdplMjZKSmBqAxg5vPj2TmRDmfkzw+cTzAElWljhcU=
github.com/Shopify/sarama v1.19.0/go.mod h1:FVkBWblsNy7DGZRfXLU0O9RCGt5g3g3yEuWXgklEdEo=
github.com/Shopify/toxiproxy v2.1.4+incompatible/go.mod h1:OXgGpZ6Cli1/URJOF1DMxUHB2q5Ap20/P/eIdh4G0pI=
github.com/VividCortex/ewma v1.1.1 h1:MnEK4VOv6n0RSY4vtRe3h11qjxL3+t0B8yOL8iMXdcM=
github.com/VividCortex/ewma v1.1.1/go.mod h1:2Tkkvm3sRDVXaiyucHiACn4cqf7DpdyLvmxzcbUokwA=
github.com/aead/siphash v1.0.1/go.mod h1:Nywa3cDsYNNK3gaciGTWPwHt0wlpNV15vwmswBAUSII=
github.com/alecthomas/template v0.0.0-20160405071501-a0175ee3bccc/go.mod h1:LOuyumcjzFXgccqObfd/Ljyb9UuFJ6TxHnclSeseNhc=
github.com/alecthomas/units v0.0.0-20151022065526-2efee857e7cf/go.mod h1:ybxpYRFXyAe+OPACYpWeL0wqObRcbAqCMya13uyzqw0=
github.com/alessio/shellescape v0.0.0-20190409004728-b115ca0f9053 h1:H/GMMKYPkEIC3DF/JWQz8Pdd+Feifov2EIgGfNpeogI=
github.com/alessio/shellescape v0.0.0-20190409004728-b115ca0f9053/go.mod h1:xW8sBma2LE3QxFSzCnH9qe6gAE2yO9GvQaWwX89HxbE=
2018-10-06 18:57:53 +01:00
github.com/alicebob/gopher-json v0.0.0-20180125190556-5a6b3ba71ee6 h1:45bxf7AZMwWcqkLzDAQugVEwedisr5nRJ1r+7LYnv0U=
github.com/alicebob/gopher-json v0.0.0-20180125190556-5a6b3ba71ee6/go.mod h1:SGnFV6hVsYE877CKEZ6tDNTjaSXYUk6QqoIK6PrAtcc=
github.com/alicebob/miniredis/v2 v2.11.1 h1:wuZ/ZHHELZ8DUF5sahK2T6V4Do2SdyKHnjrl/opkP8w=
github.com/alicebob/miniredis/v2 v2.11.1/go.mod h1:UA48pmi7aSazcGAvcdKcBB49z521IC9VjTTRz2nIaJE=
github.com/apache/thrift v0.12.0 h1:pODnxUFNcjP9UTLZGTdeh+j16A8lJbRvD3rOtrk/7bs=
github.com/apache/thrift v0.12.0/go.mod h1:cp2SuWMxlEZw2r+iP2GNCdIi4C1qmUzdZFSVb+bacwQ=
github.com/armon/consul-api v0.0.0-20180202201655-eb2c6b5be1b6/go.mod h1:grANhF5doyWs3UAsr3K4I6qtAmlQcZDesFNEHPZAzj8=
github.com/aws/aws-sdk-go v1.17.7/go.mod h1:KmX6BPdI08NWTb3/sm4ZGu5ShLoqVDhKgpiN924inxo=
2018-10-06 18:57:53 +01:00
github.com/beorn7/perks v0.0.0-20180321164747-3a771d992973/go.mod h1:Dwedo/Wpr24TaqPxmxbtue+5NUziq4I4S80YR8gNf3Q=
github.com/beorn7/perks v1.0.0 h1:HWo1m869IqiPhD389kmkxeTalrjNbbJTC8LXupb+sl0=
github.com/beorn7/perks v1.0.0/go.mod h1:KWe93zE9D1o94FZ5RNwFwVgaQK1VOXiVxmqh+CedLV8=
github.com/bitly/go-hostpool v0.0.0-20171023180738-a3a6125de932/go.mod h1:NOuUCSz6Q9T7+igc/hlvDOUdtWKryOrtFyIVABv/p7k=
2019-09-20 15:22:40 +01:00
github.com/blang/semver v3.5.1+incompatible h1:cQNTCjp13qL8KC3Nbxr/y2Bqb63oX6wdnnjpJbkM4JQ=
github.com/blang/semver v3.5.1+incompatible/go.mod h1:kRBLl5iJ+tD4TcOOxsy/0fnwebNt5EWlYSAyrTnjyyk=
github.com/bmizerany/assert v0.0.0-20160611221934-b7ed37b82869/go.mod h1:Ekp36dRnpXw/yCqJaO+ZrUyxD+3VXMFFr56k5XYrpB4=
github.com/btcsuite/btcd v0.20.1-beta/go.mod h1:wVuoA8VJLEcwgqHBwHmzLRazpKxTv13Px/pDuV7OomQ=
github.com/btcsuite/btclog v0.0.0-20170628155309-84c8d2346e9f/go.mod h1:TdznJufoqS23FtqVCzL0ZqgP5MqXbb4fg/WgDys70nA=
github.com/btcsuite/btcutil v0.0.0-20190425235716-9e5f4b9a998d/go.mod h1:+5NJ2+qvTyV9exUAL/rxXi3DcLg2Ts+ymUAY5y4NvMg=
github.com/btcsuite/btcutil v1.0.1 h1:GKOz8BnRjYrb/JTKgaOk+zh26NWNdSNvdvv0xoAZMSA=
github.com/btcsuite/btcutil v1.0.1/go.mod h1:j9HUFwoQRsZL3V4n+qG+CUnEGHOarIxfC3Le2Yhbcts=
github.com/btcsuite/btcutil v1.0.2 h1:9iZ1Terx9fMIOtq1VrwdqfsATL9MC2l8ZrUY6YZ2uts=
github.com/btcsuite/btcutil v1.0.2/go.mod h1:j9HUFwoQRsZL3V4n+qG+CUnEGHOarIxfC3Le2Yhbcts=
github.com/btcsuite/go-socks v0.0.0-20170105172521-4720035b7bfd/go.mod h1:HHNXQzUsZCxOoE+CPiyCTO6x34Zs86zZUiwtpXoGdtg=
github.com/btcsuite/goleveldb v0.0.0-20160330041536-7834afc9e8cd/go.mod h1:F+uVaaLLH7j4eDXPRvw78tMflu7Ie2bzYOH4Y8rRKBY=
github.com/btcsuite/snappy-go v0.0.0-20151229074030-0bdef8d06723/go.mod h1:8woku9dyThutzjeg+3xrA5iCpBRH8XEEg3lh6TiUghc=
github.com/btcsuite/websocket v0.0.0-20150119174127-31079b680792/go.mod h1:ghJtEyQwv5/p4Mg4C0fgbePVuGr935/5ddU9Z3TmDRY=
github.com/btcsuite/winsvc v1.0.0/go.mod h1:jsenWakMcC0zFBFurPLEAyrnc/teJEM1O46fmI40EZs=
github.com/calebcase/tmpfile v1.0.1 h1:vD8FSrbsbexhep39/6mvtbIHS3GzIRqiprDNCF6QqSk=
github.com/calebcase/tmpfile v1.0.1/go.mod h1:iErLeG/iqJr8LaQ/gYRv4GXdqssi3jg4iSzvrA06/lw=
github.com/calebcase/tmpfile v1.0.2-0.20200602150926-3af473ef8439 h1:fqGdSbbWVbQqNCQXd/dyZ7Bl+u8R2X7QCiNzwgyUa/M=
github.com/calebcase/tmpfile v1.0.2-0.20200602150926-3af473ef8439/go.mod h1:iErLeG/iqJr8LaQ/gYRv4GXdqssi3jg4iSzvrA06/lw=
github.com/calebcase/tmpfile v1.0.2 h1:1AGuhKiUu4J6wxz6lxuF6ck3f8G2kaV6KSEny0RGCig=
github.com/calebcase/tmpfile v1.0.2/go.mod h1:iErLeG/iqJr8LaQ/gYRv4GXdqssi3jg4iSzvrA06/lw=
pkg/process: Now that we are trying to identify the root cause of the satellite load limitations (i.e. currently the satellite has a max ability of 400 rps for uploads and we need this to be higher), we are using the golang diagnostic tools to collect insight into what the bottlenecks are. We currently have a debug endpoint to gather some cpu and mem data, but it could be useful to have continuous profiling. GCP stackdriver has support for continuous profiling so lets set that up and see if it is helpful to gather more data. This PR adds support for [GCP continuous profiler](https://cloud.google.com/profiler) which allows enabling continuous cpu/mem profiling and the stats are sent to stackdriver in google cloud console. To enable the continuous profiling for a storj component, do the following: - prereq: the workload must be running in GKE and have Stackdriver Profiling IAM role permissions - provide the config flag `debug.profilename` in the config.yaml file for the workload (i.e. satellite api process, etc). The profilename should be the workload name, for example "satellite-api". - once the above config flag is provided, the profiler will be initialized and profiling stats will automatically be sent to GCP project where the workload is running and viewable in the Stackdriver Profile page in the console The current implementation assumes the workload is running in GKE, however if we find if useful we can add support to enable this from anywhere. But for simplicity, its configured this way assuming the main goal is to enable in production systems. Change-Id: Ibf8ebe2df7bf06fdd4951ee6a1e48854dd36ad47
2020-02-25 16:46:12 +00:00
github.com/census-instrumentation/opencensus-proto v0.2.1/go.mod h1:f6KPmirojxKA12rnyqOA5BBL4O983OfeGPqjHWSTneU=
github.com/cespare/xxhash v1.1.0/go.mod h1:XrSqR1VqqWfGrhpAt58auRo0WTKS1nRRg3ghfAqPWnc=
github.com/cheggaaa/pb/v3 v3.0.1 h1:m0BngUk2LuSRYdx4fujDKNRXNDpbNCfptPfVT2m6OJY=
github.com/cheggaaa/pb/v3 v3.0.1/go.mod h1:SqqeMF/pMOIu3xgGoxtPYhMNQP258xE4x/XRTYua+KU=
github.com/chzyer/logex v1.1.10/go.mod h1:+Ywpsq7O8HXn0nuIou7OrIPyXbp3wmkHB+jjWRnGsAI=
github.com/chzyer/readline v0.0.0-20180603132655-2972be24d48e/go.mod h1:nSuG5e5PlCu98SY8svDHJxuZscDgtXS6KTTbou5AhLI=
github.com/chzyer/test v0.0.0-20180213035817-a1ea475d72b1/go.mod h1:Q3SI9o4m/ZMnBNeIyt5eFwwo7qiLfzFZmjNmxjkiQlU=
github.com/client9/misspell v0.3.4/go.mod h1:qj6jICC3Q7zFZvVWo7KLAzC3yx5G7kyvSDkc90ppPyw=
2018-10-06 18:57:53 +01:00
github.com/cloudfoundry/gosigar v1.1.0 h1:V/dVCzhKOdIU3WRB5inQU20s4yIgL9Dxx/Mhi0SF8eM=
github.com/cloudfoundry/gosigar v1.1.0/go.mod h1:3qLfc2GlfmwOx2+ZDaRGH3Y9fwQ0sQeaAleo2GV5pH0=
github.com/cockroachdb/apd v1.1.0 h1:3LFP3629v+1aKXU5Q37mxmRxX/pIu1nijXydLShEq5I=
github.com/cockroachdb/apd v1.1.0/go.mod h1:8Sl8LxpKi29FqWXR16WEFZRNSz3SoPzUzeMeY4+DwBQ=
github.com/cockroachdb/cockroach-go v0.0.0-20181001143604-e0a95dfd547c/go.mod h1:XGLbWH/ujMcbPbhZq52Nv6UrCghb1yGn//133kEsvDk=
github.com/containerd/containerd v1.2.7 h1:8lqLbl7u1j3MmiL9cJ/O275crSq7bfwUayvvatEupQk=
github.com/containerd/containerd v1.2.7/go.mod h1:bC6axHOhabU15QhwfG7w5PipXdVtMXFTttgp+kVtyUA=
github.com/coreos/bbolt v1.3.2/go.mod h1:iRUV2dpdMOn7Bo10OQBFzIJO9kkE559Wcmn+qkEiiKk=
github.com/coreos/etcd v3.3.10+incompatible/go.mod h1:uF7uidLiAD3TWHmW31ZFd/JWoc32PjwdhPthX9715RE=
github.com/coreos/go-semver v0.2.0/go.mod h1:nnelYz7RCh+5ahJtPPxZlU+153eP4D4r3EedlOD2RNk=
github.com/coreos/go-systemd v0.0.0-20190321100706-95778dfbb74e/go.mod h1:F5haX7vjVVG0kc13fIWeqUViNPyEJxv/OmvnBo0Yme4=
github.com/coreos/go-systemd v0.0.0-20190719114852-fd7a80b32e1f/go.mod h1:F5haX7vjVVG0kc13fIWeqUViNPyEJxv/OmvnBo0Yme4=
github.com/coreos/pkg v0.0.0-20180928190104-399ea9e2e55f/go.mod h1:E3G3o1h8I7cfcXa63jLwjI0eiQQMgzzUDFVpN/nH/eA=
github.com/cpuguy83/go-md2man/v2 v2.0.0/go.mod h1:maD7wRr/U5Z6m/iR4s+kqSMx2CaBsrgA7czyZG/E6dU=
github.com/creack/pty v1.1.7/go.mod h1:lj5s0c3V2DBrqTV7llrYr5NG6My20zk30Fl46Y7DoTY=
github.com/cznic/b v0.0.0-20180115125044-35e9bbe41f07/go.mod h1:URriBxXwVq5ijiJ12C7iIZqlA69nTlI+LgI6/pwftG8=
github.com/cznic/fileutil v0.0.0-20180108211300-6a051e75936f/go.mod h1:8S58EK26zhXSxzv7NQFpnliaOQsmDUxvoQO3rt154Vg=
github.com/cznic/golex v0.0.0-20170803123110-4ab7c5e190e4/go.mod h1:+bmmJDNmKlhWNG+gwWCkaBoTy39Fs+bzRxVBzoTQbIc=
github.com/cznic/internal v0.0.0-20180608152220-f44710a21d00/go.mod h1:olo7eAdKwJdXxb55TKGLiJ6xt1H0/tiiRCWKVLmtjY4=
github.com/cznic/lldb v1.1.0/go.mod h1:FIZVUmYUVhPwRiPzL8nD/mpFcJ/G7SSXjjXYG4uRI3A=
github.com/cznic/mathutil v0.0.0-20180504122225-ca4c9f2c1369/go.mod h1:e6NPNENfs9mPDVNRekM7lKScauxd5kXTr1Mfyig6TDM=
github.com/cznic/ql v1.2.0/go.mod h1:FbpzhyZrqr0PVlK6ury+PoW3T0ODUV22OeWIxcaOrSE=
github.com/cznic/sortutil v0.0.0-20150617083342-4c7342852e65/go.mod h1:q2w6Bg5jeox1B+QkJ6Wp/+Vn0G/bo3f1uY7Fn3vivIQ=
github.com/cznic/strutil v0.0.0-20171016134553-529a34b1c186/go.mod h1:AHHPPPXTw0h6pVabbcbyGRK1DckRn7r/STdZEeIDzZc=
github.com/cznic/zappy v0.0.0-20160723133515-2533cb5b45cc/go.mod h1:Y1SNZ4dRUOKXshKUbwUapqNncRrho4mkjQebgEHZLj8=
github.com/davecgh/go-spew v0.0.0-20171005155431-ecdeabc65495/go.mod h1:J7Y8YcW2NihsgmVo/mv3lAwl/skON4iLHjSsI+c5H38=
github.com/davecgh/go-spew v1.1.0/go.mod h1:J7Y8YcW2NihsgmVo/mv3lAwl/skON4iLHjSsI+c5H38=
2018-10-06 18:57:53 +01:00
github.com/davecgh/go-spew v1.1.1 h1:vj9j/u1bqnvCEfJOwUhtlOARqs3+rkHYY13jYWTU97c=
github.com/davecgh/go-spew v1.1.1/go.mod h1:J7Y8YcW2NihsgmVo/mv3lAwl/skON4iLHjSsI+c5H38=
github.com/denisenkom/go-mssqldb v0.0.0-20190515213511-eb9f6a1743f3/go.mod h1:zAg7JM8CkOJ43xKXIj7eRO9kmWm/TW578qo+oDO6tuM=
github.com/dgrijalva/jwt-go v3.2.0+incompatible h1:7qlOGliEKZXTDg6OTjfoBKDXWrumCAMpl/TFQ4/5kLM=
2018-10-06 18:57:53 +01:00
github.com/dgrijalva/jwt-go v3.2.0+incompatible/go.mod h1:E3ru+11k8xSBh+hMPgOLZmtrrCbhqsmaPHjLKYnJCaQ=
github.com/dgryski/go-sip13 v0.0.0-20181026042036-e10d5fee7954/go.mod h1:vAd38F8PWV+bWy6jNmig1y/TA+kYO4g3RSRF0IAv0no=
github.com/dhui/dktest v0.3.0 h1:kwX5a7EkLcjo7VpsPQSYJcKGbXBXdjI9FGjuUj1jn6I=
github.com/dhui/dktest v0.3.0/go.mod h1:cyzIUfGsBEbZ6BT7tnXqAShHSXCZhSNmFl70sZ7c1yc=
github.com/docker/distribution v2.7.0+incompatible h1:neUDAlf3wX6Ml4HdqTrbcOHXtfRN0TFIwt6YFL7N9RU=
github.com/docker/distribution v2.7.0+incompatible/go.mod h1:J2gT2udsDAN96Uj4KfcMRqY0/ypR+oyYUYmja8H+y+w=
github.com/docker/docker v0.7.3-0.20190103212154-2b7e084dc98b/go.mod h1:eEKB0N0r5NX/I1kEveEz05bcu8tLC/8azJZsviup8Sk=
github.com/docker/docker v0.7.3-0.20190817195342-4760db040282 h1:mzrx39dGtGq0VEnTHjnakmczd4uFbhx2cZU3BJDsLdc=
github.com/docker/docker v0.7.3-0.20190817195342-4760db040282/go.mod h1:eEKB0N0r5NX/I1kEveEz05bcu8tLC/8azJZsviup8Sk=
github.com/docker/go-connections v0.4.0 h1:El9xVISelRB7BuFusrZozjnkIM5YnzCViNKohAFqRJQ=
github.com/docker/go-connections v0.4.0/go.mod h1:Gbd7IOopHjR8Iph03tsViu4nIes5XhDvyHbTtUxmeec=
github.com/docker/go-units v0.3.3 h1:Xk8S3Xj5sLGlG5g67hJmYMmUgXv5N4PhkjJHHqrwnTk=
github.com/docker/go-units v0.3.3/go.mod h1:fgPhTUdO+D/Jk86RDLlptpiXQzgHJF7gydDDbaIK4Dk=
2018-10-06 18:57:53 +01:00
github.com/eapache/go-resiliency v1.1.0/go.mod h1:kFI+JgMyC7bLPUVY133qvEBtVayf5mFgVsvEsIPBvNs=
github.com/eapache/go-xerial-snappy v0.0.0-20180814174437-776d5712da21 h1:YEetp8/yCZMuEPMUDHG0CW/brkkEp8mzqk2+ODEitlw=
2018-10-06 18:57:53 +01:00
github.com/eapache/go-xerial-snappy v0.0.0-20180814174437-776d5712da21/go.mod h1:+020luEh2TKB4/GOp8oxxtq0Daoen/Cii55CzbTV6DU=
github.com/eapache/queue v1.1.0 h1:YOEu7KNc61ntiQlcEeUIoDTJ2o8mQznoNvUhiigpIqc=
2018-10-06 18:57:53 +01:00
github.com/eapache/queue v1.1.0/go.mod h1:6eCeP0CKFpHLu8blIFXhExK/dRa7WDZfr6jVFPTqq+I=
github.com/edsrzf/mmap-go v0.0.0-20170320065105-0bce6a688712/go.mod h1:YO35OhQPt3KJa3ryjFM5Bs14WD66h8eGKpfaBNrHW5M=
pkg/process: Now that we are trying to identify the root cause of the satellite load limitations (i.e. currently the satellite has a max ability of 400 rps for uploads and we need this to be higher), we are using the golang diagnostic tools to collect insight into what the bottlenecks are. We currently have a debug endpoint to gather some cpu and mem data, but it could be useful to have continuous profiling. GCP stackdriver has support for continuous profiling so lets set that up and see if it is helpful to gather more data. This PR adds support for [GCP continuous profiler](https://cloud.google.com/profiler) which allows enabling continuous cpu/mem profiling and the stats are sent to stackdriver in google cloud console. To enable the continuous profiling for a storj component, do the following: - prereq: the workload must be running in GKE and have Stackdriver Profiling IAM role permissions - provide the config flag `debug.profilename` in the config.yaml file for the workload (i.e. satellite api process, etc). The profilename should be the workload name, for example "satellite-api". - once the above config flag is provided, the profiler will be initialized and profiling stats will automatically be sent to GCP project where the workload is running and viewable in the Stackdriver Profile page in the console The current implementation assumes the workload is running in GKE, however if we find if useful we can add support to enable this from anywhere. But for simplicity, its configured this way assuming the main goal is to enable in production systems. Change-Id: Ibf8ebe2df7bf06fdd4951ee6a1e48854dd36ad47
2020-02-25 16:46:12 +00:00
github.com/envoyproxy/go-control-plane v0.9.1-0.20191026205805-5f8ba28d4473/go.mod h1:YTl/9mNaCwkRvm6d1a2C3ymFceY/DCBVvsKhRF0iEA4=
github.com/envoyproxy/protoc-gen-validate v0.1.0/go.mod h1:iSmxcyjqTsJpI2R4NaDN7+kN2VEUnK/pcBlmesArF7c=
2018-10-06 18:57:53 +01:00
github.com/fatih/color v1.7.0 h1:DkWD4oS2D8LGGgTQ6IvwJJXSL5Vp2ffcQg58nFV38Ys=
github.com/fatih/color v1.7.0/go.mod h1:Zm6kSWBoL9eyXnKyktHP6abPY2pDugNf5KwzbycvMj4=
github.com/fsnotify/fsnotify v1.4.7 h1:IXs+QLmnXW2CcXuY+8Mzv/fWEsPGWxqefPtCP5CnV9I=
github.com/fsnotify/fsnotify v1.4.7/go.mod h1:jwhsz4b93w/PPRr/qN1Yymfu8t87LnFCMoQvtojpjFo=
github.com/fsouza/fake-gcs-server v1.7.0/go.mod h1:5XIRs4YvwNbNoz+1JF8j6KLAyDh7RHGAyAK3EP2EsNk=
github.com/ghodss/yaml v1.0.0/go.mod h1:4dBDuWmgqj2HViK6kFavaiC9ZROes6MMH2rRYeMEF04=
pkg/process: Now that we are trying to identify the root cause of the satellite load limitations (i.e. currently the satellite has a max ability of 400 rps for uploads and we need this to be higher), we are using the golang diagnostic tools to collect insight into what the bottlenecks are. We currently have a debug endpoint to gather some cpu and mem data, but it could be useful to have continuous profiling. GCP stackdriver has support for continuous profiling so lets set that up and see if it is helpful to gather more data. This PR adds support for [GCP continuous profiler](https://cloud.google.com/profiler) which allows enabling continuous cpu/mem profiling and the stats are sent to stackdriver in google cloud console. To enable the continuous profiling for a storj component, do the following: - prereq: the workload must be running in GKE and have Stackdriver Profiling IAM role permissions - provide the config flag `debug.profilename` in the config.yaml file for the workload (i.e. satellite api process, etc). The profilename should be the workload name, for example "satellite-api". - once the above config flag is provided, the profiler will be initialized and profiling stats will automatically be sent to GCP project where the workload is running and viewable in the Stackdriver Profile page in the console The current implementation assumes the workload is running in GKE, however if we find if useful we can add support to enable this from anywhere. But for simplicity, its configured this way assuming the main goal is to enable in production systems. Change-Id: Ibf8ebe2df7bf06fdd4951ee6a1e48854dd36ad47
2020-02-25 16:46:12 +00:00
github.com/go-gl/glfw/v3.3/glfw v0.0.0-20191125211704-12ad95a8df72/go.mod h1:tQ2UAYgL5IevRw8kRxooKSPJfGvJ9fJQFa0TUsXzTg8=
github.com/go-kit/kit v0.8.0/go.mod h1:xBxKIO96dXMWWy0MnWVtmwkA9/13aqxPnvrjFYMA2as=
github.com/go-logfmt/logfmt v0.3.0/go.mod h1:Qt1PoO58o5twSAckw1HlFXLmHsOX5/0LbT9GBnD5lWE=
github.com/go-logfmt/logfmt v0.4.0/go.mod h1:3RMwSq7FuexP4Kalkev3ejPJsZTpXXBr9+V4qmtdjCk=
2018-10-06 18:57:53 +01:00
github.com/go-redis/redis v6.14.1+incompatible h1:kSJohAREGMr344uMa8PzuIg5OU6ylCbyDkWkkNOfEik=
github.com/go-redis/redis v6.14.1+incompatible/go.mod h1:NAIEuMOZ/fxfXJIrKDQDz8wamY7mA7PouImQ2Jvg6kA=
github.com/go-sql-driver/mysql v1.4.1 h1:g24URVg0OFbNUTx9qqY1IRZ9D9z3iPyi5zKhQZpNwpA=
github.com/go-sql-driver/mysql v1.4.1/go.mod h1:zAC/RDZ24gD3HViQzih4MyKcchzm+sOG5ZlKdlhCg5w=
github.com/go-stack/stack v1.8.0/go.mod h1:v0f6uXyyMGvRgIKkXu+yp6POWl0qKG85gN/melR3HDY=
github.com/gocql/gocql v0.0.0-20190301043612-f6df8288f9b4/go.mod h1:4Fw1eo5iaEhDUs8XyuhSVCVy52Jq3L+/3GJgYkwc+/0=
github.com/gofrs/uuid v3.2.0+incompatible h1:y12jRkkFxsd7GpqdSZ+/KCs/fJbqpEXSGd4+jfEaewE=
github.com/gofrs/uuid v3.2.0+incompatible/go.mod h1:b2aQJv3Z4Fp6yNu3cdSllBxTCLRxnplIgP/c0N/04lM=
github.com/gogo/protobuf v1.1.1/go.mod h1:r8qH/GZQm5c6nD/R0oafs1akxWv10x8SbQlK7atdtwQ=
github.com/gogo/protobuf v1.2.0/go.mod h1:r8qH/GZQm5c6nD/R0oafs1akxWv10x8SbQlK7atdtwQ=
github.com/gogo/protobuf v1.2.1 h1:/s5zKNz0uPFCZ5hddgPdo2TK2TVrUNMn0OOX8/aZMTE=
github.com/gogo/protobuf v1.2.1/go.mod h1:hp+jE20tsWTFYpLwKvXlhS1hjn+gTNwPg2I6zVXpSg4=
github.com/gogo/protobuf v1.3.1 h1:DqDEcV5aeaTmdFBePNpYsp3FlcVH/2ISVVM9Qf8PSls=
github.com/gogo/protobuf v1.3.1/go.mod h1:SlYgWuQ5SjCEi6WLHjHCa1yvBfUnHcTbrrZtXPKa29o=
github.com/golang-migrate/migrate/v4 v4.7.0 h1:gONcHxHApDTKXDyLH/H97gEHmpu1zcnnbAaq2zgrPrs=
