Hitoshi Mitake f8a290e7ca *: support jwt token in v3 auth API
This commit adds jwt token support in v3 auth API.

Remaining major ToDos:
- Currently token type isn't hidden from etcdserver. In the near
  future the information should be completely invisible from
  etcdserver package.
- Configurable expiration of token. Currently tokens can be valid
  until keys are changed.

How to use:
1. generate keys for signing and verfying jwt tokens:
 $ openssl genrsa -out app.rsa 1024
 $ openssl rsa -in app.rsa -pubout > app.rsa.pub
2.  add command line options to etcd like below:
--auth-token-type jwt \
--auth-jwt-pub-key app.rsa.pub --auth-jwt-priv-key app.rsa \
--auth-jwt-sign-method RS512
3. launch etcd cluster

Below is a performance comparison of serializable read w/ and w/o jwt
token. Every (3) etcd node is executed on a single machine. Signing
method is RS512 and key length is 1024 bit. As the results show, jwt
based token introduces a performance overhead but it would be
acceptable for a case that requires authentication.

w/o jwt token auth (no auth):

Summary:
  Total:        1.6172 secs.
  Slowest:      0.0125 secs.
  Fastest:      0.0001 secs.
  Average:      0.0002 secs.
  Stddev:       0.0004 secs.
  Requests/sec: 6183.5877

Response time histogram:
  0.000 [1]     |
  0.001 [9982]  |∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎
  0.003 [1]     |
  0.004 [1]     |
  0.005 [0]     |
  0.006 [0]     |
  0.008 [6]     |
  0.009 [0]     |
  0.010 [1]     |
  0.011 [5]     |
  0.013 [3]     |

Latency distribution:
  10% in 0.0001 secs.
  25% in 0.0001 secs.
  50% in 0.0001 secs.
  75% in 0.0001 secs.
  90% in 0.0002 secs.
  95% in 0.0002 secs.
  99% in 0.0003 secs.

w/ jwt token auth:

Summary:
  Total:        2.5364 secs.
  Slowest:      0.0182 secs.
  Fastest:      0.0002 secs.
  Average:      0.0003 secs.
  Stddev:       0.0005 secs.
  Requests/sec: 3942.5185

Response time histogram:
  0.000 [1]     |
  0.002 [9975]  |∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎
  0.004 [0]     |
  0.006 [1]     |
  0.007 [11]    |
  0.009 [2]     |
  0.011 [4]     |
  0.013 [5]     |
  0.015 [0]     |
  0.016 [0]     |
  0.018 [1]     |

Latency distribution:
  10% in 0.0002 secs.
  25% in 0.0002 secs.
  50% in 0.0002 secs.
  75% in 0.0002 secs.
  90% in 0.0003 secs.
  95% in 0.0003 secs.
  99% in 0.0004 secs.
2017-03-06 19:46:03 -08:00
2017-03-06 11:40:46 -08:00
2016-05-12 20:51:48 -07:00
2017-03-06 19:46:03 -08:00
2017-01-23 18:52:39 -08:00
2017-03-06 19:46:03 -08:00
2017-03-06 11:40:46 -08:00
2017-02-28 17:08:17 -08:00
2017-03-06 19:46:03 -08:00
2016-05-12 20:51:48 -07:00
2017-03-06 11:40:46 -08:00
2014-12-18 14:59:06 -08:00
2016-11-10 12:02:00 -08:00
2017-03-06 11:40:46 -08:00
2017-01-13 12:58:15 -08:00
2014-11-18 15:01:57 -08:00
2016-05-12 20:56:50 -07:00
2017-03-06 14:59:12 -08:00
2016-12-13 16:08:50 +08:00
2017-03-06 19:46:03 -08:00
2017-03-06 19:46:03 -08:00
2014-01-19 12:25:11 -08:00
2016-05-12 20:51:48 -07:00
2017-02-23 14:38:14 -08:00
2017-02-23 15:02:36 -08:00
2014-01-19 12:25:11 -08:00
2016-05-23 11:59:23 -07:00
2017-03-06 11:40:46 -08:00
2017-01-20 13:50:36 -08:00
2017-03-06 14:59:12 -08:00
2016-05-23 11:59:23 -07:00

etcd

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Note: The master branch may be in an unstable or even broken state during development. Please use releases instead of the master branch in order to get stable binaries.

the etcd v2 documentation has moved

etcd Logo

etcd is a distributed, consistent key-value store for shared configuration and service discovery, with a focus on being:

  • Simple: well-defined, user-facing API (gRPC)
  • Secure: automatic TLS with optional client cert authentication
  • Fast: benchmarked 10,000 writes/sec
  • Reliable: properly distributed using Raft

etcd is written in Go and uses the Raft consensus algorithm to manage a highly-available replicated log.

etcd is used in production by many companies, and the development team stands behind it in critical deployment scenarios, where etcd is frequently teamed with applications such as Kubernetes, fleet, locksmith, vulcand, Doorman, and many others. Reliability is further ensured by rigorous testing.

See etcdctl for a simple command line client.

Getting started

Getting etcd

The easiest way to get etcd is to use one of the pre-built release binaries which are available for OSX, Linux, Windows, rkt, and Docker. Instructions for using these binaries are on the GitHub releases page.

For those wanting to try the very latest version, build the latest version of etcd from the master branch. This first needs Go installed (version 1.7+ is required). All development occurs on master, including new features and bug fixes. Bug fixes are first targeted at master and subsequently ported to release branches, as described in the branch management guide.

Running etcd

First start a single-member cluster of etcd:

./bin/etcd

This will bring up etcd listening on port 2379 for client communication and on port 2380 for server-to-server communication.

Next, let's set a single key, and then retrieve it:

ETCDCTL_API=3 etcdctl put mykey "this is awesome"
ETCDCTL_API=3 etcdctl get mykey

That's it! etcd is now running and serving client requests. For more

etcd TCP ports

The official etcd ports are 2379 for client requests, and 2380 for peer communication.

Running a local etcd cluster

First install goreman, which manages Procfile-based applications.

Our Procfile script will set up a local example cluster. Start it with:

goreman start

This will bring up 3 etcd members infra1, infra2 and infra3 and etcd proxy proxy, which runs locally and composes a cluster.

Every cluster member and proxy accepts key value reads and key value writes.

Running etcd on Kubernetes

To run an etcd cluster on Kubernetes, try etcd operator.

Next steps

Now it's time to dig into the full etcd API and other guides.

Contact

Contributing

See CONTRIBUTING for details on submitting patches and the contribution workflow.

Reporting bugs

See reporting bugs for details about reporting any issues.

License

etcd is under the Apache 2.0 license. See the LICENSE file for details.

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Distributed reliable key-value store for the most critical data of a distributed system
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