github.com/golang-migrate/migrate/v4 v4.7.0/go.mod h1:Qvut3N4xKWjoH3sokBccML6WyHSnggXm/DvMMnTsQIc=
github.com/golang/glog v0.0.0-20160126235308-23def4e6c14b h1:VKtxabqXZkF25pY9ekfRL6a582T4P37/31XEstQ5p58=
2018-10-06 18:57:53 +01:00
github.com/golang/glog v0.0.0-20160126235308-23def4e6c14b/go.mod h1:SBH7ygxi8pfUlaOkMMuAQtPIUF8ecWP5IEl/CR7VP2Q=
github.com/golang/groupcache v0.0.0-20190129154638-5b532d6fd5ef/go.mod h1:cIg4eruTrX1D+g88fzRXU5OdNfaM+9IcxsU14FzY7Hc=
pkg/process: Now that we are trying to identify the root cause of the satellite load limitations (i.e. currently the satellite has a max ability of 400 rps for uploads and we need this to be higher), we are using the golang diagnostic tools to collect insight into what the bottlenecks are. We currently have a debug endpoint to gather some cpu and mem data, but it could be useful to have continuous profiling. GCP stackdriver has support for continuous profiling so lets set that up and see if it is helpful to gather more data. This PR adds support for [GCP continuous profiler](https://cloud.google.com/profiler) which allows enabling continuous cpu/mem profiling and the stats are sent to stackdriver in google cloud console. To enable the continuous profiling for a storj component, do the following: - prereq: the workload must be running in GKE and have Stackdriver Profiling IAM role permissions - provide the config flag `debug.profilename` in the config.yaml file for the workload (i.e. satellite api process, etc). The profilename should be the workload name, for example "satellite-api". - once the above config flag is provided, the profiler will be initialized and profiling stats will automatically be sent to GCP project where the workload is running and viewable in the Stackdriver Profile page in the console The current implementation assumes the workload is running in GKE, however if we find if useful we can add support to enable this from anywhere. But for simplicity, its configured this way assuming the main goal is to enable in production systems. Change-Id: Ibf8ebe2df7bf06fdd4951ee6a1e48854dd36ad47
2020-02-25 16:46:12 +00:00
github.com/golang/groupcache v0.0.0-20190702054246-869f871628b6/go.mod h1:cIg4eruTrX1D+g88fzRXU5OdNfaM+9IcxsU14FzY7Hc=
github.com/golang/groupcache v0.0.0-20191227052852-215e87163ea7 h1:5ZkaAPbicIKTF2I64qf5Fh8Aa83Q/dnOafMYV0OMwjA=
github.com/golang/groupcache v0.0.0-20191227052852-215e87163ea7/go.mod h1:cIg4eruTrX1D+g88fzRXU5OdNfaM+9IcxsU14FzY7Hc=
2018-10-06 18:57:53 +01:00
github.com/golang/mock v1.1.1/go.mod h1:oTYuIxOrZwtPieC+H1uAHpcLFnEyAGVDL/k47Jfbm0A=
github.com/golang/mock v1.2.0/go.mod h1:oTYuIxOrZwtPieC+H1uAHpcLFnEyAGVDL/k47Jfbm0A=
github.com/golang/mock v1.3.1 h1:qGJ6qTW+x6xX/my+8YUVl4WNpX9B7+/l2tRsHGZ7f2s=
pkg/process: Now that we are trying to identify the root cause of the satellite load limitations (i.e. currently the satellite has a max ability of 400 rps for uploads and we need this to be higher), we are using the golang diagnostic tools to collect insight into what the bottlenecks are. We currently have a debug endpoint to gather some cpu and mem data, but it could be useful to have continuous profiling. GCP stackdriver has support for continuous profiling so lets set that up and see if it is helpful to gather more data. This PR adds support for [GCP continuous profiler](https://cloud.google.com/profiler) which allows enabling continuous cpu/mem profiling and the stats are sent to stackdriver in google cloud console. To enable the continuous profiling for a storj component, do the following: - prereq: the workload must be running in GKE and have Stackdriver Profiling IAM role permissions - provide the config flag `debug.profilename` in the config.yaml file for the workload (i.e. satellite api process, etc). The profilename should be the workload name, for example "satellite-api". - once the above config flag is provided, the profiler will be initialized and profiling stats will automatically be sent to GCP project where the workload is running and viewable in the Stackdriver Profile page in the console The current implementation assumes the workload is running in GKE, however if we find if useful we can add support to enable this from anywhere. But for simplicity, its configured this way assuming the main goal is to enable in production systems. Change-Id: Ibf8ebe2df7bf06fdd4951ee6a1e48854dd36ad47
2020-02-25 16:46:12 +00:00
github.com/golang/mock v1.3.1/go.mod h1:sBzyDLLjw3U8JLTeZvSv8jJB+tU5PVekmnlKIyFUx0Y=
2018-10-06 18:57:53 +01:00
github.com/golang/protobuf v1.2.0/go.mod h1:6lQm79b+lXiMfvg/cZm0SGofjICqVBUtrP5yJMmIC1U=
github.com/golang/protobuf v1.3.1/go.mod h1:6lQm79b+lXiMfvg/cZm0SGofjICqVBUtrP5yJMmIC1U=
github.com/golang/protobuf v1.3.2 h1:6nsPYzhq5kReh6QImI3k5qWzO4PEbvbIW2cwSfR/6xs=
github.com/golang/protobuf v1.3.2/go.mod h1:6lQm79b+lXiMfvg/cZm0SGofjICqVBUtrP5yJMmIC1U=
github.com/golang/snappy v0.0.0-20170215233205-553a64147049/go.mod h1:/XxbfmMg8lxefKM7IXC3fBNl/7bRcc72aCRzEWrmP2Q=
2018-10-06 18:57:53 +01:00
github.com/golang/snappy v0.0.0-20180518054509-2e65f85255db/go.mod h1:/XxbfmMg8lxefKM7IXC3fBNl/7bRcc72aCRzEWrmP2Q=
github.com/golang/snappy v0.0.1 h1:Qgr9rKW7uDUkrbSmQeiDsGa8SjGyCOGtuasMWwvp2P4=
github.com/golang/snappy v0.0.1/go.mod h1:/XxbfmMg8lxefKM7IXC3fBNl/7bRcc72aCRzEWrmP2Q=
github.com/gomodule/redigo v1.7.1-0.20190322064113-39e2c31b7ca3 h1:6amM4HsNPOvMLVc2ZnyqrjeQ92YAVWn7T4WBKK87inY=
github.com/gomodule/redigo v1.7.1-0.20190322064113-39e2c31b7ca3/go.mod h1:B4C85qUVwatsJoIUNIfCRsp7qO0iAmpGFZ4EELWSbC4=
github.com/gomodule/redigo v2.0.0+incompatible h1:K/R+8tc58AaqLkqG2Ol3Qk+DR/TlNuhuh457pBFPtt0=
github.com/gomodule/redigo v2.0.0+incompatible/go.mod h1:B4C85qUVwatsJoIUNIfCRsp7qO0iAmpGFZ4EELWSbC4=
github.com/google/btree v0.0.0-20180813153112-4030bb1f1f0c/go.mod h1:lNA+9X1NB3Zf8V7Ke586lFgjr2dZNuvo3lPJSGZ5JPQ=
github.com/google/btree v1.0.0/go.mod h1:lNA+9X1NB3Zf8V7Ke586lFgjr2dZNuvo3lPJSGZ5JPQ=
2018-10-06 18:57:53 +01:00
github.com/google/go-cmp v0.2.0/go.mod h1:oXzfMopK8JAjlY9xF4vHSVASa0yLyX7SntLO5aqRK0M=
github.com/google/go-cmp v0.3.0/go.mod h1:8QqcDgzrUqlUb/G2PQTWiueGozuR1884gddMywk6iLU=
pkg/process: Now that we are trying to identify the root cause of the satellite load limitations (i.e. currently the satellite has a max ability of 400 rps for uploads and we need this to be higher), we are using the golang diagnostic tools to collect insight into what the bottlenecks are. We currently have a debug endpoint to gather some cpu and mem data, but it could be useful to have continuous profiling. GCP stackdriver has support for continuous profiling so lets set that up and see if it is helpful to gather more data. This PR adds support for [GCP continuous profiler](https://cloud.google.com/profiler) which allows enabling continuous cpu/mem profiling and the stats are sent to stackdriver in google cloud console. To enable the continuous profiling for a storj component, do the following: - prereq: the workload must be running in GKE and have Stackdriver Profiling IAM role permissions - provide the config flag `debug.profilename` in the config.yaml file for the workload (i.e. satellite api process, etc). The profilename should be the workload name, for example "satellite-api". - once the above config flag is provided, the profiler will be initialized and profiling stats will automatically be sent to GCP project where the workload is running and viewable in the Stackdriver Profile page in the console The current implementation assumes the workload is running in GKE, however if we find if useful we can add support to enable this from anywhere. But for simplicity, its configured this way assuming the main goal is to enable in production systems. Change-Id: Ibf8ebe2df7bf06fdd4951ee6a1e48854dd36ad47
2020-02-25 16:46:12 +00:00
github.com/google/go-cmp v0.4.0 h1:xsAVV57WRhGj6kEIi8ReJzQlHHqcBYCElAvkovg3B/4=
github.com/google/go-cmp v0.4.0/go.mod h1:v8dTdLbMG2kIc/vJvl+f65V22dbkXbowE6jgT/gNBxE=
github.com/google/go-github v17.0.0+incompatible/go.mod h1:zLgOLi98H3fifZn+44m+umXrS52loVEgC2AApnigrVQ=
github.com/google/go-querystring v1.0.0/go.mod h1:odCYkC5MyYFN7vkCjXpyrEuKhc/BUO6wN/zVPAxq5ck=
github.com/google/martian v2.1.0+incompatible h1:/CP5g8u/VJHijgedC/Legn3BAbAaWPgecwXBIDzw5no=
github.com/google/martian v2.1.0+incompatible/go.mod h1:9I4somxYTbIHy5NJKHRl3wXiIaQGbYVAs8BPL6v8lEs=
github.com/google/pprof v0.0.0-20181206194817-3ea8567a2e57/go.mod h1:zfwlbNMJ+OItoe0UupaVj+oy1omPYYDuagoSzA8v9mc=
pkg/process: Now that we are trying to identify the root cause of the satellite load limitations (i.e. currently the satellite has a max ability of 400 rps for uploads and we need this to be higher), we are using the golang diagnostic tools to collect insight into what the bottlenecks are. We currently have a debug endpoint to gather some cpu and mem data, but it could be useful to have continuous profiling. GCP stackdriver has support for continuous profiling so lets set that up and see if it is helpful to gather more data. This PR adds support for [GCP continuous profiler](https://cloud.google.com/profiler) which allows enabling continuous cpu/mem profiling and the stats are sent to stackdriver in google cloud console. To enable the continuous profiling for a storj component, do the following: - prereq: the workload must be running in GKE and have Stackdriver Profiling IAM role permissions - provide the config flag `debug.profilename` in the config.yaml file for the workload (i.e. satellite api process, etc). The profilename should be the workload name, for example "satellite-api". - once the above config flag is provided, the profiler will be initialized and profiling stats will automatically be sent to GCP project where the workload is running and viewable in the Stackdriver Profile page in the console The current implementation assumes the workload is running in GKE, however if we find if useful we can add support to enable this from anywhere. But for simplicity, its configured this way assuming the main goal is to enable in production systems. Change-Id: Ibf8ebe2df7bf06fdd4951ee6a1e48854dd36ad47
2020-02-25 16:46:12 +00:00
github.com/google/pprof v0.0.0-20190515194954-54271f7e092f/go.mod h1:zfwlbNMJ+OItoe0UupaVj+oy1omPYYDuagoSzA8v9mc=
github.com/google/pprof v0.0.0-20191218002539-d4f498aebedc h1:DLpL8pWq0v4JYoRpEhDfsJhhJyGKCcQM2WPW2TJs31c=
github.com/google/pprof v0.0.0-20191218002539-d4f498aebedc/go.mod h1:ZgVRPoUq/hfqzAqh7sHMqb3I9Rq5C59dIz2SbBwJ4eM=
github.com/google/pprof v0.0.0-20200229191704-1ebb73c60ed3 h1:SRgJV+IoxM5MKyFdlSUeNy6/ycRUF2yBAKdAQswoHUk=
github.com/google/pprof v0.0.0-20200229191704-1ebb73c60ed3/go.mod h1:ZgVRPoUq/hfqzAqh7sHMqb3I9Rq5C59dIz2SbBwJ4eM=
pkg/process: Now that we are trying to identify the root cause of the satellite load limitations (i.e. currently the satellite has a max ability of 400 rps for uploads and we need this to be higher), we are using the golang diagnostic tools to collect insight into what the bottlenecks are. We currently have a debug endpoint to gather some cpu and mem data, but it could be useful to have continuous profiling. GCP stackdriver has support for continuous profiling so lets set that up and see if it is helpful to gather more data. This PR adds support for [GCP continuous profiler](https://cloud.google.com/profiler) which allows enabling continuous cpu/mem profiling and the stats are sent to stackdriver in google cloud console. To enable the continuous profiling for a storj component, do the following: - prereq: the workload must be running in GKE and have Stackdriver Profiling IAM role permissions - provide the config flag `debug.profilename` in the config.yaml file for the workload (i.e. satellite api process, etc). The profilename should be the workload name, for example "satellite-api". - once the above config flag is provided, the profiler will be initialized and profiling stats will automatically be sent to GCP project where the workload is running and viewable in the Stackdriver Profile page in the console The current implementation assumes the workload is running in GKE, however if we find if useful we can add support to enable this from anywhere. But for simplicity, its configured this way assuming the main goal is to enable in production systems. Change-Id: Ibf8ebe2df7bf06fdd4951ee6a1e48854dd36ad47
2020-02-25 16:46:12 +00:00
github.com/google/renameio v0.1.0/go.mod h1:KWCgfxg9yswjAJkECMjeO8J8rahYeXnNhOm40UhjYkI=
github.com/googleapis/gax-go/v2 v2.0.4/go.mod h1:0Wqv26UfaUD9n4G6kQubkQ+KchISgw+vpHVxEJEs9eg=
pkg/process: Now that we are trying to identify the root cause of the satellite load limitations (i.e. currently the satellite has a max ability of 400 rps for uploads and we need this to be higher), we are using the golang diagnostic tools to collect insight into what the bottlenecks are. We currently have a debug endpoint to gather some cpu and mem data, but it could be useful to have continuous profiling. GCP stackdriver has support for continuous profiling so lets set that up and see if it is helpful to gather more data. This PR adds support for [GCP continuous profiler](https://cloud.google.com/profiler) which allows enabling continuous cpu/mem profiling and the stats are sent to stackdriver in google cloud console. To enable the continuous profiling for a storj component, do the following: - prereq: the workload must be running in GKE and have Stackdriver Profiling IAM role permissions - provide the config flag `debug.profilename` in the config.yaml file for the workload (i.e. satellite api process, etc). The profilename should be the workload name, for example "satellite-api". - once the above config flag is provided, the profiler will be initialized and profiling stats will automatically be sent to GCP project where the workload is running and viewable in the Stackdriver Profile page in the console The current implementation assumes the workload is running in GKE, however if we find if useful we can add support to enable this from anywhere. But for simplicity, its configured this way assuming the main goal is to enable in production systems. Change-Id: Ibf8ebe2df7bf06fdd4951ee6a1e48854dd36ad47
2020-02-25 16:46:12 +00:00
github.com/googleapis/gax-go/v2 v2.0.5 h1:sjZBwGj9Jlw33ImPtvFviGYvseOtDM7hkSKB7+Tv3SM=
github.com/googleapis/gax-go/v2 v2.0.5/go.mod h1:DWXyrwAJ9X0FpwwEdw+IPEYBICEFu5mhpdKc/us6bOk=
2018-10-06 18:57:53 +01:00
github.com/gorilla/context v1.1.1/go.mod h1:kBGZzfjB9CEq2AlWe17Uuf7NDRt0dE0s8S51q0aT7Yg=
github.com/gorilla/mux v1.6.2/go.mod h1:1lud6UwP+6orDFRuTfBEV8e9/aOM/c4fVVCaMa2zaAs=
github.com/gorilla/mux v1.7.1 h1:Dw4jY2nghMMRsh1ol8dv1axHkDwMQK2DHerMNJsIpJU=
github.com/gorilla/mux v1.7.1/go.mod h1:1lud6UwP+6orDFRuTfBEV8e9/aOM/c4fVVCaMa2zaAs=
satellite/marketing: Create New Offer (#2186) * update UI to reflect final mockups * implement create handler and render offers table data to UI * fix line-height unit and remove important from selectors * update file names and ids for clarity * shorten 'label' in ids * localize global vars, fix endpoint names, remove unnecessary receiver, fix comments * fix unnecessary initialization of pointer * correct file-naming conventions * register timeConverter in an init func for safety and remove unnecessary important from css * consolidate create endpoints and add comments * register timeConverter in init func * add copyright to files * introduce require pkg * add proper http server unit test * update linting and create offers concurrently in unit test * fix getOffers comment * add copy-right to unit-test * fix data-races * fix linting * remove converter in NewServer * requested changes in progress * add require for checking status code * renamed template file * fix 400 handler * fix missing copyright and remove extra line * fix build * run goroutine for testing parallel * evaluate reqType with switch stmt and promp for credit amount in cents * fix lint issue * add default case * remove unnecessary var * fix range scope error * remove empty lines and use long form for struct field * fix merge conflicts * fix template reference * fix modal id * not delete package * add currency formatting and requested changes * markup formatting * lean out currency logic and move wait outside loop * pass ToDollars func to home template * fix lint
2019-06-28 15:34:10 +01:00
github.com/gorilla/schema v1.1.0 h1:CamqUDOFUBqzrvxuz2vEwo8+SUdwsluFh7IlzJh30LY=
github.com/gorilla/schema v1.1.0/go.mod h1:kgLaKoK1FELgZqMAVxx/5cbj0kT+57qxUrAlIO2eleU=
github.com/gorilla/websocket v1.4.0/go.mod h1:E7qHFY5m1UJ88s3WnNqhKjPHQ0heANvMoAMk2YaljkQ=
github.com/graphql-go/graphql v0.7.9 h1:5Va/Rt4l5g3YjwDnid3vFfn43faaQBq7rMcIZ0VnV34=
github.com/graphql-go/graphql v0.7.9/go.mod h1:k6yrAYQaSP59DC5UVxbgxESlmVyojThKdORUqGDGmrI=
github.com/grpc-ecosystem/go-grpc-middleware v1.0.0/go.mod h1:FiyG127CGDf3tlThmgyCl78X/SZQqEOJBCDaAfeWzPs=
github.com/grpc-ecosystem/go-grpc-prometheus v1.2.0/go.mod h1:8NvIoxWQoOIhqOTXgfV/d3M/q6VIi02HzZEHgUlZvzk=
github.com/grpc-ecosystem/grpc-gateway v1.9.0/go.mod h1:vNeuVxBJEsws4ogUvrchl83t/GYV9WGTSLVdBhOQFDY=
github.com/hailocab/go-hostpool v0.0.0-20160125115350-e80d13ce29ed/go.mod h1:tMWxXQ9wFIaZeTI9F+hmhFiGpFmhOHzyShyFUhRm0H4=
github.com/hashicorp/errwrap v1.0.0 h1:hLrqtEDnRye3+sgx6z4qVLNuviH3MR5aQ0ykNJa/UYA=
github.com/hashicorp/errwrap v1.0.0/go.mod h1:YH+1FKiLXxHSkmPseP+kNlulaMuP3n2brvKWEqk/Jc4=
github.com/hashicorp/go-multierror v1.0.0 h1:iVjPR7a6H0tWELX5NxNe7bYopibicUzc7uPribsnS6o=
github.com/hashicorp/go-multierror v1.0.0/go.mod h1:dHtQlpGsu+cZNNAkkCN/P3hoUDHhCYQXV3UM06sGGrk=
2018-10-06 18:57:53 +01:00
github.com/hashicorp/golang-lru v0.5.0/go.mod h1:/m3WP610KZHVQ1SGc6re/UDhFvYD7pJ4Ao+sR/qLZy8=
github.com/hashicorp/golang-lru v0.5.1/go.mod h1:/m3WP610KZHVQ1SGc6re/UDhFvYD7pJ4Ao+sR/qLZy8=
2018-10-06 18:57:53 +01:00
github.com/hashicorp/hcl v1.0.0 h1:0Anlzjpi4vEasTeNFn2mLJgTSwt0+6sfsiTG8qcWGx4=
github.com/hashicorp/hcl v1.0.0/go.mod h1:E5yfLk+7swimpb2L/Alb/PJmXilQ/rhwaUYs4T20WEQ=
github.com/hpcloud/tail v1.0.0 h1:nfCOvKYfkgYP8hkirhJocXT2+zOD8yUNjXaWfTlyFKI=
2018-10-06 18:57:53 +01:00
github.com/hpcloud/tail v1.0.0/go.mod h1:ab1qPbhIpdTxEkNHXyeSf5vhxWSCs/tWer42PpOxQnU=
pkg/process: Now that we are trying to identify the root cause of the satellite load limitations (i.e. currently the satellite has a max ability of 400 rps for uploads and we need this to be higher), we are using the golang diagnostic tools to collect insight into what the bottlenecks are. We currently have a debug endpoint to gather some cpu and mem data, but it could be useful to have continuous profiling. GCP stackdriver has support for continuous profiling so lets set that up and see if it is helpful to gather more data. This PR adds support for [GCP continuous profiler](https://cloud.google.com/profiler) which allows enabling continuous cpu/mem profiling and the stats are sent to stackdriver in google cloud console. To enable the continuous profiling for a storj component, do the following: - prereq: the workload must be running in GKE and have Stackdriver Profiling IAM role permissions - provide the config flag `debug.profilename` in the config.yaml file for the workload (i.e. satellite api process, etc). The profilename should be the workload name, for example "satellite-api". - once the above config flag is provided, the profiler will be initialized and profiling stats will automatically be sent to GCP project where the workload is running and viewable in the Stackdriver Profile page in the console The current implementation assumes the workload is running in GKE, however if we find if useful we can add support to enable this from anywhere. But for simplicity, its configured this way assuming the main goal is to enable in production systems. Change-Id: Ibf8ebe2df7bf06fdd4951ee6a1e48854dd36ad47
2020-02-25 16:46:12 +00:00
github.com/ianlancetaylor/demangle v0.0.0-20181102032728-5e5cf60278f6/go.mod h1:aSSvb/t6k1mPoxDqO4vJh6VOCGPwU4O0C2/Eqndh1Sc=
github.com/inconshreveable/mousetrap v1.0.0 h1:Z8tu5sraLXCXIcARxBp/8cbvlwVa7Z1NHg9XEKhtSvM=
2018-10-06 18:57:53 +01:00
github.com/inconshreveable/mousetrap v1.0.0/go.mod h1:PxqpIevigyE2G7u3NXJIT2ANytuPF1OarO4DADm73n8=
github.com/jackc/chunkreader v1.0.0 h1:4s39bBR8ByfqH+DKm8rQA3E1LHZWB9XWcrz8fqaZbe0=
github.com/jackc/chunkreader v1.0.0/go.mod h1:RT6O25fNZIuasFJRyZ4R/Y2BbhasbmZXF9QQ7T3kePo=
github.com/jackc/chunkreader/v2 v2.0.0/go.mod h1:odVSm741yZoC3dpHEUXIqA9tQRhFrgOHwnPIn9lDKlk=
github.com/jackc/chunkreader/v2 v2.0.1 h1:i+RDz65UE+mmpjTfyz0MoVTnzeYxroil2G82ki7MGG8=
github.com/jackc/chunkreader/v2 v2.0.1/go.mod h1:odVSm741yZoC3dpHEUXIqA9tQRhFrgOHwnPIn9lDKlk=
github.com/jackc/fake v0.0.0-20150926172116-812a484cc733 h1:vr3AYkKovP8uR8AvSGGUK1IDqRa5lAAvEkZG1LKaCRc=
github.com/jackc/fake v0.0.0-20150926172116-812a484cc733/go.mod h1:WrMFNQdiFJ80sQsxDoMokWK1W5TQtxBFNpzWTD84ibQ=
github.com/jackc/pgconn v0.0.0-20190420214824-7e0022ef6ba3/go.mod h1:jkELnwuX+w9qN5YIfX0fl88Ehu4XC3keFuOJJk9pcnA=
github.com/jackc/pgconn v0.0.0-20190824142844-760dd75542eb/go.mod h1:lLjNuW/+OfW9/pnVKPazfWOgNfH2aPem8YQ7ilXGvJE=
github.com/jackc/pgconn v0.0.0-20190831204454-2fabfa3c18b7/go.mod h1:ZJKsE/KZfsUgOEh9hBm+xYTstcNHg7UPMVJqRfQxq4s=
github.com/jackc/pgconn v1.4.0/go.mod h1:Y2O3ZDF0q4mMacyWV3AstPJpeHXWGEetiFttmq5lahk=
github.com/jackc/pgconn v1.5.0/go.mod h1:QeD3lBfpTFe8WUnPZWN5KY/mB8FGMIYRdd8P8Jr0fAI=
github.com/jackc/pgconn v1.5.1-0.20200601181101-fa742c524853/go.mod h1:QeD3lBfpTFe8WUnPZWN5KY/mB8FGMIYRdd8P8Jr0fAI=
github.com/jackc/pgconn v1.6.1 h1:lwofaXKPbIx6qEaK8mNm7uZuOwxHw+PnAFGDsDFpkRI=
github.com/jackc/pgconn v1.6.1/go.mod h1:g8mKMqmSUO6AzAvha7vy07g1rbGOlc7iF0nU0ei83hc=
github.com/jackc/pgio v1.0.0 h1:g12B9UwVnzGhueNavwioyEEpAmqMe1E/BN9ES+8ovkE=
github.com/jackc/pgio v1.0.0/go.mod h1:oP+2QK2wFfUWgr+gxjoBH9KGBb31Eio69xUb0w5bYf8=
github.com/jackc/pgmock v0.0.0-20190831213851-13a1b77aafa2 h1:JVX6jT/XfzNqIjye4717ITLaNwV9mWbJx0dLCpcRzdA=
github.com/jackc/pgmock v0.0.0-20190831213851-13a1b77aafa2/go.mod h1:fGZlG77KXmcq05nJLRkk0+p82V8B8Dw8KN2/V9c/OAE=
github.com/jackc/pgpassfile v1.0.0 h1:/6Hmqy13Ss2zCq62VdNG8tM1wchn8zjSGOBJ6icpsIM=
github.com/jackc/pgpassfile v1.0.0/go.mod h1:CEx0iS5ambNFdcRtxPj5JhEz+xB6uRky5eyVu/W2HEg=
github.com/jackc/pgproto3 v1.1.0 h1:FYYE4yRw+AgI8wXIinMlNjBbp/UitDJwfj5LqqewP1A=
github.com/jackc/pgproto3 v1.1.0/go.mod h1:eR5FA3leWg7p9aeAqi37XOTgTIbkABlvcPB3E5rlc78=
github.com/jackc/pgproto3/v2 v2.0.0-alpha1.0.20190420180111-c116219b62db/go.mod h1:bhq50y+xrl9n5mRYyCBFKkpRVTLYJVWeCc+mEAI3yXA=
github.com/jackc/pgproto3/v2 v2.0.0-alpha1.0.20190609003834-432c2951c711/go.mod h1:uH0AWtUmuShn0bcesswc4aBTWGvw0cAxIJp+6OB//Wg=
github.com/jackc/pgproto3/v2 v2.0.0-rc3/go.mod h1:ryONWYqW6dqSg1Lw6vXNMXoBJhpzvWKnT95C46ckYeM=
github.com/jackc/pgproto3/v2 v2.0.0-rc3.0.20190831210041-4c03ce451f29/go.mod h1:ryONWYqW6dqSg1Lw6vXNMXoBJhpzvWKnT95C46ckYeM=
github.com/jackc/pgproto3/v2 v2.0.1/go.mod h1:WfJCnwN3HIg9Ish/j3sgWXnAfK8A9Y0bwXYU5xKaEdA=
github.com/jackc/pgproto3/v2 v2.0.2 h1:q1Hsy66zh4vuNsajBUF2PNqfAMMfxU5mk594lPE9vjY=
github.com/jackc/pgproto3/v2 v2.0.2/go.mod h1:WfJCnwN3HIg9Ish/j3sgWXnAfK8A9Y0bwXYU5xKaEdA=
github.com/jackc/pgservicefile v0.0.0-20200307190119-3430c5407db8 h1:Q3tB+ExeflWUW7AFcAhXqk40s9mnNYLk1nOkKNZ5GnU=
github.com/jackc/pgservicefile v0.0.0-20200307190119-3430c5407db8/go.mod h1:vsD4gTJCa9TptPL8sPkXrLZ+hDuNrZCnj29CQpr4X1E=
github.com/jackc/pgtype v0.0.0-20190421001408-4ed0de4755e0/go.mod h1:hdSHsc1V01CGwFsrv11mJRHWJ6aifDLfdV3aVjFF0zg=
github.com/jackc/pgtype v0.0.0-20190824184912-ab885b375b90/go.mod h1:KcahbBH1nCMSo2DXpzsoWOAfFkdEtEJpPbVLq8eE+mc=
github.com/jackc/pgtype v0.0.0-20190828014616-a8802b16cc59/go.mod h1:MWlu30kVJrUS8lot6TQqcg7mtthZ9T0EoIBFiJcmcyw=
github.com/jackc/pgtype v1.2.0/go.mod h1:5m2OfMh1wTK7x+Fk952IDmI4nw3nPrvtQdM0ZT4WpC0=
github.com/jackc/pgtype v1.3.1-0.20200510190516-8cd94a14c75a/go.mod h1:vaogEUkALtxZMCH411K+tKzNpwzCKU+AnPzBKZ+I+Po=
github.com/jackc/pgtype v1.3.1-0.20200606141011-f6355165a91c/go.mod h1:cvk9Bgu/VzJ9/lxTO5R5sf80p0DiucVtN7ZxvaC4GmQ=
github.com/jackc/pgtype v1.4.0 h1:pHQfb4jh9iKqHyxPthq1fr+0HwSNIl3btYPbw2m2lbM=
github.com/jackc/pgtype v1.4.0/go.mod h1:JCULISAZBFGrHaOXIIFiyfzW5VY0GRitRr8NeJsrdig=
github.com/jackc/pgx v3.2.0+incompatible h1:0Vihzu20St42/UDsvZGdNE6jak7oi/UOeMzwMPHkgFY=
github.com/jackc/pgx v3.2.0+incompatible/go.mod h1:0ZGrqGqkRlliWnWB4zKnWtjbSWbGkVEFm4TeybAXq+I=
github.com/jackc/pgx/v4 v4.0.0-20190420224344-cc3461e65d96/go.mod h1:mdxmSJJuR08CZQyj1PVQBHy9XOp5p8/SHH6a0psbY9Y=
github.com/jackc/pgx/v4 v4.0.0-20190421002000-1b8f0016e912/go.mod h1:no/Y67Jkk/9WuGR0JG/JseM9irFbnEPbuWV2EELPNuM=
github.com/jackc/pgx/v4 v4.0.0-pre1.0.20190824185557-6972a5742186/go.mod h1:X+GQnOEnf1dqHGpw7JmHqHc1NxDoalibchSk9/RWuDc=
github.com/jackc/pgx/v4 v4.5.0/go.mod h1:EpAKPLdnTorwmPUUsqrPxy5fphV18j9q3wrfRXgo+kA=
github.com/jackc/pgx/v4 v4.6.1-0.20200510190926-94ba730bb1e9/go.mod h1:t3/cdRQl6fOLDxqtlyhe9UWgfIi9R8+8v8GKV5TRA/o=
github.com/jackc/pgx/v4 v4.6.1-0.20200606145419-4e5062306904/go.mod h1:ZDaNWkt9sW1JMiNn0kdYBaLelIhw7Pg4qd+Vk6tw7Hg=
github.com/jackc/pgx/v4 v4.7.1 h1:aqUSOcStk6fik+lSE+tqfFhvt/EwT8q/oMtJbP9CjXI=
github.com/jackc/pgx/v4 v4.7.1/go.mod h1:nu42q3aPjuC1M0Nak4bnoprKlXPINqopEKqbq5AZSC4=
github.com/jackc/puddle v0.0.0-20190413234325-e4ced69a3a2b/go.mod h1:m4B5Dj62Y0fbyuIc15OsIqK0+JU8nkqQjsgx7dvjSWk=
github.com/jackc/puddle v0.0.0-20190608224051-11cab39313c9/go.mod h1:m4B5Dj62Y0fbyuIc15OsIqK0+JU8nkqQjsgx7dvjSWk=
github.com/jackc/puddle v1.1.0/go.mod h1:m4B5Dj62Y0fbyuIc15OsIqK0+JU8nkqQjsgx7dvjSWk=
github.com/jackc/puddle v1.1.1/go.mod h1:m4B5Dj62Y0fbyuIc15OsIqK0+JU8nkqQjsgx7dvjSWk=
github.com/jessevdk/go-flags v0.0.0-20141203071132-1679536dcc89/go.mod h1:4FA24M0QyGHXBuZZK/XkWh8h0e1EYbRYJSGM75WSRxI=
github.com/jmespath/go-jmespath v0.0.0-20180206201540-c2b33e8439af/go.mod h1:Nht3zPeWKUH0NzdCt2Blrr5ys8VGpn0CEB0cQHVjt7k=
github.com/jonboulle/clockwork v0.1.0/go.mod h1:Ii8DK3G1RaLaWxj9trq07+26W01tbo22gdxWY5EU2bo=
github.com/jrick/logrotate v1.0.0/go.mod h1:LNinyqDIJnpAur+b8yyulnQw/wDuN1+BYKlTRt3OuAQ=
github.com/jstemmer/go-junit-report v0.0.0-20190106144839-af01ea7f8024/go.mod h1:6v2b51hI/fHJwM22ozAgKL4VKDeJcHhJFhtBdhmNjmU=
github.com/jstemmer/go-junit-report v0.9.1 h1:6QPYqodiu3GuPL+7mfx+NwDdp2eTkp9IfEUpgAwUN0o=
pkg/process: Now that we are trying to identify the root cause of the satellite load limitations (i.e. currently the satellite has a max ability of 400 rps for uploads and we need this to be higher), we are using the golang diagnostic tools to collect insight into what the bottlenecks are. We currently have a debug endpoint to gather some cpu and mem data, but it could be useful to have continuous profiling. GCP stackdriver has support for continuous profiling so lets set that up and see if it is helpful to gather more data. This PR adds support for [GCP continuous profiler](https://cloud.google.com/profiler) which allows enabling continuous cpu/mem profiling and the stats are sent to stackdriver in google cloud console. To enable the continuous profiling for a storj component, do the following: - prereq: the workload must be running in GKE and have Stackdriver Profiling IAM role permissions - provide the config flag `debug.profilename` in the config.yaml file for the workload (i.e. satellite api process, etc). The profilename should be the workload name, for example "satellite-api". - once the above config flag is provided, the profiler will be initialized and profiling stats will automatically be sent to GCP project where the workload is running and viewable in the Stackdriver Profile page in the console The current implementation assumes the workload is running in GKE, however if we find if useful we can add support to enable this from anywhere. But for simplicity, its configured this way assuming the main goal is to enable in production systems. Change-Id: Ibf8ebe2df7bf06fdd4951ee6a1e48854dd36ad47
2020-02-25 16:46:12 +00:00
github.com/jstemmer/go-junit-report v0.9.1/go.mod h1:Brl9GWCQeLvo8nXZwPNNblvFj/XSXhF0NWZEnDohbsk=
github.com/jtolds/monkit-hw/v2 v2.0.0-20191108235325-141a0da276b3 h1:dITCBge70U9RDyZUL/Thn/yAT/ct4Rz40mNUX51dFCk=
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github.com/kisielk/errcheck v1.1.0/go.mod h1:EZBBE59ingxPouuu3KfxchcWSUPOHkagtvWXihfKN4Q=
github.com/kisielk/errcheck v1.2.0/go.mod h1:/BMXB+zMLi60iA8Vv6Ksmxu/1UDYcXs4uQLJ+jE2L00=
2018-10-06 18:57:53 +01:00
github.com/kisielk/gotool v1.0.0/go.mod h1:XhKaO+MFFWcvkIS/tQcRk01m1F5IRFswLeQ+oQHNcck=
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github.com/konsorten/go-windows-terminal-sequences v1.0.2/go.mod h1:T0+1ngSBFLxvqU3pZ+m/2kptfBszLMUkC4ZK/EgS/cQ=
github.com/kr/logfmt v0.0.0-20140226030751-b84e30acd515/go.mod h1:+0opPa2QZZtGFBFZlji/RkVcI2GknAs/DXo4wKdlNEc=
github.com/kr/pretty v0.1.0 h1:L/CwN0zerZDmRFUapSPitk6f+Q3+0za1rQkzVuMiMFI=
2018-10-06 18:57:53 +01:00
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github.com/kr/pty v1.1.8/go.mod h1:O1sed60cT9XZ5uDucP5qwvh+TE3NnUj51EiZO/lmSfw=
github.com/kr/text v0.1.0 h1:45sCR5RtlFHMR4UwH9sdQ5TC8v0qDQCHnXt+kaKSTVE=
2018-10-06 18:57:53 +01:00
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github.com/kshvakov/clickhouse v1.3.5/go.mod h1:DMzX7FxRymoNkVgizH0DWAL8Cur7wHLgx3MUnGwJqpE=
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github.com/lib/pq v1.3.0 h1:/qkRGz8zljWiDcFvgpwUpwIAPu3r07TDvs3Rws+o/pU=
github.com/lib/pq v1.3.0/go.mod h1:5WUZQaWbwv1U+lTReE5YruASi9Al49XbQIvNi/34Woo=
2018-10-06 18:57:53 +01:00
github.com/magiconair/properties v1.8.0 h1:LLgXmsheXeRoUOBOjtwPQCWIYqM/LU1ayDtDePerRcY=
github.com/magiconair/properties v1.8.0/go.mod h1:PppfXfuXeibc/6YijjN8zIbojt8czPbwD3XqdrwzmxQ=
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github.com/mattn/go-colorable v0.1.2 h1:/bC9yWikZXAL9uJdulbSfyVNIR3n3trXl+v8+1sx8mU=
github.com/mattn/go-colorable v0.1.2/go.mod h1:U0ppj6V5qS13XJ6of8GYAs25YV2eR4EVcfRqFIhoBtE=
github.com/mattn/go-colorable v0.1.6 h1:6Su7aK7lXmJ/U79bYtBjLNaha4Fs1Rg9plHpcH+vvnE=
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github.com/mattn/go-sqlite3 v2.0.3+incompatible/go.mod h1:FPy6KqzDD04eiIsT53CuJW3U88zkxoIYsOqkbpncsNc=
github.com/matttproud/golang_protobuf_extensions v1.0.1 h1:4hp9jkHxhMHkqkrB3Ix0jegS5sx/RkqARlsWZ6pIwiU=
2018-10-06 18:57:53 +01:00
github.com/matttproud/golang_protobuf_extensions v1.0.1/go.mod h1:D8He9yQNgCq6Z5Ld7szi9bcBfOoFv/3dc6xSMkL2PC0=
2019-04-04 09:12:08 +01:00
github.com/minio/sha256-simd v0.0.0-20190328051042-05b4dd3047e5/go.mod h1:2FMWW+8GMoPweT6+pI63m9YE3Lmw4J71hV56Chs1E/U=
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github.com/mitchellh/go-homedir v1.1.0 h1:lukF9ziXFxDFPkA1vsr5zpc1XuPDn/wFntq5mG+4E0Y=
github.com/mitchellh/go-homedir v1.1.0/go.mod h1:SfyaCUpYCn1Vlf4IUYiD9fPX4A5wJrkLzIz1N1q0pr0=
github.com/mitchellh/mapstructure v1.1.2 h1:fmNYVwqnSfB9mZU6OS2O6GsXM+wcskZDuKQzvN1EDeE=
github.com/mitchellh/mapstructure v1.1.2/go.mod h1:FVVH3fgwuzCH5S8UJGiWEs2h04kUh9fWfEaFds41c1Y=
github.com/morikuni/aec v0.0.0-20170113033406-39771216ff4c h1:nXxl5PrvVm2L/wCy8dQu6DMTwH4oIuGN8GJDAlqDdVE=
github.com/morikuni/aec v0.0.0-20170113033406-39771216ff4c/go.mod h1:BbKIizmSmc5MMPqRYbxO4ZU0S0+P200+tUnFx7PXmsc=
github.com/mwitkow/go-conntrack v0.0.0-20161129095857-cc309e4a2223/go.mod h1:qRWi+5nqEBWmkhHvq77mSJWrCKwh8bxhgT7d/eI7P4U=
github.com/nakagami/firebirdsql v0.0.0-20190310045651-3c02a58cfed8/go.mod h1:86wM1zFnC6/uDBfZGNwB65O+pR2OFi5q/YQaEUid1qA=
github.com/nsf/jsondiff v0.0.0-20160203110537-7de28ed2b6e3 h1:OqFSgO6CJ8heZRAbXLpT+ojX+jnnGij4qZwUz/SJJ9I=
github.com/nsf/jsondiff v0.0.0-20160203110537-7de28ed2b6e3/go.mod h1:uFMI8w+ref4v2r9jz+c9i1IfIttS/OkmLfrk1jne5hs=
2019-02-06 08:04:12 +00:00
github.com/nsf/termbox-go v0.0.0-20190121233118-02980233997d h1:x3S6kxmy49zXVVyhcnrFqxvNVCBPb2KZ9hV2RBdS840=
github.com/nsf/termbox-go v0.0.0-20190121233118-02980233997d/go.mod h1:IuKpRQcYE1Tfu+oAQqaLisqDeXgjyyltCfsaoYN18NQ=
github.com/oklog/ulid v1.3.1/go.mod h1:CirwcVhetQ6Lv90oh/F+FBtV6XMibvdAFo93nm5qn4U=
2018-10-06 18:57:53 +01:00
github.com/onsi/ginkgo v1.6.0/go.mod h1:lLunBs/Ym6LB5Z9jYTR76FiuTmxDTDusOGeTQH+WWjE=
github.com/onsi/ginkgo v1.7.0 h1:WSHQ+IS43OoUrWtD1/bbclrwK8TTH5hzp+umCiuxHgs=
github.com/onsi/ginkgo v1.7.0/go.mod h1:lLunBs/Ym6LB5Z9jYTR76FiuTmxDTDusOGeTQH+WWjE=
github.com/onsi/ginkgo v1.10.3 h1:OoxbjfXVZyod1fmWYhI7SEyaD8B00ynP3T+D5GiyHOY=
github.com/onsi/ginkgo v1.10.3/go.mod h1:lLunBs/Ym6LB5Z9jYTR76FiuTmxDTDusOGeTQH+WWjE=
github.com/onsi/gomega v1.4.3 h1:RE1xgDvH7imwFD45h+u2SgIfERHlS2yNG4DObb5BSKU=
github.com/onsi/gomega v1.4.3/go.mod h1:ex+gbHU/CVuBBDIJjb2X0qEXbFg53c61hWP/1CpauHY=
github.com/onsi/gomega v1.7.1 h1:K0jcRCwNQM3vFGh1ppMtDh/+7ApJrjldlX8fA0jDTLQ=
github.com/onsi/gomega v1.7.1/go.mod h1:XdKZgCCFLUoM/7CFJVPcG8C1xQ1AJ0vpAezJrB7JYyY=
github.com/opencontainers/go-digest v1.0.0-rc1 h1:WzifXhOVOEOuFYOJAW6aQqW0TooG2iki3E3Ii+WN7gQ=
github.com/opencontainers/go-digest v1.0.0-rc1/go.mod h1:cMLVZDEM3+U2I4VmLI6N8jQYUd2OVphdqWwCJHrFt2s=
github.com/opencontainers/image-spec v1.0.1 h1:JMemWkRwHx4Zj+fVxWoMCFm/8sYGGrUVojFA6h/TRcI=
github.com/opencontainers/image-spec v1.0.1/go.mod h1:BtxoFyWECRxE4U/7sNtV5W15zMzWCbyJoFRP3s7yZA0=
github.com/openzipkin/zipkin-go v0.1.6/go.mod h1:QgAqvLzwWbR/WpD4A3cGpPtJrZXNIiJc5AZX7/PBEpw=
2018-10-06 18:57:53 +01:00
github.com/pelletier/go-toml v1.2.0 h1:T5zMGML61Wp+FlcbWjRDT7yAxhJNAiPPLOFECq181zc=
github.com/pelletier/go-toml v1.2.0/go.mod h1:5z9KED0ma1S8pY6P1sdut58dfprrGBbd/94hg7ilaic=
github.com/pierrec/lz4 v2.0.5+incompatible/go.mod h1:pdkljMzZIN41W+lC3N2tnIh5sFi+IEE17M5jbnwPHcY=
github.com/pkg/errors v0.8.0/go.mod h1:bwawxfHBFNV+L2hUp1rHADufV3IMtnDRdf1r5NINEl0=
2019-01-07 19:11:11 +00:00
github.com/pkg/errors v0.8.1 h1:iURUrRGxPUNPdy5/HRSm+Yj6okJ6UtLINN0Q9M4+h3I=
github.com/pkg/errors v0.8.1/go.mod h1:bwawxfHBFNV+L2hUp1rHADufV3IMtnDRdf1r5NINEl0=
github.com/pkg/errors v0.9.1 h1:FEBLx1zS214owpjy7qsBeixbURkuhQAwrK5UwLGTwt4=
github.com/pkg/errors v0.9.1/go.mod h1:bwawxfHBFNV+L2hUp1rHADufV3IMtnDRdf1r5NINEl0=
2018-10-06 18:57:53 +01:00
github.com/pmezard/go-difflib v1.0.0 h1:4DBwDE0NGyQoBHbLQYPwSUPoCMWR5BEzIk/f1lZbAQM=
github.com/pmezard/go-difflib v1.0.0/go.mod h1:iKH77koFhYxTK1pcRnkKkqfTogsbg7gZNVY4sRDYZ/4=
github.com/prometheus/client_golang v0.9.1/go.mod h1:7SWBe2y4D6OKWSNQJUaRYU/AaXPKyh/dDVn+NZz0KFw=
github.com/prometheus/client_golang v0.9.3-0.20190127221311-3c4408c8b829/go.mod h1:p2iRAGwDERtqlqzRXnrOVns+ignqQo//hLXqYxZYVNs=
github.com/prometheus/client_golang v0.9.3 h1:9iH4JKXLzFbOAdtqv/a+j8aewx2Y8lAjAydhbaScPF8=
github.com/prometheus/client_golang v0.9.3/go.mod h1:/TN21ttK/J9q6uSwhBd54HahCDft0ttaMvbicHlPoso=
github.com/prometheus/client_model v0.0.0-20180712105110-5c3871d89910/go.mod h1:MbSGuTsp3dbXC40dX6PRTWyKYBIrTGTE9sqQNg2J8bo=
github.com/prometheus/client_model v0.0.0-20190115171406-56726106282f/go.mod h1:MbSGuTsp3dbXC40dX6PRTWyKYBIrTGTE9sqQNg2J8bo=
github.com/prometheus/client_model v0.0.0-20190129233127-fd36f4220a90/go.mod h1:xMI15A0UPsDsEKsMN9yxemIoYk6Tm2C1GtYGdfGttqA=
github.com/prometheus/client_model v0.0.0-20190812154241-14fe0d1b01d4 h1:gQz4mCbXsO+nc9n1hCxHcGA3Zx3Eo+UHZoInFGUIXNM=
pkg/process: Now that we are trying to identify the root cause of the satellite load limitations (i.e. currently the satellite has a max ability of 400 rps for uploads and we need this to be higher), we are using the golang diagnostic tools to collect insight into what the bottlenecks are. We currently have a debug endpoint to gather some cpu and mem data, but it could be useful to have continuous profiling. GCP stackdriver has support for continuous profiling so lets set that up and see if it is helpful to gather more data. This PR adds support for [GCP continuous profiler](https://cloud.google.com/profiler) which allows enabling continuous cpu/mem profiling and the stats are sent to stackdriver in google cloud console. To enable the continuous profiling for a storj component, do the following: - prereq: the workload must be running in GKE and have Stackdriver Profiling IAM role permissions - provide the config flag `debug.profilename` in the config.yaml file for the workload (i.e. satellite api process, etc). The profilename should be the workload name, for example "satellite-api". - once the above config flag is provided, the profiler will be initialized and profiling stats will automatically be sent to GCP project where the workload is running and viewable in the Stackdriver Profile page in the console The current implementation assumes the workload is running in GKE, however if we find if useful we can add support to enable this from anywhere. But for simplicity, its configured this way assuming the main goal is to enable in production systems. Change-Id: Ibf8ebe2df7bf06fdd4951ee6a1e48854dd36ad47
2020-02-25 16:46:12 +00:00
github.com/prometheus/client_model v0.0.0-20190812154241-14fe0d1b01d4/go.mod h1:xMI15A0UPsDsEKsMN9yxemIoYk6Tm2C1GtYGdfGttqA=
github.com/prometheus/common v0.0.0-20181113130724-41aa239b4cce/go.mod h1:daVV7qP5qjZbuso7PdcryaAu0sAZbrN9i7WWcTMWvro=
github.com/prometheus/common v0.2.0/go.mod h1:TNfzLD0ON7rHzMJeJkieUDPYmFC7Snx/y86RQel1bk4=
github.com/prometheus/common v0.4.0 h1:7etb9YClo3a6HjLzfl6rIQaU+FDfi0VSX39io3aQ+DM=
github.com/prometheus/common v0.4.0/go.mod h1:TNfzLD0ON7rHzMJeJkieUDPYmFC7Snx/y86RQel1bk4=
github.com/prometheus/procfs v0.0.0-20181005140218-185b4288413d/go.mod h1:c3At6R/oaqEKCNdg8wHV1ftS6bRYblBhIjjI8uT2IGk=
github.com/prometheus/procfs v0.0.0-20190117184657-bf6a532e95b1/go.mod h1:c3At6R/oaqEKCNdg8wHV1ftS6bRYblBhIjjI8uT2IGk=
github.com/prometheus/procfs v0.0.0-20190507164030-5867b95ac084/go.mod h1:TjEm7ze935MbeOT/UhFTIMYKhuLP4wbCsTZCD3I8kEA=
github.com/prometheus/tsdb v0.7.1/go.mod h1:qhTCs0VvXwvX/y3TZrWD7rabWM+ijKTux40TwIPHuXU=
github.com/rcrowley/go-metrics v0.0.0-20181016184325-3113b8401b8a/go.mod h1:bCqnVzQkZxMG4s8nGwiZ5l3QUCyqpo9Y+/ZMZ9VjZe4=
github.com/rogpeppe/fastuuid v0.0.0-20150106093220-6724a57986af/go.mod h1:XWv6SoW27p1b0cqNHllgS5HIMJraePCO15w5zCzIWYg=
pkg/process: Now that we are trying to identify the root cause of the satellite load limitations (i.e. currently the satellite has a max ability of 400 rps for uploads and we need this to be higher), we are using the golang diagnostic tools to collect insight into what the bottlenecks are. We currently have a debug endpoint to gather some cpu and mem data, but it could be useful to have continuous profiling. GCP stackdriver has support for continuous profiling so lets set that up and see if it is helpful to gather more data. This PR adds support for [GCP continuous profiler](https://cloud.google.com/profiler) which allows enabling continuous cpu/mem profiling and the stats are sent to stackdriver in google cloud console. To enable the continuous profiling for a storj component, do the following: - prereq: the workload must be running in GKE and have Stackdriver Profiling IAM role permissions - provide the config flag `debug.profilename` in the config.yaml file for the workload (i.e. satellite api process, etc). The profilename should be the workload name, for example "satellite-api". - once the above config flag is provided, the profiler will be initialized and profiling stats will automatically be sent to GCP project where the workload is running and viewable in the Stackdriver Profile page in the console The current implementation assumes the workload is running in GKE, however if we find if useful we can add support to enable this from anywhere. But for simplicity, its configured this way assuming the main goal is to enable in production systems. Change-Id: Ibf8ebe2df7bf06fdd4951ee6a1e48854dd36ad47
2020-02-25 16:46:12 +00:00
github.com/rogpeppe/go-internal v1.3.0/go.mod h1:M8bDsm7K2OlrFYOpmOWEs/qY81heoFRclV5y23lUDJ4=
github.com/rs/xid v1.2.1/go.mod h1:+uKXf+4Djp6Md1KODXJxgGQPKngRmWyn10oCKFzNHOQ=
github.com/rs/zerolog v1.13.0/go.mod h1:YbFCdg8HfsridGWAh22vktObvhZbQsZXe4/zB0OKkWU=
github.com/rs/zerolog v1.15.0/go.mod h1:xYTKnLHcpfU2225ny5qZjxnj9NvkumZYjJHlAThCjNc=
github.com/russross/blackfriday/v2 v2.0.1/go.mod h1:+Rmxgy9KzJVeS9/2gXHxylqXiyQDYRxCVz55jmeOWTM=
github.com/satori/go.uuid v1.2.0 h1:0uYX9dsZ2yD7q2RtLRtPSdGDWzjeM3TbMJP9utgA0ww=
github.com/satori/go.uuid v1.2.0/go.mod h1:dA0hQrYB0VpLJoorglMZABFdXlWrHn1NEOzdhQKdks0=
github.com/shopspring/decimal v0.0.0-20180709203117-cd690d0c9e24/go.mod h1:M+9NzErvs504Cn4c5DxATwIqPbtswREoFCre64PpcG4=
github.com/shopspring/decimal v0.0.0-20200227202807-02e2044944cc h1:jUIKcSPO9MoMJBbEoyE/RJoE8vz7Mb8AjvifMMwSyvY=
github.com/shopspring/decimal v0.0.0-20200227202807-02e2044944cc/go.mod h1:DKyhrW/HYNuLGql+MJL6WCR6knT2jwCFRcu2hWCYk4o=
github.com/shurcooL/sanitized_anchor_name v1.0.0/go.mod h1:1NzhyTcUVG4SuEtjjoZeVRXNmyL/1OwPU0+IJeTBvfc=
github.com/sirupsen/logrus v1.2.0/go.mod h1:LxeOpSwHxABJmUn/MG1IvRgCAasNZTLOkJPxbbu5VWo=
github.com/sirupsen/logrus v1.4.1 h1:GL2rEmy6nsikmW0r8opw9JIRScdMF5hA8cOYLH7In1k=
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github.com/sirupsen/logrus v1.4.2/go.mod h1:tLMulIdttU9McNUspp0xgXVQah82FyeX6MwdIuYE2rE=
github.com/sirupsen/logrus v1.5.0 h1:1N5EYkVAPEywqZRJd7cwnRtCb6xJx7NH3T3WUTF980Q=
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github.com/soheilhy/cmux v0.1.4/go.mod h1:IM3LyeVVIOuxMH7sFAkER9+bJ4dT7Ms6E4xg4kGIyLM=
github.com/spacemonkeygo/monkit/v3 v3.0.0-20191108235033-eacca33b3037/go.mod h1:JcK1pCbReQsOsMKF/POFSZCq7drXFybgGmbc27tuwes=
github.com/spacemonkeygo/monkit/v3 v3.0.4 h1:Ay+PZirv+qfd4sqcT+X/U3BnC7AcIaqp/IXh0oV36k8=
github.com/spacemonkeygo/monkit/v3 v3.0.4/go.mod h1:JcK1pCbReQsOsMKF/POFSZCq7drXFybgGmbc27tuwes=
github.com/spacemonkeygo/monkit/v3 v3.0.5 h1:vMW8Ne6WAUU/OMYaSv7KGW9h/sRNgeh6TyBapOSuMhM=
github.com/spacemonkeygo/monkit/v3 v3.0.5/go.mod h1:JcK1pCbReQsOsMKF/POFSZCq7drXFybgGmbc27tuwes=
github.com/spacemonkeygo/monkit/v3 v3.0.7-0.20200515175308-072401d8c752 h1:WcQDknqg0qajLNYKv3mXgbkWlYs5rPgZehGJFWePHVI=
github.com/spacemonkeygo/monkit/v3 v3.0.7-0.20200515175308-072401d8c752/go.mod h1:kj1ViJhlyADa7DiA4xVnTuPA46lFKbM7mxQTrXCuJP4=
2018-10-06 18:57:53 +01:00
github.com/spacemonkeygo/monotime v0.0.0-20180824235756-e3f48a95f98a h1:8+cCjxhToanKmxLIbuyBNe2EnpgwhiivsIaRJstDRFA=
github.com/spacemonkeygo/monotime v0.0.0-20180824235756-e3f48a95f98a/go.mod h1:ul4bvvnCOPZgq8w0nTkSmWVg/hauVpFS97Am1YM1XXo=
github.com/spacemonkeygo/spacelog v0.0.0-20180420211403-2296661a0572 h1:RC6RW7j+1+HkWaX/Yh71Ee5ZHaHYt7ZP4sQgUrm6cDU=
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2018-10-06 18:57:53 +01:00
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github.com/spf13/cast v1.3.0 h1:oget//CVOEoFewqQxwr0Ej5yjygnqGkvggSE/gB35Q8=
github.com/spf13/cast v1.3.0/go.mod h1:Qx5cxh0v+4UWYiBimWS+eyWzqEqokIECu5etghLkUJE=
github.com/spf13/cobra v0.0.6 h1:breEStsVwemnKh2/s6gMvSdMEkwW0sK8vGStnlVBMCs=
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2018-10-06 18:57:53 +01:00
github.com/spf13/jwalterweatherman v1.0.0 h1:XHEdyB+EcvlqZamSM4ZOMGlc93t6AcsBEu9Gc1vn7yk=
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github.com/spf13/pflag v1.0.3 h1:zPAT6CGy6wXeQ7NtTnaTerfKOsV6V6F8agHXFiazDkg=
github.com/spf13/pflag v1.0.3/go.mod h1:DYY7MBk1bdzusC3SYhjObp+wFpr4gzcvqqNjLnInEg4=
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github.com/spf13/viper v1.4.0 h1:yXHLWeravcrgGyFSyCgdYpXQ9dR9c/WED3pg1RhxqEU=
github.com/spf13/viper v1.4.0/go.mod h1:PTJ7Z/lr49W6bUbkmS1V3by4uWynFiR9p7+dSq/yZzE=
github.com/stretchr/objx v0.1.0/go.mod h1:HFkY916IF+rwdDfMAkV7OtwuqBVzrE8GR6GFx+wExME=
github.com/stretchr/objx v0.1.1 h1:2vfRuCMp5sSVIDSqO8oNnWJq7mPa6KVP3iPIwFBuy8A=
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2018-10-06 18:57:53 +01:00
github.com/stretchr/testify v1.2.2/go.mod h1:a8OnRcib4nhh0OaRAV+Yts87kKdq0PP7pXfy6kDkUVs=
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github.com/stretchr/testify v1.4.0/go.mod h1:j7eGeouHqKxXV5pUuKE4zz7dFj8WfuZ+81PSLYec5m4=
github.com/stretchr/testify v1.5.1 h1:nOGnQDM7FYENwehXlg/kFVnos3rEvtKTjRvOWSzb6H4=
github.com/stretchr/testify v1.5.1/go.mod h1:5W2xD1RspED5o8YsWQXVCued0rvSQ+mT+I5cxcmMvtA=
github.com/stripe/stripe-go v70.15.0+incompatible h1:hNML7M1zx8RgtepEMlxyu/FpVPrP7KZm1gPFQquJQvM=
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github.com/tidwall/pretty v0.0.0-20180105212114-65a9db5fad51/go.mod h1:XNkn88O1ChpSDQmQeStsy+sBenx6DDtFZJxhVysOjyk=
github.com/tmc/grpc-websocket-proxy v0.0.0-20190109142713-0ad062ec5ee5/go.mod h1:ncp9v5uamzpCO7NfCPTXjqaC+bZgJeR0sMTm6dMHP7U=
github.com/ugorji/go v1.1.4/go.mod h1:uQMGLiO92mf5W77hV/PUCpI3pbzQx3CRekS0kk+RGrc=
github.com/vivint/infectious v0.0.0-20200605153912-25a574ae18a3 h1:zMsHhfK9+Wdl1F7sIKLyx3wrOFofpb3rWFbA4HgcK5k=
github.com/vivint/infectious v0.0.0-20200605153912-25a574ae18a3/go.mod h1:R0Gbuw7ElaGSLOZUSwBm/GgVwMd30jWxBDdAyMOeTuc=
github.com/xanzy/go-gitlab v0.15.0/go.mod h1:8zdQa/ri1dfn8eS3Ir1SyfvOKlw7WBJ8DVThkpGiXrs=
github.com/xdg/scram v0.0.0-20180814205039-7eeb5667e42c/go.mod h1:lB8K/P019DLNhemzwFU4jHLhdvlE6uDZjXFejJXr49I=
github.com/xdg/stringprep v1.0.0/go.mod h1:Jhud4/sHMO4oL310DaZAKk9ZaJ08SJfe+sJh0HrGL1Y=
github.com/xiang90/probing v0.0.0-20190116061207-43a291ad63a2/go.mod h1:UETIi67q53MR2AWcXfiuqkDkRtnGDLqkBTpCHuJHxtU=
github.com/xordataexchange/crypt v0.0.3-0.20170626215501-b2862e3d0a77/go.mod h1:aYKd//L2LvnjZzWKhF00oedf4jCCReLcmhLdhm1A27Q=
github.com/yuin/goldmark v1.1.27/go.mod h1:3hX8gzYuyVAZsxl0MRgGTJEmQBFcNTphYh9decYSb74=
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github.com/yuin/gopher-lua v0.0.0-20190206043414-8bfc7677f583/go.mod h1:gqRgreBUhTSL0GeU64rtZ3Uq3wtjOa/TB2YfrtkCbVQ=
github.com/zeebo/admission/v2 v2.0.0/go.mod h1:gSeHGelDHW7Vq6UyJo2boeSt/6Dsnqpisv0i4YZSOyM=
github.com/zeebo/admission/v3 v3.0.1 h1:/IWg2jLhfjBOUhhdKcbweSzcY3QlbbE57sqvU72EpqA=
github.com/zeebo/admission/v3 v3.0.1/go.mod h1:BP3isIv9qa2A7ugEratNq1dnl2oZRXaQUGdU7WXKtbw=
github.com/zeebo/assert v0.0.0-20181109011804-10f827ce2ed6/go.mod h1:yssERNPivllc1yU3BvpjYI5BUW+zglcz6QWqeVRL5t0=
github.com/zeebo/assert v1.1.0 h1:hU1L1vLTHsnO8x8c9KAR5GmM5QscxHg5RNU5z5qbUWY=
github.com/zeebo/assert v1.1.0/go.mod h1:Pq9JiuJQpG8JLJdtkwrJESF0Foym2/D9XMU5ciN/wJ0=
github.com/zeebo/errs v1.1.1/go.mod h1:Yj8dHrUQwls1bF3dr/vcSIu+qf4mI7idnTcHfoACc6I=
github.com/zeebo/errs v1.2.2 h1:5NFypMTuSdoySVTqlNs1dEoU21QVamMQJxW/Fii5O7g=
github.com/zeebo/errs v1.2.2/go.mod h1:sgbWHsvVuTPHcqJJGQ1WhI5KbWlHYz+2+2C/LSEtCw4=
2018-10-06 18:57:53 +01:00
github.com/zeebo/float16 v0.1.0 h1:kRqxv5og6z1emEyz5FpW0/BVHe5VfxEAw6b1ljCZlUc=
github.com/zeebo/float16 v0.1.0/go.mod h1:fssGvvXu+XS8MH57cKmyrLB/cqioYeYX/2mXCN3a5wo=
github.com/zeebo/incenc v0.0.0-20180505221441-0d92902eec54 h1:+cwNE5KJ3pika4HuzmDHkDlK5myo0G9Sv+eO7WWxnUQ=
github.com/zeebo/incenc v0.0.0-20180505221441-0d92902eec54/go.mod h1:EI8LcOBDlSL3POyqwC1eJhOYlMBMidES+613EtmmT5w=
lib/uplink: encryption context (#2349) * lib/uplink: encryption context Change-Id: I5c23dca3286a46b713b30c4997e9ae6e630b2280 * lib/uplink: bucket operation examples Change-Id: Ia0f6e69f365dcff0cf11c731f51b30842bce053b * lib/uplink: encryption key sharing test cases Change-Id: I3a172d565f33f4e591402cdcb9460664a7cc7fbe * fix encrypted path prefix restriction issue Change-Id: I8f3921f9d52aaf4b84039de608b8cbbc88769554 * implement panics in libuplink encryption code todo on cipher suite selection as well as an api concern Change-Id: Ifa39eb3cc4b3443f7d96f9304df9b2ac4ec4085d * implement GetProjectInfo api call to get salt Change-Id: Ic5f6b3be9ea35df48c1aa214ab5d355fb328e2cf * some fixes and accessors for encryption store Change-Id: I3bb61f6712a037900e2a96e72ad4029ec1d3f718 * general fixes to builds/tests/etc Change-Id: I9930fa96acb3b221d9a001f8e274af5729cc8a47 * java bindings changes Change-Id: Ia2bd4c9c69739c8d3154d79616cff1f36fb403b6 * get libuplink examples passing Change-Id: I828f09a144160e0a5dd932324f78491ae2ec8a07 * fix proto.lock file Change-Id: I2fbbf4d0976a7d0473c2645e6dcb21aaa3be7651 * fix proto.lock again Change-Id: I92702cf49e1a340eef6379c2be4f7c4a268112a9 * fix golint issues Change-Id: I631ff9f43307a58e3b25a58cbb4a4cc2495f5eb6 * more linting fixes Change-Id: I51f8f30b367b5bca14c94b15417b9a4c9e7aa0ce * bug fixed by structs bump Change-Id: Ibb03c691fce7606c35c08721b3ef0781ab48a38a * retrigger Change-Id: Ieee0470b6a2d07168a1578552e8e7f271ae93a13 * retrigger Change-Id: I753d63853171e6a436c104ce176048892eb974c5 * semantic merge conflict Change-Id: I9419448496de90340569047a6a16a1b858a7978a * update total to match prod defaults Change-Id: I693d55c1ebb28b5803ee1d26e9e198decf82308b * retrigger Change-Id: I28b74d5d6202f61aa3866fe407d423f6a0a14b9e * retrigger Change-Id: I6fd054885c715f602e2cef623fd464c42e88742c * retrigger Change-Id: I6a01bae88c72406d4ed5a8f13bf8a2b3c650bd2d
2019-06-27 18:36:51 +01:00
github.com/zeebo/structs v1.0.2 h1:kvcd7s2LqXuO9cdV5LqrGHCOAfCBXaZpKCA3jD9SJIc=
github.com/zeebo/structs v1.0.2/go.mod h1:LphfpprlqJQcbCq+eA3iIK/NsejMwk9mlfH/tM1XuKQ=
github.com/zenazn/goji v0.9.0/go.mod h1:7S9M489iMyHBNxwZnk9/EHS098H4/F6TATF2mIxtB1Q=
gitlab.com/nyarla/go-crypt v0.0.0-20160106005555-d9a5dc2b789b/go.mod h1:T3BPAOm2cqquPa0MKWeNkmOM5RQsRhkrwMWonFMN7fE=
go.etcd.io/bbolt v1.3.2/go.mod h1:IbVyRI1SCnLcuJnV2u8VeU0CEYM7e686BmAb1XKL+uU=
go.etcd.io/bbolt v1.3.4 h1:hi1bXHMVrlQh6WwxAy+qZCV/SYIlqo+Ushwdpa4tAKg=
go.etcd.io/bbolt v1.3.4/go.mod h1:G5EMThwa9y8QZGBClrRx5EY+Yw9kAhnjy3bSjsnlVTQ=
go.mongodb.org/mongo-driver v1.1.0/go.mod h1:u7ryQJ+DOzQmeO7zB6MHyr8jkEQvC8vH7qLUO4lqsUM=
go.opencensus.io v0.20.1/go.mod h1:6WKK9ahsWS3RSO+PY9ZHZUfv2irvY6gN279GOPZjmmk=
go.opencensus.io v0.21.0/go.mod h1:mSImk1erAIZhrmZN+AvHh14ztQfjbGwt4TtuofqLduU=
pkg/process: Now that we are trying to identify the root cause of the satellite load limitations (i.e. currently the satellite has a max ability of 400 rps for uploads and we need this to be higher), we are using the golang diagnostic tools to collect insight into what the bottlenecks are. We currently have a debug endpoint to gather some cpu and mem data, but it could be useful to have continuous profiling. GCP stackdriver has support for continuous profiling so lets set that up and see if it is helpful to gather more data. This PR adds support for [GCP continuous profiler](https://cloud.google.com/profiler) which allows enabling continuous cpu/mem profiling and the stats are sent to stackdriver in google cloud console. To enable the continuous profiling for a storj component, do the following: - prereq: the workload must be running in GKE and have Stackdriver Profiling IAM role permissions - provide the config flag `debug.profilename` in the config.yaml file for the workload (i.e. satellite api process, etc). The profilename should be the workload name, for example "satellite-api". - once the above config flag is provided, the profiler will be initialized and profiling stats will automatically be sent to GCP project where the workload is running and viewable in the Stackdriver Profile page in the console The current implementation assumes the workload is running in GKE, however if we find if useful we can add support to enable this from anywhere. But for simplicity, its configured this way assuming the main goal is to enable in production systems. Change-Id: Ibf8ebe2df7bf06fdd4951ee6a1e48854dd36ad47
2020-02-25 16:46:12 +00:00
go.opencensus.io v0.22.0/go.mod h1:+kGneAE2xo2IficOXnaByMWTGM9T73dGwxeWcUqIpI8=
go.opencensus.io v0.22.2 h1:75k/FF0Q2YM8QYo07VPddOLBslDt1MZOdEslOHvmzAs=
go.opencensus.io v0.22.2/go.mod h1:yxeiOL68Rb0Xd1ddK5vPZ/oVn4vY4Ynel7k9FzqtOIw=
go.uber.org/atomic v1.3.2/go.mod h1:gD2HeocX3+yG+ygLZcrzQJaqmWj9AIm7n08wl/qW/PE=
go.uber.org/atomic v1.4.0/go.mod h1:gD2HeocX3+yG+ygLZcrzQJaqmWj9AIm7n08wl/qW/PE=
go.uber.org/atomic v1.6.0 h1:Ezj3JGmsOnG1MoRWQkPBsKLe9DwWD9QeXzTRzzldNVk=
go.uber.org/atomic v1.6.0/go.mod h1:sABNBOSYdrvTF6hTgEIbc7YasKWGhgEQZyfxyTvoXHQ=
2018-10-06 18:57:53 +01:00
go.uber.org/multierr v1.1.0/go.mod h1:wR5kodmAFQ0UK8QlbwjlSNy0Z68gJhDJUG5sjR94q/0=
go.uber.org/multierr v1.5.0 h1:KCa4XfM8CWFCpxXRGok+Q0SS/0XBhMDbHHGABQLvD2A=
go.uber.org/multierr v1.5.0/go.mod h1:FeouvMocqHpRaaGuG9EjoKcStLC43Zu/fmqdUMPcKYU=
go.uber.org/tools v0.0.0-20190618225709-2cfd321de3ee h1:0mgffUl7nfd+FpvXMVz4IDEaUSmT1ysygQC7qYo7sG4=
go.uber.org/tools v0.0.0-20190618225709-2cfd321de3ee/go.mod h1:vJERXedbb3MVM5f9Ejo0C68/HhF8uaILCdgjnY+goOA=
go.uber.org/zap v1.9.1/go.mod h1:vwi/ZaCAaUcBkycHslxD9B2zi4UTXhF60s6SWpuDF0Q=
go.uber.org/zap v1.10.0/go.mod h1:vwi/ZaCAaUcBkycHslxD9B2zi4UTXhF60s6SWpuDF0Q=
go.uber.org/zap v1.14.1/go.mod h1:Mb2vm2krFEG5DV0W9qcHBYFtp/Wku1cvYaqPsS/WYfc=
go.uber.org/zap v1.15.0 h1:ZZCA22JRF2gQE5FoNmhmrf7jeJJ2uhqDUNRYKm8dvmM=
go.uber.org/zap v1.15.0/go.mod h1:Mb2vm2krFEG5DV0W9qcHBYFtp/Wku1cvYaqPsS/WYfc=
golang.org/x/crypto v0.0.0-20170930174604-9419663f5a44/go.mod h1:6SG95UA2DQfeDnfUPMdvaQW0Q7yPrPDi9nlGo2tz2b4=
golang.org/x/crypto v0.0.0-20180904163835-0709b304e793/go.mod h1:6SG95UA2DQfeDnfUPMdvaQW0Q7yPrPDi9nlGo2tz2b4=
golang.org/x/crypto v0.0.0-20190308221718-c2843e01d9a2/go.mod h1:djNgcEr1/C05ACkg1iLfiJU5Ep61QUkGW8qpdssI0+w=
golang.org/x/crypto v0.0.0-20190325154230-a5d413f7728c/go.mod h1:djNgcEr1/C05ACkg1iLfiJU5Ep61QUkGW8qpdssI0+w=
golang.org/x/crypto v0.0.0-20190411191339-88737f569e3a/go.mod h1:WFFai1msRO1wXaEeE5yQxYXgSfI8pQAWXbQop6sCtWE=
golang.org/x/crypto v0.0.0-20190426145343-a29dc8fdc734/go.mod h1:yigFU9vqHzYiE8UmvKecakEJjdnWj3jj499lnFckfCI=
pkg/process: Now that we are trying to identify the root cause of the satellite load limitations (i.e. currently the satellite has a max ability of 400 rps for uploads and we need this to be higher), we are using the golang diagnostic tools to collect insight into what the bottlenecks are. We currently have a debug endpoint to gather some cpu and mem data, but it could be useful to have continuous profiling. GCP stackdriver has support for continuous profiling so lets set that up and see if it is helpful to gather more data. This PR adds support for [GCP continuous profiler](https://cloud.google.com/profiler) which allows enabling continuous cpu/mem profiling and the stats are sent to stackdriver in google cloud console. To enable the continuous profiling for a storj component, do the following: - prereq: the workload must be running in GKE and have Stackdriver Profiling IAM role permissions - provide the config flag `debug.profilename` in the config.yaml file for the workload (i.e. satellite api process, etc). The profilename should be the workload name, for example "satellite-api". - once the above config flag is provided, the profiler will be initialized and profiling stats will automatically be sent to GCP project where the workload is running and viewable in the Stackdriver Profile page in the console The current implementation assumes the workload is running in GKE, however if we find if useful we can add support to enable this from anywhere. But for simplicity, its configured this way assuming the main goal is to enable in production systems. Change-Id: Ibf8ebe2df7bf06fdd4951ee6a1e48854dd36ad47
2020-02-25 16:46:12 +00:00
golang.org/x/crypto v0.0.0-20190510104115-cbcb75029529/go.mod h1:yigFU9vqHzYiE8UmvKecakEJjdnWj3jj499lnFckfCI=
golang.org/x/crypto v0.0.0-20190605123033-f99c8df09eb5/go.mod h1:yigFU9vqHzYiE8UmvKecakEJjdnWj3jj499lnFckfCI=
golang.org/x/crypto v0.0.0-20190820162420-60c769a6c586/go.mod h1:yigFU9vqHzYiE8UmvKecakEJjdnWj3jj499lnFckfCI=
golang.org/x/crypto v0.0.0-20190911031432-227b76d455e7/go.mod h1:yigFU9vqHzYiE8UmvKecakEJjdnWj3jj499lnFckfCI=
pkg/process: Now that we are trying to identify the root cause of the satellite load limitations (i.e. currently the satellite has a max ability of 400 rps for uploads and we need this to be higher), we are using the golang diagnostic tools to collect insight into what the bottlenecks are. We currently have a debug endpoint to gather some cpu and mem data, but it could be useful to have continuous profiling. GCP stackdriver has support for continuous profiling so lets set that up and see if it is helpful to gather more data. This PR adds support for [GCP continuous profiler](https://cloud.google.com/profiler) which allows enabling continuous cpu/mem profiling and the stats are sent to stackdriver in google cloud console. To enable the continuous profiling for a storj component, do the following: - prereq: the workload must be running in GKE and have Stackdriver Profiling IAM role permissions - provide the config flag `debug.profilename` in the config.yaml file for the workload (i.e. satellite api process, etc). The profilename should be the workload name, for example "satellite-api". - once the above config flag is provided, the profiler will be initialized and profiling stats will automatically be sent to GCP project where the workload is running and viewable in the Stackdriver Profile page in the console The current implementation assumes the workload is running in GKE, however if we find if useful we can add support to enable this from anywhere. But for simplicity, its configured this way assuming the main goal is to enable in production systems. Change-Id: Ibf8ebe2df7bf06fdd4951ee6a1e48854dd36ad47
2020-02-25 16:46:12 +00:00
golang.org/x/crypto v0.0.0-20191011191535-87dc89f01550/go.mod h1:yigFU9vqHzYiE8UmvKecakEJjdnWj3jj499lnFckfCI=
golang.org/x/crypto v0.0.0-20200115085410-6d4e4cb37c7d/go.mod h1:LzIPMQfyMNhhGPhUkYOs5KpL4U8rLKemX1yGLhDgUto=
golang.org/x/crypto v0.0.0-20200220183623-bac4c82f6975/go.mod h1:LzIPMQfyMNhhGPhUkYOs5KpL4U8rLKemX1yGLhDgUto=
golang.org/x/crypto v0.0.0-20200323165209-0ec3e9974c59/go.mod h1:LzIPMQfyMNhhGPhUkYOs5KpL4U8rLKemX1yGLhDgUto=
golang.org/x/crypto v0.0.0-20200604202706-70a84ac30bf9 h1:vEg9joUBmeBcK9iSJftGNf3coIG4HqZElCPehJsfAYM=
golang.org/x/crypto v0.0.0-20200604202706-70a84ac30bf9/go.mod h1:LzIPMQfyMNhhGPhUkYOs5KpL4U8rLKemX1yGLhDgUto=
golang.org/x/crypto v0.0.0-20200622213623-75b288015ac9 h1:psW17arqaxU48Z5kZ0CQnkZWQJsqcURM6tKiBApRjXI=
golang.org/x/crypto v0.0.0-20200622213623-75b288015ac9/go.mod h1:LzIPMQfyMNhhGPhUkYOs5KpL4U8rLKemX1yGLhDgUto=
golang.org/x/crypto v0.0.0-20200728195943-123391ffb6de h1:ikNHVSjEfnvz6sxdSPCaPt572qowuyMDMJLLm3Db3ig=
golang.org/x/crypto v0.0.0-20200728195943-123391ffb6de/go.mod h1:LzIPMQfyMNhhGPhUkYOs5KpL4U8rLKemX1yGLhDgUto=
golang.org/x/exp v0.0.0-20190121172915-509febef88a4/go.mod h1:CJ0aWSM057203Lf6IL+f9T1iT9GByDxfZKAQTCR3kQA=
pkg/process: Now that we are trying to identify the root cause of the satellite load limitations (i.e. currently the satellite has a max ability of 400 rps for uploads and we need this to be higher), we are using the golang diagnostic tools to collect insight into what the bottlenecks are. We currently have a debug endpoint to gather some cpu and mem data, but it could be useful to have continuous profiling. GCP stackdriver has support for continuous profiling so lets set that up and see if it is helpful to gather more data. This PR adds support for [GCP continuous profiler](https://cloud.google.com/profiler) which allows enabling continuous cpu/mem profiling and the stats are sent to stackdriver in google cloud console. To enable the continuous profiling for a storj component, do the following: - prereq: the workload must be running in GKE and have Stackdriver Profiling IAM role permissions - provide the config flag `debug.profilename` in the config.yaml file for the workload (i.e. satellite api process, etc). The profilename should be the workload name, for example "satellite-api". - once the above config flag is provided, the profiler will be initialized and profiling stats will automatically be sent to GCP project where the workload is running and viewable in the Stackdriver Profile page in the console The current implementation assumes the workload is running in GKE, however if we find if useful we can add support to enable this from anywhere. But for simplicity, its configured this way assuming the main goal is to enable in production systems. Change-Id: Ibf8ebe2df7bf06fdd4951ee6a1e48854dd36ad47
2020-02-25 16:46:12 +00:00
golang.org/x/exp v0.0.0-20190306152737-a1d7652674e8/go.mod h1:CJ0aWSM057203Lf6IL+f9T1iT9GByDxfZKAQTCR3kQA=
golang.org/x/exp v0.0.0-20190510132918-efd6b22b2522/go.mod h1:ZjyILWgesfNpC6sMxTJOJm9Kp84zZh5NQWvqDGG3Qr8=
golang.org/x/exp v0.0.0-20190829153037-c13cbed26979/go.mod h1:86+5VVa7VpoJ4kLfm080zCjGlMRFzhUhsZKEZO7MGek=
golang.org/x/exp v0.0.0-20191227195350-da58074b4299 h1:zQpM52jfKHG6II1ISZY1ZcpygvuSFZpLwfluuF89XOg=
pkg/process: Now that we are trying to identify the root cause of the satellite load limitations (i.e. currently the satellite has a max ability of 400 rps for uploads and we need this to be higher), we are using the golang diagnostic tools to collect insight into what the bottlenecks are. We currently have a debug endpoint to gather some cpu and mem data, but it could be useful to have continuous profiling. GCP stackdriver has support for continuous profiling so lets set that up and see if it is helpful to gather more data. This PR adds support for [GCP continuous profiler](https://cloud.google.com/profiler) which allows enabling continuous cpu/mem profiling and the stats are sent to stackdriver in google cloud console. To enable the continuous profiling for a storj component, do the following: - prereq: the workload must be running in GKE and have Stackdriver Profiling IAM role permissions - provide the config flag `debug.profilename` in the config.yaml file for the workload (i.e. satellite api process, etc). The profilename should be the workload name, for example "satellite-api". - once the above config flag is provided, the profiler will be initialized and profiling stats will automatically be sent to GCP project where the workload is running and viewable in the Stackdriver Profile page in the console The current implementation assumes the workload is running in GKE, however if we find if useful we can add support to enable this from anywhere. But for simplicity, its configured this way assuming the main goal is to enable in production systems. Change-Id: Ibf8ebe2df7bf06fdd4951ee6a1e48854dd36ad47
2020-02-25 16:46:12 +00:00
golang.org/x/exp v0.0.0-20191227195350-da58074b4299/go.mod h1:2RIsYlXP63K8oxa1u096TMicItID8zy7Y6sNkU49FU4=
golang.org/x/image v0.0.0-20190227222117-0694c2d4d067/go.mod h1:kZ7UVZpmo3dzQBMxlp+ypCbDeSB+sBbTgSJuh5dn5js=
golang.org/x/image v0.0.0-20190802002840-cff245a6509b/go.mod h1:FeLwcggjj3mMvU+oOTbSwawSJRM1uh48EjtB4UJZlP0=
golang.org/x/lint v0.0.0-20181026193005-c67002cb31c3/go.mod h1:UVdnD1Gm6xHRNCYTkRU2/jEulfH38KcIWyp/GAMgvoE=
golang.org/x/lint v0.0.0-20190227174305-5b3e6a55c961/go.mod h1:wehouNa3lNwaWXcvxsM5YxQ5yQlVC4a0KAMCusXpPoU=
golang.org/x/lint v0.0.0-20190301231843-5614ed5bae6f/go.mod h1:UVdnD1Gm6xHRNCYTkRU2/jEulfH38KcIWyp/GAMgvoE=
golang.org/x/lint v0.0.0-20190313153728-d0100b6bd8b3/go.mod h1:6SW0HCj/g11FgYtHlgUYUwCkIfeOF89ocIRzGO/8vkc=
pkg/process: Now that we are trying to identify the root cause of the satellite load limitations (i.e. currently the satellite has a max ability of 400 rps for uploads and we need this to be higher), we are using the golang diagnostic tools to collect insight into what the bottlenecks are. We currently have a debug endpoint to gather some cpu and mem data, but it could be useful to have continuous profiling. GCP stackdriver has support for continuous profiling so lets set that up and see if it is helpful to gather more data. This PR adds support for [GCP continuous profiler](https://cloud.google.com/profiler) which allows enabling continuous cpu/mem profiling and the stats are sent to stackdriver in google cloud console. To enable the continuous profiling for a storj component, do the following: - prereq: the workload must be running in GKE and have Stackdriver Profiling IAM role permissions - provide the config flag `debug.profilename` in the config.yaml file for the workload (i.e. satellite api process, etc). The profilename should be the workload name, for example "satellite-api". - once the above config flag is provided, the profiler will be initialized and profiling stats will automatically be sent to GCP project where the workload is running and viewable in the Stackdriver Profile page in the console The current implementation assumes the workload is running in GKE, however if we find if useful we can add support to enable this from anywhere. But for simplicity, its configured this way assuming the main goal is to enable in production systems. Change-Id: Ibf8ebe2df7bf06fdd4951ee6a1e48854dd36ad47
2020-02-25 16:46:12 +00:00
golang.org/x/lint v0.0.0-20190409202823-959b441ac422/go.mod h1:6SW0HCj/g11FgYtHlgUYUwCkIfeOF89ocIRzGO/8vkc=
golang.org/x/lint v0.0.0-20190909230951-414d861bb4ac/go.mod h1:6SW0HCj/g11FgYtHlgUYUwCkIfeOF89ocIRzGO/8vkc=
golang.org/x/lint v0.0.0-20190930215403-16217165b5de/go.mod h1:6SW0HCj/g11FgYtHlgUYUwCkIfeOF89ocIRzGO/8vkc=
golang.org/x/lint v0.0.0-20191125180803-fdd1cda4f05f h1:J5lckAjkw6qYlOZNj90mLYNTEKDvWeuc1yieZ8qUzUE=
pkg/process: Now that we are trying to identify the root cause of the satellite load limitations (i.e. currently the satellite has a max ability of 400 rps for uploads and we need this to be higher), we are using the golang diagnostic tools to collect insight into what the bottlenecks are. We currently have a debug endpoint to gather some cpu and mem data, but it could be useful to have continuous profiling. GCP stackdriver has support for continuous profiling so lets set that up and see if it is helpful to gather more data. This PR adds support for [GCP continuous profiler](https://cloud.google.com/profiler) which allows enabling continuous cpu/mem profiling and the stats are sent to stackdriver in google cloud console. To enable the continuous profiling for a storj component, do the following: - prereq: the workload must be running in GKE and have Stackdriver Profiling IAM role permissions - provide the config flag `debug.profilename` in the config.yaml file for the workload (i.e. satellite api process, etc). The profilename should be the workload name, for example "satellite-api". - once the above config flag is provided, the profiler will be initialized and profiling stats will automatically be sent to GCP project where the workload is running and viewable in the Stackdriver Profile page in the console The current implementation assumes the workload is running in GKE, however if we find if useful we can add support to enable this from anywhere. But for simplicity, its configured this way assuming the main goal is to enable in production systems. Change-Id: Ibf8ebe2df7bf06fdd4951ee6a1e48854dd36ad47
2020-02-25 16:46:12 +00:00
golang.org/x/lint v0.0.0-20191125180803-fdd1cda4f05f/go.mod h1:5qLYkcX4OjUUV8bRuDixDT3tpyyb+LUpUlRWLxfhWrs=
golang.org/x/mobile v0.0.0-20190312151609-d3739f865fa6/go.mod h1:z+o9i4GpDbdi3rU15maQ/Ox0txvL9dWGYEHz965HBQE=
golang.org/x/mobile v0.0.0-20190719004257-d2bd2a29d028/go.mod h1:E/iHnbuqvinMTCcRqshq8CkpyQDoeVncDDYHnLhea+o=
golang.org/x/mod v0.0.0-20190513183733-4bf6d317e70e/go.mod h1:mXi4GBBbnImb6dmsKGUJ2LatrhH/nqhxcFungHvyanc=
golang.org/x/mod v0.1.0/go.mod h1:0QHyrYULN0/3qlju5TqG8bIK38QM8yzMo5ekMj3DlcY=
golang.org/x/mod v0.1.1-0.20191105210325-c90efee705ee/go.mod h1:QqPTAvyqsEbceGzBzNggFXnrqF1CaUcvgkdR5Ot7KZg=
golang.org/x/mod v0.2.0 h1:KU7oHjnv3XNWfa5COkzUifxZmxp1TyI7ImMXqFxLwvQ=
golang.org/x/mod v0.2.0/go.mod h1:s0Qsj1ACt9ePp/hMypM3fl4fZqREWJwdYDEqhRiZZUA=
golang.org/x/net v0.0.0-20180724234803-3673e40ba225/go.mod h1:mL1N/T3taQHkDXs73rZJwtUhF3w3ftmwwsq0BUmARs4=
golang.org/x/net v0.0.0-20180826012351-8a410e7b638d/go.mod h1:mL1N/T3taQHkDXs73rZJwtUhF3w3ftmwwsq0BUmARs4=
2018-10-06 18:57:53 +01:00
golang.org/x/net v0.0.0-20180906233101-161cd47e91fd/go.mod h1:mL1N/T3taQHkDXs73rZJwtUhF3w3ftmwwsq0BUmARs4=
golang.org/x/net v0.0.0-20181108082009-03003ca0c849/go.mod h1:mL1N/T3taQHkDXs73rZJwtUhF3w3ftmwwsq0BUmARs4=
golang.org/x/net v0.0.0-20181114220301-adae6a3d119a/go.mod h1:mL1N/T3taQHkDXs73rZJwtUhF3w3ftmwwsq0BUmARs4=
golang.org/x/net v0.0.0-20181220203305-927f97764cc3/go.mod h1:mL1N/T3taQHkDXs73rZJwtUhF3w3ftmwwsq0BUmARs4=
golang.org/x/net v0.0.0-20190108225652-1e06a53dbb7e/go.mod h1:mL1N/T3taQHkDXs73rZJwtUhF3w3ftmwwsq0BUmARs4=
golang.org/x/net v0.0.0-20190125091013-d26f9f9a57f3/go.mod h1:mL1N/T3taQHkDXs73rZJwtUhF3w3ftmwwsq0BUmARs4=
golang.org/x/net v0.0.0-20190213061140-3a22650c66bd/go.mod h1:mL1N/T3taQHkDXs73rZJwtUhF3w3ftmwwsq0BUmARs4=
golang.org/x/net v0.0.0-20190311183353-d8887717615a/go.mod h1:t9HGtf8HONx5eT2rtn7q6eTqICYqUVnKs3thJo3Qplg=
golang.org/x/net v0.0.0-20190404232315-eb5bcb51f2a3/go.mod h1:t9HGtf8HONx5eT2rtn7q6eTqICYqUVnKs3thJo3Qplg=
golang.org/x/net v0.0.0-20190424112056-4829fb13d2c6/go.mod h1:t9HGtf8HONx5eT2rtn7q6eTqICYqUVnKs3thJo3Qplg=
pkg/process: Now that we are trying to identify the root cause of the satellite load limitations (i.e. currently the satellite has a max ability of 400 rps for uploads and we need this to be higher), we are using the golang diagnostic tools to collect insight into what the bottlenecks are. We currently have a debug endpoint to gather some cpu and mem data, but it could be useful to have continuous profiling. GCP stackdriver has support for continuous profiling so lets set that up and see if it is helpful to gather more data. This PR adds support for [GCP continuous profiler](https://cloud.google.com/profiler) which allows enabling continuous cpu/mem profiling and the stats are sent to stackdriver in google cloud console. To enable the continuous profiling for a storj component, do the following: - prereq: the workload must be running in GKE and have Stackdriver Profiling IAM role permissions - provide the config flag `debug.profilename` in the config.yaml file for the workload (i.e. satellite api process, etc). The profilename should be the workload name, for example "satellite-api". - once the above config flag is provided, the profiler will be initialized and profiling stats will automatically be sent to GCP project where the workload is running and viewable in the Stackdriver Profile page in the console The current implementation assumes the workload is running in GKE, however if we find if useful we can add support to enable this from anywhere. But for simplicity, its configured this way assuming the main goal is to enable in production systems. Change-Id: Ibf8ebe2df7bf06fdd4951ee6a1e48854dd36ad47
2020-02-25 16:46:12 +00:00
golang.org/x/net v0.0.0-20190501004415-9ce7a6920f09/go.mod h1:t9HGtf8HONx5eT2rtn7q6eTqICYqUVnKs3thJo3Qplg=
golang.org/x/net v0.0.0-20190503192946-f4e77d36d62c/go.mod h1:t9HGtf8HONx5eT2rtn7q6eTqICYqUVnKs3thJo3Qplg=
golang.org/x/net v0.0.0-20190522155817-f3200d17e092/go.mod h1:HSz+uSET+XFnRR8LxR5pz3Of3rY3CfYBVs4xY44aLks=
pkg/process: Now that we are trying to identify the root cause of the satellite load limitations (i.e. currently the satellite has a max ability of 400 rps for uploads and we need this to be higher), we are using the golang diagnostic tools to collect insight into what the bottlenecks are. We currently have a debug endpoint to gather some cpu and mem data, but it could be useful to have continuous profiling. GCP stackdriver has support for continuous profiling so lets set that up and see if it is helpful to gather more data. This PR adds support for [GCP continuous profiler](https://cloud.google.com/profiler) which allows enabling continuous cpu/mem profiling and the stats are sent to stackdriver in google cloud console. To enable the continuous profiling for a storj component, do the following: - prereq: the workload must be running in GKE and have Stackdriver Profiling IAM role permissions - provide the config flag `debug.profilename` in the config.yaml file for the workload (i.e. satellite api process, etc). The profilename should be the workload name, for example "satellite-api". - once the above config flag is provided, the profiler will be initialized and profiling stats will automatically be sent to GCP project where the workload is running and viewable in the Stackdriver Profile page in the console The current implementation assumes the workload is running in GKE, however if we find if useful we can add support to enable this from anywhere. But for simplicity, its configured this way assuming the main goal is to enable in production systems. Change-Id: Ibf8ebe2df7bf06fdd4951ee6a1e48854dd36ad47
2020-02-25 16:46:12 +00:00
golang.org/x/net v0.0.0-20190603091049-60506f45cf65/go.mod h1:HSz+uSET+XFnRR8LxR5pz3Of3rY3CfYBVs4xY44aLks=
golang.org/x/net v0.0.0-20190620200207-3b0461eec859/go.mod h1:z5CRVTTTmAJ677TzLLGU+0bjPO0LkuOLi4/5GtJWs/s=
golang.org/x/net v0.0.0-20190813141303-74dc4d7220e7/go.mod h1:z5CRVTTTmAJ677TzLLGU+0bjPO0LkuOLi4/5GtJWs/s=
golang.org/x/net v0.0.0-20190923162816-aa69164e4478/go.mod h1:z5CRVTTTmAJ677TzLLGU+0bjPO0LkuOLi4/5GtJWs/s=
pkg/process: Now that we are trying to identify the root cause of the satellite load limitations (i.e. currently the satellite has a max ability of 400 rps for uploads and we need this to be higher), we are using the golang diagnostic tools to collect insight into what the bottlenecks are. We currently have a debug endpoint to gather some cpu and mem data, but it could be useful to have continuous profiling. GCP stackdriver has support for continuous profiling so lets set that up and see if it is helpful to gather more data. This PR adds support for [GCP continuous profiler](https://cloud.google.com/profiler) which allows enabling continuous cpu/mem profiling and the stats are sent to stackdriver in google cloud console. To enable the continuous profiling for a storj component, do the following: - prereq: the workload must be running in GKE and have Stackdriver Profiling IAM role permissions - provide the config flag `debug.profilename` in the config.yaml file for the workload (i.e. satellite api process, etc). The profilename should be the workload name, for example "satellite-api". - once the above config flag is provided, the profiler will be initialized and profiling stats will automatically be sent to GCP project where the workload is running and viewable in the Stackdriver Profile page in the console The current implementation assumes the workload is running in GKE, however if we find if useful we can add support to enable this from anywhere. But for simplicity, its configured this way assuming the main goal is to enable in production systems. Change-Id: Ibf8ebe2df7bf06fdd4951ee6a1e48854dd36ad47
2020-02-25 16:46:12 +00:00
golang.org/x/net v0.0.0-20200114155413-6afb5195e5aa/go.mod h1:z5CRVTTTmAJ677TzLLGU+0bjPO0LkuOLi4/5GtJWs/s=
golang.org/x/net v0.0.0-20200226121028-0de0cce0169b/go.mod h1:z5CRVTTTmAJ677TzLLGU+0bjPO0LkuOLi4/5GtJWs/s=
golang.org/x/net v0.0.0-20200324143707-d3edc9973b7e h1:3G+cUijn7XD+S4eJFddp53Pv7+slrESplyjG25HgL+k=
golang.org/x/net v0.0.0-20200324143707-d3edc9973b7e/go.mod h1:qpuaurCH72eLCgpAm/N6yyVIVM9cpaDIP3A8BGJEC5A=
2018-10-06 18:57:53 +01:00
golang.org/x/oauth2 v0.0.0-20180821212333-d2e6202438be/go.mod h1:N/0e6XlmueqKjAGxoOufVs8QHGRruUQn6yWY3a++T0U=
golang.org/x/oauth2 v0.0.0-20181106182150-f42d05182288/go.mod h1:N/0e6XlmueqKjAGxoOufVs8QHGRruUQn6yWY3a++T0U=
golang.org/x/oauth2 v0.0.0-20190226205417-e64efc72b421/go.mod h1:gOpvHmFTYa4IltrdGE7lF6nIHvwfUNPOp7c8zoXwtLw=
golang.org/x/oauth2 v0.0.0-20190402181905-9f3314589c9a/go.mod h1:gOpvHmFTYa4IltrdGE7lF6nIHvwfUNPOp7c8zoXwtLw=
pkg/process: Now that we are trying to identify the root cause of the satellite load limitations (i.e. currently the satellite has a max ability of 400 rps for uploads and we need this to be higher), we are using the golang diagnostic tools to collect insight into what the bottlenecks are. We currently have a debug endpoint to gather some cpu and mem data, but it could be useful to have continuous profiling. GCP stackdriver has support for continuous profiling so lets set that up and see if it is helpful to gather more data. This PR adds support for [GCP continuous profiler](https://cloud.google.com/profiler) which allows enabling continuous cpu/mem profiling and the stats are sent to stackdriver in google cloud console. To enable the continuous profiling for a storj component, do the following: - prereq: the workload must be running in GKE and have Stackdriver Profiling IAM role permissions - provide the config flag `debug.profilename` in the config.yaml file for the workload (i.e. satellite api process, etc). The profilename should be the workload name, for example "satellite-api". - once the above config flag is provided, the profiler will be initialized and profiling stats will automatically be sent to GCP project where the workload is running and viewable in the Stackdriver Profile page in the console The current implementation assumes the workload is running in GKE, however if we find if useful we can add support to enable this from anywhere. But for simplicity, its configured this way assuming the main goal is to enable in production systems. Change-Id: Ibf8ebe2df7bf06fdd4951ee6a1e48854dd36ad47
2020-02-25 16:46:12 +00:00
golang.org/x/oauth2 v0.0.0-20190604053449-0f29369cfe45/go.mod h1:gOpvHmFTYa4IltrdGE7lF6nIHvwfUNPOp7c8zoXwtLw=
golang.org/x/oauth2 v0.0.0-20200107190931-bf48bf16ab8d h1:TzXSXBo42m9gQenoE3b9BGiEpg5IG2JkU5FkPIawgtw=
golang.org/x/oauth2 v0.0.0-20200107190931-bf48bf16ab8d/go.mod h1:gOpvHmFTYa4IltrdGE7lF6nIHvwfUNPOp7c8zoXwtLw=
2018-10-06 18:57:53 +01:00
golang.org/x/sync v0.0.0-20180314180146-1d60e4601c6f/go.mod h1:RxMgew5VJxzue5/jJTE5uejpjVlOe/izrB70Jof72aM=
golang.org/x/sync v0.0.0-20181108010431-42b317875d0f/go.mod h1:RxMgew5VJxzue5/jJTE5uejpjVlOe/izrB70Jof72aM=
golang.org/x/sync v0.0.0-20181221193216-37e7f081c4d4/go.mod h1:RxMgew5VJxzue5/jJTE5uejpjVlOe/izrB70Jof72aM=
golang.org/x/sync v0.0.0-20190227155943-e225da77a7e6/go.mod h1:RxMgew5VJxzue5/jJTE5uejpjVlOe/izrB70Jof72aM=
golang.org/x/sync v0.0.0-20190423024810-112230192c58/go.mod h1:RxMgew5VJxzue5/jJTE5uejpjVlOe/izrB70Jof72aM=
golang.org/x/sync v0.0.0-20190911185100-cd5d95a43a6e/go.mod h1:RxMgew5VJxzue5/jJTE5uejpjVlOe/izrB70Jof72aM=
golang.org/x/sync v0.0.0-20200317015054-43a5402ce75a h1:WXEvlFVvvGxCJLG6REjsT03iWnKLEWinaScsxF2Vm2o=
golang.org/x/sync v0.0.0-20200317015054-43a5402ce75a/go.mod h1:RxMgew5VJxzue5/jJTE5uejpjVlOe/izrB70Jof72aM=
golang.org/x/sync v0.0.0-20200625203802-6e8e738ad208 h1:qwRHBd0NqMbJxfbotnDhm2ByMI1Shq4Y6oRJo21SGJA=
golang.org/x/sync v0.0.0-20200625203802-6e8e738ad208/go.mod h1:RxMgew5VJxzue5/jJTE5uejpjVlOe/izrB70Jof72aM=
golang.org/x/sys v0.0.0-20180830151530-49385e6e1522/go.mod h1:STP8DvDyc/dI5b8T5hshtkjS+E42TnysNCUPdjciGhY=
golang.org/x/sys v0.0.0-20180905080454-ebe1bf3edb33/go.mod h1:STP8DvDyc/dI5b8T5hshtkjS+E42TnysNCUPdjciGhY=
2018-10-06 18:57:53 +01:00
golang.org/x/sys v0.0.0-20180909124046-d0be0721c37e/go.mod h1:STP8DvDyc/dI5b8T5hshtkjS+E42TnysNCUPdjciGhY=
golang.org/x/sys v0.0.0-20181107165924-66b7b1311ac8/go.mod h1:STP8DvDyc/dI5b8T5hshtkjS+E42TnysNCUPdjciGhY=
golang.org/x/sys v0.0.0-20181116152217-5ac8a444bdc5/go.mod h1:STP8DvDyc/dI5b8T5hshtkjS+E42TnysNCUPdjciGhY=
golang.org/x/sys v0.0.0-20181122145206-62eef0e2fa9b/go.mod h1:STP8DvDyc/dI5b8T5hshtkjS+E42TnysNCUPdjciGhY=
golang.org/x/sys v0.0.0-20190102155601-82a175fd1598/go.mod h1:STP8DvDyc/dI5b8T5hshtkjS+E42TnysNCUPdjciGhY=
golang.org/x/sys v0.0.0-20190204203706-41f3e6584952/go.mod h1:STP8DvDyc/dI5b8T5hshtkjS+E42TnysNCUPdjciGhY=
golang.org/x/sys v0.0.0-20190215142949-d0b11bdaac8a/go.mod h1:STP8DvDyc/dI5b8T5hshtkjS+E42TnysNCUPdjciGhY=
golang.org/x/sys v0.0.0-20190222072716-a9d3bda3a223/go.mod h1:STP8DvDyc/dI5b8T5hshtkjS+E42TnysNCUPdjciGhY=
pkg/process: Now that we are trying to identify the root cause of the satellite load limitations (i.e. currently the satellite has a max ability of 400 rps for uploads and we need this to be higher), we are using the golang diagnostic tools to collect insight into what the bottlenecks are. We currently have a debug endpoint to gather some cpu and mem data, but it could be useful to have continuous profiling. GCP stackdriver has support for continuous profiling so lets set that up and see if it is helpful to gather more data. This PR adds support for [GCP continuous profiler](https://cloud.google.com/profiler) which allows enabling continuous cpu/mem profiling and the stats are sent to stackdriver in google cloud console. To enable the continuous profiling for a storj component, do the following: - prereq: the workload must be running in GKE and have Stackdriver Profiling IAM role permissions - provide the config flag `debug.profilename` in the config.yaml file for the workload (i.e. satellite api process, etc). The profilename should be the workload name, for example "satellite-api". - once the above config flag is provided, the profiler will be initialized and profiling stats will automatically be sent to GCP project where the workload is running and viewable in the Stackdriver Profile page in the console The current implementation assumes the workload is running in GKE, however if we find if useful we can add support to enable this from anywhere. But for simplicity, its configured this way assuming the main goal is to enable in production systems. Change-Id: Ibf8ebe2df7bf06fdd4951ee6a1e48854dd36ad47
2020-02-25 16:46:12 +00:00
golang.org/x/sys v0.0.0-20190312061237-fead79001313/go.mod h1:h1NjWce9XRLGQEsW7wpKNCjG9DtNlClVuFLEZdDNbEs=
golang.org/x/sys v0.0.0-20190403152447-81d4e9dc473e/go.mod h1:h1NjWce9XRLGQEsW7wpKNCjG9DtNlClVuFLEZdDNbEs=
golang.org/x/sys v0.0.0-20190412213103-97732733099d/go.mod h1:h1NjWce9XRLGQEsW7wpKNCjG9DtNlClVuFLEZdDNbEs=
golang.org/x/sys v0.0.0-20190422165155-953cdadca894/go.mod h1:h1NjWce9XRLGQEsW7wpKNCjG9DtNlClVuFLEZdDNbEs=
golang.org/x/sys v0.0.0-20190426135247-a129542de9ae/go.mod h1:h1NjWce9XRLGQEsW7wpKNCjG9DtNlClVuFLEZdDNbEs=
pkg/process: Now that we are trying to identify the root cause of the satellite load limitations (i.e. currently the satellite has a max ability of 400 rps for uploads and we need this to be higher), we are using the golang diagnostic tools to collect insight into what the bottlenecks are. We currently have a debug endpoint to gather some cpu and mem data, but it could be useful to have continuous profiling. GCP stackdriver has support for continuous profiling so lets set that up and see if it is helpful to gather more data. This PR adds support for [GCP continuous profiler](https://cloud.google.com/profiler) which allows enabling continuous cpu/mem profiling and the stats are sent to stackdriver in google cloud console. To enable the continuous profiling for a storj component, do the following: - prereq: the workload must be running in GKE and have Stackdriver Profiling IAM role permissions - provide the config flag `debug.profilename` in the config.yaml file for the workload (i.e. satellite api process, etc). The profilename should be the workload name, for example "satellite-api". - once the above config flag is provided, the profiler will be initialized and profiling stats will automatically be sent to GCP project where the workload is running and viewable in the Stackdriver Profile page in the console The current implementation assumes the workload is running in GKE, however if we find if useful we can add support to enable this from anywhere. But for simplicity, its configured this way assuming the main goal is to enable in production systems. Change-Id: Ibf8ebe2df7bf06fdd4951ee6a1e48854dd36ad47
2020-02-25 16:46:12 +00:00
golang.org/x/sys v0.0.0-20190502145724-3ef323f4f1fd/go.mod h1:h1NjWce9XRLGQEsW7wpKNCjG9DtNlClVuFLEZdDNbEs=
golang.org/x/sys v0.0.0-20190507160741-ecd444e8653b/go.mod h1:h1NjWce9XRLGQEsW7wpKNCjG9DtNlClVuFLEZdDNbEs=
golang.org/x/sys v0.0.0-20190606165138-5da285871e9c/go.mod h1:h1NjWce9XRLGQEsW7wpKNCjG9DtNlClVuFLEZdDNbEs=
golang.org/x/sys v0.0.0-20190624142023-c5567b49c5d0/go.mod h1:h1NjWce9XRLGQEsW7wpKNCjG9DtNlClVuFLEZdDNbEs=
golang.org/x/sys v0.0.0-20190813064441-fde4db37ae7a/go.mod h1:h1NjWce9XRLGQEsW7wpKNCjG9DtNlClVuFLEZdDNbEs=
golang.org/x/sys v0.0.0-20190826190057-c7b8b68b1456/go.mod h1:h1NjWce9XRLGQEsW7wpKNCjG9DtNlClVuFLEZdDNbEs=
pkg/process: Now that we are trying to identify the root cause of the satellite load limitations (i.e. currently the satellite has a max ability of 400 rps for uploads and we need this to be higher), we are using the golang diagnostic tools to collect insight into what the bottlenecks are. We currently have a debug endpoint to gather some cpu and mem data, but it could be useful to have continuous profiling. GCP stackdriver has support for continuous profiling so lets set that up and see if it is helpful to gather more data. This PR adds support for [GCP continuous profiler](https://cloud.google.com/profiler) which allows enabling continuous cpu/mem profiling and the stats are sent to stackdriver in google cloud console. To enable the continuous profiling for a storj component, do the following: - prereq: the workload must be running in GKE and have Stackdriver Profiling IAM role permissions - provide the config flag `debug.profilename` in the config.yaml file for the workload (i.e. satellite api process, etc). The profilename should be the workload name, for example "satellite-api". - once the above config flag is provided, the profiler will be initialized and profiling stats will automatically be sent to GCP project where the workload is running and viewable in the Stackdriver Profile page in the console The current implementation assumes the workload is running in GKE, however if we find if useful we can add support to enable this from anywhere. But for simplicity, its configured this way assuming the main goal is to enable in production systems. Change-Id: Ibf8ebe2df7bf06fdd4951ee6a1e48854dd36ad47
2020-02-25 16:46:12 +00:00
golang.org/x/sys v0.0.0-20191204072324-ce4227a45e2e/go.mod h1:h1NjWce9XRLGQEsW7wpKNCjG9DtNlClVuFLEZdDNbEs=
golang.org/x/sys v0.0.0-20191210023423-ac6580df4449/go.mod h1:h1NjWce9XRLGQEsW7wpKNCjG9DtNlClVuFLEZdDNbEs=
golang.org/x/sys v0.0.0-20200107144601-ef85f5a75ddf/go.mod h1:h1NjWce9XRLGQEsW7wpKNCjG9DtNlClVuFLEZdDNbEs=
pkg/process: Now that we are trying to identify the root cause of the satellite load limitations (i.e. currently the satellite has a max ability of 400 rps for uploads and we need this to be higher), we are using the golang diagnostic tools to collect insight into what the bottlenecks are. We currently have a debug endpoint to gather some cpu and mem data, but it could be useful to have continuous profiling. GCP stackdriver has support for continuous profiling so lets set that up and see if it is helpful to gather more data. This PR adds support for [GCP continuous profiler](https://cloud.google.com/profiler) which allows enabling continuous cpu/mem profiling and the stats are sent to stackdriver in google cloud console. To enable the continuous profiling for a storj component, do the following: - prereq: the workload must be running in GKE and have Stackdriver Profiling IAM role permissions - provide the config flag `debug.profilename` in the config.yaml file for the workload (i.e. satellite api process, etc). The profilename should be the workload name, for example "satellite-api". - once the above config flag is provided, the profiler will be initialized and profiling stats will automatically be sent to GCP project where the workload is running and viewable in the Stackdriver Profile page in the console The current implementation assumes the workload is running in GKE, however if we find if useful we can add support to enable this from anywhere. But for simplicity, its configured this way assuming the main goal is to enable in production systems. Change-Id: Ibf8ebe2df7bf06fdd4951ee6a1e48854dd36ad47
2020-02-25 16:46:12 +00:00
golang.org/x/sys v0.0.0-20200113162924-86b910548bc1/go.mod h1:h1NjWce9XRLGQEsW7wpKNCjG9DtNlClVuFLEZdDNbEs=
golang.org/x/sys v0.0.0-20200116001909-b77594299b42/go.mod h1:h1NjWce9XRLGQEsW7wpKNCjG9DtNlClVuFLEZdDNbEs=
golang.org/x/sys v0.0.0-20200202164722-d101bd2416d5/go.mod h1:h1NjWce9XRLGQEsW7wpKNCjG9DtNlClVuFLEZdDNbEs=
golang.org/x/sys v0.0.0-20200223170610-d5e6a3e2c0ae/go.mod h1:h1NjWce9XRLGQEsW7wpKNCjG9DtNlClVuFLEZdDNbEs=
golang.org/x/sys v0.0.0-20200323222414-85ca7c5b95cd/go.mod h1:h1NjWce9XRLGQEsW7wpKNCjG9DtNlClVuFLEZdDNbEs=
golang.org/x/sys v0.0.0-20200610111108-226ff32320da h1:bGb80FudwxpeucJUjPYJXuJ8Hk91vNtfvrymzwiei38=
golang.org/x/sys v0.0.0-20200610111108-226ff32320da/go.mod h1:h1NjWce9XRLGQEsW7wpKNCjG9DtNlClVuFLEZdDNbEs=
golang.org/x/sys v0.0.0-20200808120158-1030fc2bf1d9 h1:yi1hN8dcqI9l8klZfy4B8mJvFmmAxJEePIQQFNSd7Cs=
golang.org/x/sys v0.0.0-20200808120158-1030fc2bf1d9/go.mod h1:h1NjWce9XRLGQEsW7wpKNCjG9DtNlClVuFLEZdDNbEs=
2018-10-06 18:57:53 +01:00
golang.org/x/text v0.3.0/go.mod h1:NqM8EUOU14njkJ3fqMW+pc6Ldnwhi/IjpwHt7yyuwOQ=
golang.org/x/text v0.3.1-0.20180807135948-17ff2d5776d2/go.mod h1:NqM8EUOU14njkJ3fqMW+pc6Ldnwhi/IjpwHt7yyuwOQ=
golang.org/x/text v0.3.2 h1:tW2bmiBqwgJj/UpqtC8EpXEZVYOwU0yG4iWbprSVAcs=
golang.org/x/text v0.3.2/go.mod h1:bEr9sfX3Q8Zfm5fL9x+3itogRgK3+ptLWKqgva+5dAk=
golang.org/x/text v0.3.3 h1:cokOdA+Jmi5PJGXLlLllQSgYigAEfHXJAERHVMaCc2k=
golang.org/x/text v0.3.3/go.mod h1:5Zoc/QRtKVWzQhOtBMvqHzDpF6irO9z98xDceosuGiQ=
golang.org/x/time v0.0.0-20181108054448-85acf8d2951c/go.mod h1:tRJNPiyCQ0inRvYxbN9jk5I+vvW/OXSQhTDSoE431IQ=
golang.org/x/time v0.0.0-20190308202827-9d24e82272b4/go.mod h1:tRJNPiyCQ0inRvYxbN9jk5I+vvW/OXSQhTDSoE431IQ=
golang.org/x/time v0.0.0-20191024005414-555d28b269f0 h1:/5xXl8Y5W96D+TtHSlonuFqGHIWVuyCkGJLwGh9JJFs=
golang.org/x/time v0.0.0-20191024005414-555d28b269f0/go.mod h1:tRJNPiyCQ0inRvYxbN9jk5I+vvW/OXSQhTDSoE431IQ=
golang.org/x/tools v0.0.0-20180221164845-07fd8470d635/go.mod h1:n7NCudcB/nEzxVGmLbDWY5pfWTLqBcC2KZ6jyYvM4mQ=
golang.org/x/tools v0.0.0-20180828015842-6cd1fcedba52/go.mod h1:n7NCudcB/nEzxVGmLbDWY5pfWTLqBcC2KZ6jyYvM4mQ=
golang.org/x/tools v0.0.0-20180917221912-90fa682c2a6e/go.mod h1:n7NCudcB/nEzxVGmLbDWY5pfWTLqBcC2KZ6jyYvM4mQ=
golang.org/x/tools v0.0.0-20181030221726-6c7e314b6563/go.mod h1:n7NCudcB/nEzxVGmLbDWY5pfWTLqBcC2KZ6jyYvM4mQ=
golang.org/x/tools v0.0.0-20190114222345-bf090417da8b/go.mod h1:n7NCudcB/nEzxVGmLbDWY5pfWTLqBcC2KZ6jyYvM4mQ=
golang.org/x/tools v0.0.0-20190226205152-f727befe758c/go.mod h1:9Yl7xja0Znq3iFh3HoIrodX9oNMXvdceNzlUR8zjMvY=
golang.org/x/tools v0.0.0-20190311212946-11955173bddd/go.mod h1:LCzVGOaR6xXOjkQ3onu1FJEFr0SW1gC7cKk1uF8kGRs=
pkg/process: Now that we are trying to identify the root cause of the satellite load limitations (i.e. currently the satellite has a max ability of 400 rps for uploads and we need this to be higher), we are using the golang diagnostic tools to collect insight into what the bottlenecks are. We currently have a debug endpoint to gather some cpu and mem data, but it could be useful to have continuous profiling. GCP stackdriver has support for continuous profiling so lets set that up and see if it is helpful to gather more data. This PR adds support for [GCP continuous profiler](https://cloud.google.com/profiler) which allows enabling continuous cpu/mem profiling and the stats are sent to stackdriver in google cloud console. To enable the continuous profiling for a storj component, do the following: - prereq: the workload must be running in GKE and have Stackdriver Profiling IAM role permissions - provide the config flag `debug.profilename` in the config.yaml file for the workload (i.e. satellite api process, etc). The profilename should be the workload name, for example "satellite-api". - once the above config flag is provided, the profiler will be initialized and profiling stats will automatically be sent to GCP project where the workload is running and viewable in the Stackdriver Profile page in the console The current implementation assumes the workload is running in GKE, however if we find if useful we can add support to enable this from anywhere. But for simplicity, its configured this way assuming the main goal is to enable in production systems. Change-Id: Ibf8ebe2df7bf06fdd4951ee6a1e48854dd36ad47
2020-02-25 16:46:12 +00:00
golang.org/x/tools v0.0.0-20190312151545-0bb0c0a6e846/go.mod h1:LCzVGOaR6xXOjkQ3onu1FJEFr0SW1gC7cKk1uF8kGRs=
golang.org/x/tools v0.0.0-20190312170243-e65039ee4138/go.mod h1:LCzVGOaR6xXOjkQ3onu1FJEFr0SW1gC7cKk1uF8kGRs=
pkg/process: Now that we are trying to identify the root cause of the satellite load limitations (i.e. currently the satellite has a max ability of 400 rps for uploads and we need this to be higher), we are using the golang diagnostic tools to collect insight into what the bottlenecks are. We currently have a debug endpoint to gather some cpu and mem data, but it could be useful to have continuous profiling. GCP stackdriver has support for continuous profiling so lets set that up and see if it is helpful to gather more data. This PR adds support for [GCP continuous profiler](https://cloud.google.com/profiler) which allows enabling continuous cpu/mem profiling and the stats are sent to stackdriver in google cloud console. To enable the continuous profiling for a storj component, do the following: - prereq: the workload must be running in GKE and have Stackdriver Profiling IAM role permissions - provide the config flag `debug.profilename` in the config.yaml file for the workload (i.e. satellite api process, etc). The profilename should be the workload name, for example "satellite-api". - once the above config flag is provided, the profiler will be initialized and profiling stats will automatically be sent to GCP project where the workload is running and viewable in the Stackdriver Profile page in the console The current implementation assumes the workload is running in GKE, however if we find if useful we can add support to enable this from anywhere. But for simplicity, its configured this way assuming the main goal is to enable in production systems. Change-Id: Ibf8ebe2df7bf06fdd4951ee6a1e48854dd36ad47
2020-02-25 16:46:12 +00:00
golang.org/x/tools v0.0.0-20190425150028-36563e24a262/go.mod h1:RgjU9mgBXZiqYHBnxXauZ1Gv1EHHAz9KjViQ78xBX0Q=
golang.org/x/tools v0.0.0-20190425163242-31fd60d6bfdc/go.mod h1:RgjU9mgBXZiqYHBnxXauZ1Gv1EHHAz9KjViQ78xBX0Q=
golang.org/x/tools v0.0.0-20190425222832-ad9eeb80039a/go.mod h1:RgjU9mgBXZiqYHBnxXauZ1Gv1EHHAz9KjViQ78xBX0Q=
pkg/process: Now that we are trying to identify the root cause of the satellite load limitations (i.e. currently the satellite has a max ability of 400 rps for uploads and we need this to be higher), we are using the golang diagnostic tools to collect insight into what the bottlenecks are. We currently have a debug endpoint to gather some cpu and mem data, but it could be useful to have continuous profiling. GCP stackdriver has support for continuous profiling so lets set that up and see if it is helpful to gather more data. This PR adds support for [GCP continuous profiler](https://cloud.google.com/profiler) which allows enabling continuous cpu/mem profiling and the stats are sent to stackdriver in google cloud console. To enable the continuous profiling for a storj component, do the following: - prereq: the workload must be running in GKE and have Stackdriver Profiling IAM role permissions - provide the config flag `debug.profilename` in the config.yaml file for the workload (i.e. satellite api process, etc). The profilename should be the workload name, for example "satellite-api". - once the above config flag is provided, the profiler will be initialized and profiling stats will automatically be sent to GCP project where the workload is running and viewable in the Stackdriver Profile page in the console The current implementation assumes the workload is running in GKE, however if we find if useful we can add support to enable this from anywhere. But for simplicity, its configured this way assuming the main goal is to enable in production systems. Change-Id: Ibf8ebe2df7bf06fdd4951ee6a1e48854dd36ad47
2020-02-25 16:46:12 +00:00
golang.org/x/tools v0.0.0-20190506145303-2d16b83fe98c/go.mod h1:RgjU9mgBXZiqYHBnxXauZ1Gv1EHHAz9KjViQ78xBX0Q=
golang.org/x/tools v0.0.0-20190524140312-2c0ae7006135/go.mod h1:RgjU9mgBXZiqYHBnxXauZ1Gv1EHHAz9KjViQ78xBX0Q=
pkg/process: Now that we are trying to identify the root cause of the satellite load limitations (i.e. currently the satellite has a max ability of 400 rps for uploads and we need this to be higher), we are using the golang diagnostic tools to collect insight into what the bottlenecks are. We currently have a debug endpoint to gather some cpu and mem data, but it could be useful to have continuous profiling. GCP stackdriver has support for continuous profiling so lets set that up and see if it is helpful to gather more data. This PR adds support for [GCP continuous profiler](https://cloud.google.com/profiler) which allows enabling continuous cpu/mem profiling and the stats are sent to stackdriver in google cloud console. To enable the continuous profiling for a storj component, do the following: - prereq: the workload must be running in GKE and have Stackdriver Profiling IAM role permissions - provide the config flag `debug.profilename` in the config.yaml file for the workload (i.e. satellite api process, etc). The profilename should be the workload name, for example "satellite-api". - once the above config flag is provided, the profiler will be initialized and profiling stats will automatically be sent to GCP project where the workload is running and viewable in the Stackdriver Profile page in the console The current implementation assumes the workload is running in GKE, however if we find if useful we can add support to enable this from anywhere. But for simplicity, its configured this way assuming the main goal is to enable in production systems. Change-Id: Ibf8ebe2df7bf06fdd4951ee6a1e48854dd36ad47
2020-02-25 16:46:12 +00:00
golang.org/x/tools v0.0.0-20190606124116-d0a3d012864b/go.mod h1:/rFqwRUd4F7ZHNgwSSTFct+R/Kf4OFW1sUzUTQQTgfc=
golang.org/x/tools v0.0.0-20190621195816-6e04913cbbac/go.mod h1:/rFqwRUd4F7ZHNgwSSTFct+R/Kf4OFW1sUzUTQQTgfc=
golang.org/x/tools v0.0.0-20190628153133-6cdbf07be9d0/go.mod h1:/rFqwRUd4F7ZHNgwSSTFct+R/Kf4OFW1sUzUTQQTgfc=
golang.org/x/tools v0.0.0-20190816200558-6889da9d5479/go.mod h1:b+2E5dAYhXwXZwtnZ6UAqBI28+e2cm9otk0dWdXHAEo=
golang.org/x/tools v0.0.0-20190823170909-c4a336ef6a2f/go.mod h1:b+2E5dAYhXwXZwtnZ6UAqBI28+e2cm9otk0dWdXHAEo=
pkg/process: Now that we are trying to identify the root cause of the satellite load limitations (i.e. currently the satellite has a max ability of 400 rps for uploads and we need this to be higher), we are using the golang diagnostic tools to collect insight into what the bottlenecks are. We currently have a debug endpoint to gather some cpu and mem data, but it could be useful to have continuous profiling. GCP stackdriver has support for continuous profiling so lets set that up and see if it is helpful to gather more data. This PR adds support for [GCP continuous profiler](https://cloud.google.com/profiler) which allows enabling continuous cpu/mem profiling and the stats are sent to stackdriver in google cloud console. To enable the continuous profiling for a storj component, do the following: - prereq: the workload must be running in GKE and have Stackdriver Profiling IAM role permissions - provide the config flag `debug.profilename` in the config.yaml file for the workload (i.e. satellite api process, etc). The profilename should be the workload name, for example "satellite-api". - once the above config flag is provided, the profiler will be initialized and profiling stats will automatically be sent to GCP project where the workload is running and viewable in the Stackdriver Profile page in the console The current implementation assumes the workload is running in GKE, however if we find if useful we can add support to enable this from anywhere. But for simplicity, its configured this way assuming the main goal is to enable in production systems. Change-Id: Ibf8ebe2df7bf06fdd4951ee6a1e48854dd36ad47
2020-02-25 16:46:12 +00:00
golang.org/x/tools v0.0.0-20190911174233-4f2ddba30aff/go.mod h1:b+2E5dAYhXwXZwtnZ6UAqBI28+e2cm9otk0dWdXHAEo=
golang.org/x/tools v0.0.0-20191012152004-8de300cfc20a/go.mod h1:b+2E5dAYhXwXZwtnZ6UAqBI28+e2cm9otk0dWdXHAEo=
golang.org/x/tools v0.0.0-20191029041327-9cc4af7d6b2c/go.mod h1:b+2E5dAYhXwXZwtnZ6UAqBI28+e2cm9otk0dWdXHAEo=
golang.org/x/tools v0.0.0-20191029190741-b9c20aec41a5/go.mod h1:b+2E5dAYhXwXZwtnZ6UAqBI28+e2cm9otk0dWdXHAEo=
golang.org/x/tools v0.0.0-20191119224855-298f0cb1881e/go.mod h1:b+2E5dAYhXwXZwtnZ6UAqBI28+e2cm9otk0dWdXHAEo=
pkg/process: Now that we are trying to identify the root cause of the satellite load limitations (i.e. currently the satellite has a max ability of 400 rps for uploads and we need this to be higher), we are using the golang diagnostic tools to collect insight into what the bottlenecks are. We currently have a debug endpoint to gather some cpu and mem data, but it could be useful to have continuous profiling. GCP stackdriver has support for continuous profiling so lets set that up and see if it is helpful to gather more data. This PR adds support for [GCP continuous profiler](https://cloud.google.com/profiler) which allows enabling continuous cpu/mem profiling and the stats are sent to stackdriver in google cloud console. To enable the continuous profiling for a storj component, do the following: - prereq: the workload must be running in GKE and have Stackdriver Profiling IAM role permissions - provide the config flag `debug.profilename` in the config.yaml file for the workload (i.e. satellite api process, etc). The profilename should be the workload name, for example "satellite-api". - once the above config flag is provided, the profiler will be initialized and profiling stats will automatically be sent to GCP project where the workload is running and viewable in the Stackdriver Profile page in the console The current implementation assumes the workload is running in GKE, however if we find if useful we can add support to enable this from anywhere. But for simplicity, its configured this way assuming the main goal is to enable in production systems. Change-Id: Ibf8ebe2df7bf06fdd4951ee6a1e48854dd36ad47
2020-02-25 16:46:12 +00:00
golang.org/x/tools v0.0.0-20191125144606-a911d9008d1f/go.mod h1:b+2E5dAYhXwXZwtnZ6UAqBI28+e2cm9otk0dWdXHAEo=
golang.org/x/tools v0.0.0-20200117161641-43d50277825c/go.mod h1:TB2adYChydJhpapKDTa4BR/hXlZSLoq2Wpct/0txZ28=
golang.org/x/tools v0.0.0-20200428211428-0c9eba77bc32 h1:Xvf3ZQTm5bjXPxhI7g+dwqsCqadK1rcNtwtszuatetk=
golang.org/x/tools v0.0.0-20200428211428-0c9eba77bc32/go.mod h1:EkVYQZoAsY45+roYkvgYkIh4xh/qjgUK9TdY2XT94GE=
golang.org/x/xerrors v0.0.0-20190410155217-1f06c39b4373/go.mod h1:I/5z698sn9Ka8TeJc9MKroUUfqBBauWjQqLJ2OPfmY0=
golang.org/x/xerrors v0.0.0-20190513163551-3ee3066db522/go.mod h1:I/5z698sn9Ka8TeJc9MKroUUfqBBauWjQqLJ2OPfmY0=
pkg/process: Now that we are trying to identify the root cause of the satellite load limitations (i.e. currently the satellite has a max ability of 400 rps for uploads and we need this to be higher), we are using the golang diagnostic tools to collect insight into what the bottlenecks are. We currently have a debug endpoint to gather some cpu and mem data, but it could be useful to have continuous profiling. GCP stackdriver has support for continuous profiling so lets set that up and see if it is helpful to gather more data. This PR adds support for [GCP continuous profiler](https://cloud.google.com/profiler) which allows enabling continuous cpu/mem profiling and the stats are sent to stackdriver in google cloud console. To enable the continuous profiling for a storj component, do the following: - prereq: the workload must be running in GKE and have Stackdriver Profiling IAM role permissions - provide the config flag `debug.profilename` in the config.yaml file for the workload (i.e. satellite api process, etc). The profilename should be the workload name, for example "satellite-api". - once the above config flag is provided, the profiler will be initialized and profiling stats will automatically be sent to GCP project where the workload is running and viewable in the Stackdriver Profile page in the console The current implementation assumes the workload is running in GKE, however if we find if useful we can add support to enable this from anywhere. But for simplicity, its configured this way assuming the main goal is to enable in production systems. Change-Id: Ibf8ebe2df7bf06fdd4951ee6a1e48854dd36ad47
2020-02-25 16:46:12 +00:00
golang.org/x/xerrors v0.0.0-20190717185122-a985d3407aa7/go.mod h1:I/5z698sn9Ka8TeJc9MKroUUfqBBauWjQqLJ2OPfmY0=
golang.org/x/xerrors v0.0.0-20191011141410-1b5146add898/go.mod h1:I/5z698sn9Ka8TeJc9MKroUUfqBBauWjQqLJ2OPfmY0=
golang.org/x/xerrors v0.0.0-20191204190536-9bdfabe68543 h1:E7g+9GITq07hpfrRu66IVDexMakfv52eLZ2CXBWiKr4=
golang.org/x/xerrors v0.0.0-20191204190536-9bdfabe68543/go.mod h1:I/5z698sn9Ka8TeJc9MKroUUfqBBauWjQqLJ2OPfmY0=
google.golang.org/api v0.3.1/go.mod h1:6wY9I6uQWHQ8EM57III9mq/AjF+i8G65rmVagqKMtkk=
google.golang.org/api v0.3.2/go.mod h1:6wY9I6uQWHQ8EM57III9mq/AjF+i8G65rmVagqKMtkk=
google.golang.org/api v0.4.0/go.mod h1:8k5glujaEP+g9n7WNsDg8QP6cUVNI86fCNMcbazEtwE=
pkg/process: Now that we are trying to identify the root cause of the satellite load limitations (i.e. currently the satellite has a max ability of 400 rps for uploads and we need this to be higher), we are using the golang diagnostic tools to collect insight into what the bottlenecks are. We currently have a debug endpoint to gather some cpu and mem data, but it could be useful to have continuous profiling. GCP stackdriver has support for continuous profiling so lets set that up and see if it is helpful to gather more data. This PR adds support for [GCP continuous profiler](https://cloud.google.com/profiler) which allows enabling continuous cpu/mem profiling and the stats are sent to stackdriver in google cloud console. To enable the continuous profiling for a storj component, do the following: - prereq: the workload must be running in GKE and have Stackdriver Profiling IAM role permissions - provide the config flag `debug.profilename` in the config.yaml file for the workload (i.e. satellite api process, etc). The profilename should be the workload name, for example "satellite-api". - once the above config flag is provided, the profiler will be initialized and profiling stats will automatically be sent to GCP project where the workload is running and viewable in the Stackdriver Profile page in the console The current implementation assumes the workload is running in GKE, however if we find if useful we can add support to enable this from anywhere. But for simplicity, its configured this way assuming the main goal is to enable in production systems. Change-Id: Ibf8ebe2df7bf06fdd4951ee6a1e48854dd36ad47
2020-02-25 16:46:12 +00:00
google.golang.org/api v0.7.0/go.mod h1:WtwebWUNSVBH/HAw79HIFXZNqEvBhG+Ra+ax0hx3E3M=
google.golang.org/api v0.8.0/go.mod h1:o4eAsZoiT+ibD93RtjEohWalFOjRDx6CVaqeizhEnKg=
google.golang.org/api v0.9.0/go.mod h1:o4eAsZoiT+ibD93RtjEohWalFOjRDx6CVaqeizhEnKg=
google.golang.org/api v0.15.0/go.mod h1:iLdEw5Ide6rF15KTC1Kkl0iskquN2gFfn9o9XIsbkAI=
google.golang.org/api v0.20.0 h1:jz2KixHX7EcCPiQrySzPdnYT7DbINAypCqKZ1Z7GM40=
google.golang.org/api v0.20.0/go.mod h1:BwFmGc8tA3vsd7r/7kR8DY7iEEGSU04BFxCo5jP/sfE=
google.golang.org/appengine v1.1.0/go.mod h1:EbEs0AVv82hx2wNQdGPgUI5lhzA/G0D9YwlJXL52JkM=
google.golang.org/appengine v1.3.0/go.mod h1:xpcJRLb0r/rnEns0DIKYYv+WjYCduHsrkT7/EB5XEv4=
google.golang.org/appengine v1.4.0/go.mod h1:xpcJRLb0r/rnEns0DIKYYv+WjYCduHsrkT7/EB5XEv4=
google.golang.org/appengine v1.5.0/go.mod h1:xpcJRLb0r/rnEns0DIKYYv+WjYCduHsrkT7/EB5XEv4=
pkg/process: Now that we are trying to identify the root cause of the satellite load limitations (i.e. currently the satellite has a max ability of 400 rps for uploads and we need this to be higher), we are using the golang diagnostic tools to collect insight into what the bottlenecks are. We currently have a debug endpoint to gather some cpu and mem data, but it could be useful to have continuous profiling. GCP stackdriver has support for continuous profiling so lets set that up and see if it is helpful to gather more data. This PR adds support for [GCP continuous profiler](https://cloud.google.com/profiler) which allows enabling continuous cpu/mem profiling and the stats are sent to stackdriver in google cloud console. To enable the continuous profiling for a storj component, do the following: - prereq: the workload must be running in GKE and have Stackdriver Profiling IAM role permissions - provide the config flag `debug.profilename` in the config.yaml file for the workload (i.e. satellite api process, etc). The profilename should be the workload name, for example "satellite-api". - once the above config flag is provided, the profiler will be initialized and profiling stats will automatically be sent to GCP project where the workload is running and viewable in the Stackdriver Profile page in the console The current implementation assumes the workload is running in GKE, however if we find if useful we can add support to enable this from anywhere. But for simplicity, its configured this way assuming the main goal is to enable in production systems. Change-Id: Ibf8ebe2df7bf06fdd4951ee6a1e48854dd36ad47
2020-02-25 16:46:12 +00:00
google.golang.org/appengine v1.6.1/go.mod h1:i06prIuMbXzDqacNJfV5OdTW448YApPu5ww/cMBSeb0=
google.golang.org/appengine v1.6.5 h1:tycE03LOZYQNhDpS27tcQdAzLCVMaj7QT2SXxebnpCM=
pkg/process: Now that we are trying to identify the root cause of the satellite load limitations (i.e. currently the satellite has a max ability of 400 rps for uploads and we need this to be higher), we are using the golang diagnostic tools to collect insight into what the bottlenecks are. We currently have a debug endpoint to gather some cpu and mem data, but it could be useful to have continuous profiling. GCP stackdriver has support for continuous profiling so lets set that up and see if it is helpful to gather more data. This PR adds support for [GCP continuous profiler](https://cloud.google.com/profiler) which allows enabling continuous cpu/mem profiling and the stats are sent to stackdriver in google cloud console. To enable the continuous profiling for a storj component, do the following: - prereq: the workload must be running in GKE and have Stackdriver Profiling IAM role permissions - provide the config flag `debug.profilename` in the config.yaml file for the workload (i.e. satellite api process, etc). The profilename should be the workload name, for example "satellite-api". - once the above config flag is provided, the profiler will be initialized and profiling stats will automatically be sent to GCP project where the workload is running and viewable in the Stackdriver Profile page in the console The current implementation assumes the workload is running in GKE, however if we find if useful we can add support to enable this from anywhere. But for simplicity, its configured this way assuming the main goal is to enable in production systems. Change-Id: Ibf8ebe2df7bf06fdd4951ee6a1e48854dd36ad47
2020-02-25 16:46:12 +00:00
google.golang.org/appengine v1.6.5/go.mod h1:8WjMMxjGQR8xUklV/ARdw2HLXBOI7O7uCIDZVag1xfc=
2018-10-06 18:57:53 +01:00
google.golang.org/genproto v0.0.0-20180817151627-c66870c02cf8/go.mod h1:JiN7NxoALGmiZfu7CAH4rXhgtRTLTxftemlI0sWmxmc=
google.golang.org/genproto v0.0.0-20190307195333-5fe7a883aa19/go.mod h1:VzzqZJRnGkLBvHegQrXjBqPurQTc5/KpmUdxsrq26oE=
google.golang.org/genproto v0.0.0-20190404172233-64821d5d2107/go.mod h1:VzzqZJRnGkLBvHegQrXjBqPurQTc5/KpmUdxsrq26oE=
pkg/process: Now that we are trying to identify the root cause of the satellite load limitations (i.e. currently the satellite has a max ability of 400 rps for uploads and we need this to be higher), we are using the golang diagnostic tools to collect insight into what the bottlenecks are. We currently have a debug endpoint to gather some cpu and mem data, but it could be useful to have continuous profiling. GCP stackdriver has support for continuous profiling so lets set that up and see if it is helpful to gather more data. This PR adds support for [GCP continuous profiler](https://cloud.google.com/profiler) which allows enabling continuous cpu/mem profiling and the stats are sent to stackdriver in google cloud console. To enable the continuous profiling for a storj component, do the following: - prereq: the workload must be running in GKE and have Stackdriver Profiling IAM role permissions - provide the config flag `debug.profilename` in the config.yaml file for the workload (i.e. satellite api process, etc). The profilename should be the workload name, for example "satellite-api". - once the above config flag is provided, the profiler will be initialized and profiling stats will automatically be sent to GCP project where the workload is running and viewable in the Stackdriver Profile page in the console The current implementation assumes the workload is running in GKE, however if we find if useful we can add support to enable this from anywhere. But for simplicity, its configured this way assuming the main goal is to enable in production systems. Change-Id: Ibf8ebe2df7bf06fdd4951ee6a1e48854dd36ad47
2020-02-25 16:46:12 +00:00
google.golang.org/genproto v0.0.0-20190418145605-e7d98fc518a7/go.mod h1:VzzqZJRnGkLBvHegQrXjBqPurQTc5/KpmUdxsrq26oE=
google.golang.org/genproto v0.0.0-20190425155659-357c62f0e4bb/go.mod h1:VzzqZJRnGkLBvHegQrXjBqPurQTc5/KpmUdxsrq26oE=
pkg/process: Now that we are trying to identify the root cause of the satellite load limitations (i.e. currently the satellite has a max ability of 400 rps for uploads and we need this to be higher), we are using the golang diagnostic tools to collect insight into what the bottlenecks are. We currently have a debug endpoint to gather some cpu and mem data, but it could be useful to have continuous profiling. GCP stackdriver has support for continuous profiling so lets set that up and see if it is helpful to gather more data. This PR adds support for [GCP continuous profiler](https://cloud.google.com/profiler) which allows enabling continuous cpu/mem profiling and the stats are sent to stackdriver in google cloud console. To enable the continuous profiling for a storj component, do the following: - prereq: the workload must be running in GKE and have Stackdriver Profiling IAM role permissions - provide the config flag `debug.profilename` in the config.yaml file for the workload (i.e. satellite api process, etc). The profilename should be the workload name, for example "satellite-api". - once the above config flag is provided, the profiler will be initialized and profiling stats will automatically be sent to GCP project where the workload is running and viewable in the Stackdriver Profile page in the console The current implementation assumes the workload is running in GKE, however if we find if useful we can add support to enable this from anywhere. But for simplicity, its configured this way assuming the main goal is to enable in production systems. Change-Id: Ibf8ebe2df7bf06fdd4951ee6a1e48854dd36ad47
2020-02-25 16:46:12 +00:00
google.golang.org/genproto v0.0.0-20190502173448-54afdca5d873/go.mod h1:VzzqZJRnGkLBvHegQrXjBqPurQTc5/KpmUdxsrq26oE=
google.golang.org/genproto v0.0.0-20190801165951-fa694d86fc64/go.mod h1:DMBHOl98Agz4BDEuKkezgsaosCRResVns1a3J2ZsMNc=
google.golang.org/genproto v0.0.0-20190819201941-24fa4b261c55/go.mod h1:DMBHOl98Agz4BDEuKkezgsaosCRResVns1a3J2ZsMNc=
google.golang.org/genproto v0.0.0-20190911173649-1774047e7e51/go.mod h1:IbNlFCBrqXvoKpeg0TB2l7cyZUmoaFKYIwrEpbDKLA8=
google.golang.org/genproto v0.0.0-20200115191322-ca5a22157cba h1:pRj9OXZbwNtbtZtOB4dLwfK4u+EVRMvP+e9zKkg2grM=
google.golang.org/genproto v0.0.0-20200115191322-ca5a22157cba/go.mod h1:n3cpQtvxv34hfy77yVDNjmbRyujviMdxYliBSkLhpCc=
google.golang.org/grpc v1.17.0/go.mod h1:6QZJwpn2B+Zp71q/5VxRsJ6NXXVCE5NRUHRo+f3cWCs=
google.golang.org/grpc v1.19.0/go.mod h1:mqu4LbDTu4XGKhr4mRzUsmM4RtVoemTSY81AxZiDr8c=
google.golang.org/grpc v1.20.1/go.mod h1:10oTOabMzJvdu6/UiuZezV6QK5dSlG84ov/aaiqXj38=
google.golang.org/grpc v1.21.0/go.mod h1:oYelfM1adQP15Ek0mdvEgi9Df8B9CZIaU1084ijfRaM=
google.golang.org/grpc v1.21.1/go.mod h1:oYelfM1adQP15Ek0mdvEgi9Df8B9CZIaU1084ijfRaM=
google.golang.org/grpc v1.23.0/go.mod h1:Y5yQAOtifL1yxbo5wqy6BxZv8vAUGQwXBOALyacEbxg=
google.golang.org/grpc v1.26.0/go.mod h1:qbnxyOmOxrQa7FizSgH+ReBfzJrCY1pSN7KXBS8abTk=
google.golang.org/grpc v1.27.0/go.mod h1:qbnxyOmOxrQa7FizSgH+ReBfzJrCY1pSN7KXBS8abTk=
google.golang.org/grpc v1.27.1 h1:zvIju4sqAGvwKspUQOhwnpcqSbzi7/H6QomNNjTL4sk=
google.golang.org/grpc v1.27.1/go.mod h1:qbnxyOmOxrQa7FizSgH+ReBfzJrCY1pSN7KXBS8abTk=
gopkg.in/alecthomas/kingpin.v2 v2.2.6/go.mod h1:FMv+mEhP44yOT+4EoQTLFTRgOQ1FBLkstjWtayDeSgw=
2018-10-06 18:57:53 +01:00
gopkg.in/check.v1 v0.0.0-20161208181325-20d25e280405/go.mod h1:Co6ibVJAznAaIkqp8huTwlJQCZ016jof/cbN4VW5Yz0=
gopkg.in/check.v1 v1.0.0-20180628173108-788fd7840127 h1:qIbj1fsPNlZgppZ+VLlY7N33q108Sa+fhmuc+sWQYwY=
2018-10-06 18:57:53 +01:00
gopkg.in/check.v1 v1.0.0-20180628173108-788fd7840127/go.mod h1:Co6ibVJAznAaIkqp8huTwlJQCZ016jof/cbN4VW5Yz0=
pkg/process: Now that we are trying to identify the root cause of the satellite load limitations (i.e. currently the satellite has a max ability of 400 rps for uploads and we need this to be higher), we are using the golang diagnostic tools to collect insight into what the bottlenecks are. We currently have a debug endpoint to gather some cpu and mem data, but it could be useful to have continuous profiling. GCP stackdriver has support for continuous profiling so lets set that up and see if it is helpful to gather more data. This PR adds support for [GCP continuous profiler](https://cloud.google.com/profiler) which allows enabling continuous cpu/mem profiling and the stats are sent to stackdriver in google cloud console. To enable the continuous profiling for a storj component, do the following: - prereq: the workload must be running in GKE and have Stackdriver Profiling IAM role permissions - provide the config flag `debug.profilename` in the config.yaml file for the workload (i.e. satellite api process, etc). The profilename should be the workload name, for example "satellite-api". - once the above config flag is provided, the profiler will be initialized and profiling stats will automatically be sent to GCP project where the workload is running and viewable in the Stackdriver Profile page in the console The current implementation assumes the workload is running in GKE, however if we find if useful we can add support to enable this from anywhere. But for simplicity, its configured this way assuming the main goal is to enable in production systems. Change-Id: Ibf8ebe2df7bf06fdd4951ee6a1e48854dd36ad47
2020-02-25 16:46:12 +00:00
gopkg.in/errgo.v2 v2.1.0/go.mod h1:hNsd1EY+bozCKY1Ytp96fpM3vjJbqLJn88ws8XvfDNI=
gopkg.in/fsnotify.v1 v1.4.7 h1:xOHLXZwVvI9hhs+cLKq5+I5onOuwQLhQwiu63xxlHs4=
2018-10-06 18:57:53 +01:00
gopkg.in/fsnotify.v1 v1.4.7/go.mod h1:Tz8NjZHkW78fSQdbUxIjBTcgA1z1m8ZHf0WmKUhAMys=
gopkg.in/inconshreveable/log15.v2 v2.0.0-20180818164646-67afb5ed74ec/go.mod h1:aPpfJ7XW+gOuirDoZ8gHhLh3kZ1B08FtV2bbmy7Jv3s=
gopkg.in/inf.v0 v0.9.1/go.mod h1:cWUDdTG/fYaXco+Dcufb5Vnc6Gp2YChqWtbxRZE0mXw=
gopkg.in/resty.v1 v1.12.0/go.mod h1:mDo4pnntr5jdWRML875a/NmxYqAlA73dVijT2AXvQQo=
gopkg.in/tomb.v1 v1.0.0-20141024135613-dd632973f1e7 h1:uRGJdciOHaEIrze2W8Q3AKkepLTh2hOroT7a+7czfdQ=
2018-10-06 18:57:53 +01:00
gopkg.in/tomb.v1 v1.0.0-20141024135613-dd632973f1e7/go.mod h1:dt/ZhP58zS4L8KSrWDmTeBkI65Dw0HsyUHuEVlX15mw=
gopkg.in/yaml.v2 v2.0.0-20170812160011-eb3733d160e7/go.mod h1:JAlM8MvJe8wmxCU4Bli9HhUf9+ttbYbLASfIpnQbh74=
2018-10-06 18:57:53 +01:00
gopkg.in/yaml.v2 v2.2.1/go.mod h1:hI93XBmqTisBFMUTm0b8Fm+jr3Dg1NNxqwp+5A1VGuI=
gopkg.in/yaml.v2 v2.2.2/go.mod h1:hI93XBmqTisBFMUTm0b8Fm+jr3Dg1NNxqwp+5A1VGuI=
gopkg.in/yaml.v2 v2.2.4 h1:/eiJrUcujPVeJ3xlSWaiNi3uSVmDGBK1pDHUHAnao1I=
gopkg.in/yaml.v2 v2.2.4/go.mod h1:hI93XBmqTisBFMUTm0b8Fm+jr3Dg1NNxqwp+5A1VGuI=
gotest.tools v2.2.0+incompatible h1:VsBPFP1AI068pPrMxtb/S8Zkgf9xEmTLJjfM+P5UIEo=
gotest.tools v2.2.0+incompatible/go.mod h1:DsYFclhRJ6vuDpmuTbkuFWG+y2sxOXAzmJt81HFBacw=
honnef.co/go/tools v0.0.0-20180728063816-88497007e858/go.mod h1:rf3lG4BRIbNafJWhAfAdb/ePZxsR/4RtNHQocxwk9r4=
honnef.co/go/tools v0.0.0-20190102054323-c2f93a96b099/go.mod h1:rf3lG4BRIbNafJWhAfAdb/ePZxsR/4RtNHQocxwk9r4=
honnef.co/go/tools v0.0.0-20190106161140-3f1c8253044a/go.mod h1:rf3lG4BRIbNafJWhAfAdb/ePZxsR/4RtNHQocxwk9r4=
pkg/process: Now that we are trying to identify the root cause of the satellite load limitations (i.e. currently the satellite has a max ability of 400 rps for uploads and we need this to be higher), we are using the golang diagnostic tools to collect insight into what the bottlenecks are. We currently have a debug endpoint to gather some cpu and mem data, but it could be useful to have continuous profiling. GCP stackdriver has support for continuous profiling so lets set that up and see if it is helpful to gather more data. This PR adds support for [GCP continuous profiler](https://cloud.google.com/profiler) which allows enabling continuous cpu/mem profiling and the stats are sent to stackdriver in google cloud console. To enable the continuous profiling for a storj component, do the following: - prereq: the workload must be running in GKE and have Stackdriver Profiling IAM role permissions - provide the config flag `debug.profilename` in the config.yaml file for the workload (i.e. satellite api process, etc). The profilename should be the workload name, for example "satellite-api". - once the above config flag is provided, the profiler will be initialized and profiling stats will automatically be sent to GCP project where the workload is running and viewable in the Stackdriver Profile page in the console The current implementation assumes the workload is running in GKE, however if we find if useful we can add support to enable this from anywhere. But for simplicity, its configured this way assuming the main goal is to enable in production systems. Change-Id: Ibf8ebe2df7bf06fdd4951ee6a1e48854dd36ad47
2020-02-25 16:46:12 +00:00
honnef.co/go/tools v0.0.0-20190418001031-e561f6794a2a/go.mod h1:rf3lG4BRIbNafJWhAfAdb/ePZxsR/4RtNHQocxwk9r4=
honnef.co/go/tools v0.0.0-20190523083050-ea95bdfd59fc/go.mod h1:rf3lG4BRIbNafJWhAfAdb/ePZxsR/4RtNHQocxwk9r4=
honnef.co/go/tools v0.0.1-2019.2.3 h1:3JgtbtFHMiCmsznwGVTUWbgGov+pVqnlf1dEJTNAXeM=
pkg/process: Now that we are trying to identify the root cause of the satellite load limitations (i.e. currently the satellite has a max ability of 400 rps for uploads and we need this to be higher), we are using the golang diagnostic tools to collect insight into what the bottlenecks are. We currently have a debug endpoint to gather some cpu and mem data, but it could be useful to have continuous profiling. GCP stackdriver has support for continuous profiling so lets set that up and see if it is helpful to gather more data. This PR adds support for [GCP continuous profiler](https://cloud.google.com/profiler) which allows enabling continuous cpu/mem profiling and the stats are sent to stackdriver in google cloud console. To enable the continuous profiling for a storj component, do the following: - prereq: the workload must be running in GKE and have Stackdriver Profiling IAM role permissions - provide the config flag `debug.profilename` in the config.yaml file for the workload (i.e. satellite api process, etc). The profilename should be the workload name, for example "satellite-api". - once the above config flag is provided, the profiler will be initialized and profiling stats will automatically be sent to GCP project where the workload is running and viewable in the Stackdriver Profile page in the console The current implementation assumes the workload is running in GKE, however if we find if useful we can add support to enable this from anywhere. But for simplicity, its configured this way assuming the main goal is to enable in production systems. Change-Id: Ibf8ebe2df7bf06fdd4951ee6a1e48854dd36ad47
2020-02-25 16:46:12 +00:00
honnef.co/go/tools v0.0.1-2019.2.3/go.mod h1:a3bituU0lyd329TUQxRnasdCoJDkEUEAqEt0JzvZhAg=
rsc.io/binaryregexp v0.2.0/go.mod h1:qTv7/COck+e2FymRvadv62gMdZztPaShugOCi3I+8D8=
storj.io/common v0.0.0-20200424175742-65ac59022f4f/go.mod h1:pZyXiIE7bGETIRXtfs0nICqMwp7PM8HqnDuyUeldNA0=
storj.io/common v0.0.0-20200729140050-4c1ddac6fa63 h1:BkRvlginTJGi0yAkpN+4ZKm2YpG63bDSDFLQtXYxxdg=
storj.io/common v0.0.0-20200729140050-4c1ddac6fa63/go.mod h1:ILr54ISCqCQ6MmIwT7eaR/fEGrBfgfxiPt8nmpWqnUM=
storj.io/common v0.0.0-20200818131620-f9cddf66b4be h1:kyX4v2M3ZNjlj0cFGON9as91Qm08Jg3XtVz7MQwjMy8=
storj.io/common v0.0.0-20200818131620-f9cddf66b4be/go.mod h1:ILr54ISCqCQ6MmIwT7eaR/fEGrBfgfxiPt8nmpWqnUM=
storj.io/drpc v0.0.11/go.mod h1:TiFc2obNjL9/3isMW1Rpxjy8V9uE0B2HMeMFGiiI7Iw=
storj.io/drpc v0.0.11/go.mod h1:TiFc2obNjL9/3isMW1Rpxjy8V9uE0B2HMeMFGiiI7Iw=
storj.io/drpc v0.0.14 h1:GCBdymTt1BRw4oHmmUZZlxYXLVRxxYj6x3Ivide2J+I=
storj.io/drpc v0.0.14/go.mod h1:82nfl+6YwRwF6UG31cEWWUqv/FaKvP5SGqUvoqTxCMA=
storj.io/monkit-jaeger v0.0.0-20200518165323-80778fc3f91b h1:Bbg9JCtY6l3HrDxs3BXzT2UYnYCBLqNi6i84Y8QIPUs=
storj.io/monkit-jaeger v0.0.0-20200518165323-80778fc3f91b/go.mod h1:gj4vuCeyCRjRmH8LIrgoyU9Dc9uR6H+/GcDUXmTbf80=
storj.io/private v0.0.0-20200729145012-46794d335b51 h1:wX/vLW1gzp/+sNXxt1fwJ9z3ZNpabQSBnY5IZXhtkhc=
storj.io/private v0.0.0-20200729145012-46794d335b51/go.mod h1:3BB0H9SmnJDfgk55uZli6DLHmhLiOdKiDY58ZI2e+pk=
storj.io/private v0.0.0-20200818170340-c2963305092f h1:1EC5IuctBVgxs3SQI2N2f6u5QdwTaX8IM9QAkFKGbMQ=
storj.io/private v0.0.0-20200818170340-c2963305092f/go.mod h1:3BB0H9SmnJDfgk55uZli6DLHmhLiOdKiDY58ZI2e+pk=
storj.io/uplink v1.2.0 h1:7gGfkTv7zT9ivSCMqu7QirUMdHVaeBnlZgqgWhRKEd4=
storj.io/uplink v1.2.0/go.mod h1:U7VFTdoZiBgmijKdvB5dUfi6V5Ew9PAHhUVyy8rJWBs=