Loggregator is the user application logging subsystem of Cloud Foundry.
If you would to see our plans for future development, check out the Loggregator Roadmap. You can track what we're working on today in our Pivotal Tracker Project. If you need to contact us, you can join our Slack channel.
- Features
- Usage
- Constraints
- Architecture
- Emitting Messages from other Cloud Foundry components
- Enabling TLS between Metron and Doppler
- Enabling TLS communication with etcd
- Deploying via BOSH
- Configuring the Firehose
- Consuming Log and Metric Data
- Development
- Loggregator as a separate release
- Metrics generated by Loggregator
- Logging message loss in Loggregator
- Loggregator does not guarantee logs delivered in order
- Workaround for Java multiline messages
- Log Message Size Constraints
- FAQ
- Additional readmes
Loggregator allows users to:
- Tail their application logs.
- Dump a recent set of application logs (where recent is a configurable number of log packets).
- Continually drain their application logs to 3rd party log archive and analysis services.
- (Operators and administrators only) Access the firehose, which includes the combined stream of logs from all apps, plus metrics data from CF components.
First, make sure you're using the new golang based CF CLI. Once that's installed:
cf logs APP_NAME [--recent]
For example:
$ cf logs private-app
Connected, tailing...
Oct 3 15:09:26 private-app App/0 STDERR This message is on stderr at 2013-10-03 22:09:26 +0000 for private-app instance 0
Oct 3 15:09:26 private-app App/0 STDERR 204.15.2.45, 10.10.2.148 - - [03/Oct/2013 22:09:26] "GET / HTTP/1.1" 200 81 0.0010
Oct 3 15:09:26 private-app App/0 This message is on stdout at 2013-10-03 22:09:26 +0000 for private-app instance 0
Oct 3 15:09:26 private-app App/0 STDERR This message is on stderr at 2013-10-03 22:09:26 +0000 for private-app instance 0
^C
- Loggregator collects STDOUT & STDERR from applications. This may require configuration on the developer's side.
- Warning: The DEA logging agent expects an application to have open connections on both STDOUT and STDERR. Closing either of these (for example, by redirecting output to
/dev/null
) will be read by the logging agent as a misbehaving application, and it will disconnect from all sockets for that app.
- A Loggregator outage must not affect the running application.
- Loggregator gathers and stores logs in a best-effort manner. While undesirable, losing the current buffer of application logs is acceptable.
- The 3rd party drain API should mimic Heroku's in order to reduce integration effort for our partners. The Heroku drain API is simply remote syslog over TCP.
Loggregator is composed of:
- Sources: Logging agents that run on the Cloud Foundry components.
- Metron: Metron agents are co-located with sources. They collect logs and forward them to:
- Doppler: Responsible for gathering logs from the Metron agents, storing them in temporary buffers, and forwarding logs to 3rd party syslog drains.
- Traffic Controller: Handles client requests for logs. Gathers and collates messages from all Doppler servers, and provides external API and message translation (as needed for legacy APIs).
Source agents emit the logging data as protocol-buffers, and the data stays in that format throughout the system.
In a redundant CloudFoundry setup, Loggregator can be configured to survive zone failures. Log messages from non-affected zones will still make it to the end user. On AWS, availability zones could be used as redundancy zones. The following is an example of a multi zone setup with two zones.
The role of Metron is to take traffic from the various emitter sources (dea, dea-logging-agent, router, etc) and route that traffic to one or more dopplers. In the current config we route this traffic to the dopplers in the same az. The traffic is randomly distributed across dopplers.
The role of Traffic Controller is to handle inbound HTTP and WebSocket requests for log data. It does this by proxying the request to all dopplers (regardless of AZ). Since an application can be deployed to multiple AZs, its logs can potentially end up on dopplers in multiple AZs. This is why the traffic controller will attempt to connect to dopplers in each AZ and will collate the data into a single stream for the web socket client.
The traffic controller itself is stateless; an incoming request can be handled by any instance in any AZ.
Traffic controllers also exposes a firehose
web socket endpoint. Connecting to this endpoint establishes connections to all dopplers, and streams logs and metrics for all applications and CF components.
Cloud Foundry developers can easily add source clients to new CF components that emit messages to the doppler. Currently, there are libraries for Go and Ruby. For usage information, look at their respective READMEs.
The default transport between Metron and Doppler is UDP. We have recently added support for TLS since it is a reliable protocol and also has in-built support to ensure integrity, encryption and authentication.
Metron agents can take a list of protocols of which it will fall back to in case the first protocol fails. For example, for metron_agent.protocols: ["tls", "udp"]
Metrons will try and connect to Dopplers over TLS. If there aren't any available TLS enabled Dopplers, it will then try to connect to Dopplers
over UDP.
NOTE: TLS support is currently experimental. Enable it at your own discretion. The properties discussed below as well as their behavior might change in the future.
Property | Required | Description |
---|---|---|
metron_agent.protocols |
No Default: ["udp"] |
Metron prefers this protocol to communicate with Doppler. Options are udp , tcp and tls . metron_agent.tls.* properties are required when this is set to tls |
metron_agent.tls.client_cert |
Yes if metron_agent.protocols includes "tls" Default: "" |
Signed client certificate used by Metron when communicating with Doppler over TLS |
metron_agent.tls.client_key |
Yes if metron_agent.protocols includes "tls" Default: "" |
Client key used by Metron when communicating with Doppler over TLS |
loggregator.tls.ca_cert |
Yes if metron_agent.protocols include "tls" Default: "" |
Certificate Authority used to sign the certificate |
Property | Required | Description |
---|---|---|
doppler.tls.enable |
No Default: false |
Enable TLS communication with Metron. If enabled, doppler.tls.* properties are required. |
doppler.tls.server_cert |
Yes if doppler.tls.enable: true Default: "" |
Signed server certificate used by Doppler when communicating with Doppler over TLS |
doppler.tls.server_key |
Yes if doppler.tls.enable: true Default: "" |
Server key used by Doppler when communicating with Metron over TLS |
loggregator.tls.ca_cert |
Yes if doppler.tls.enable: true Default: "" |
Certificate Authority used to sign the certificate |
An example manifest is given below:
loggregator:
tls:
ca_cert: |
-----BEGIN CERTIFICATE-----
LOGGREGATOR CA CERTIFICATE
-----END CERTIFICATE-----
metron_agent:
protocols: ["tls", "udp"]
tls:
client_cert: |
-----BEGIN CERTIFICATE-----
METRON AGENT CERTIFICATE
-----END CERTIFICATE-----
client_key: |
-----BEGIN RSA PRIVATE KEY-----
METRON AGENT KEY
-----END RSA PRIVATE KEY-----
doppler:
tls:
enable: true
server_cert: |
-----BEGIN CERTIFICATE-----
DOPPLER CERTIFICATE
-----END CERTIFICATE-----
server_key: |
-----BEGIN RSA PRIVATE KEY-----
DOPPLER KEY
-----END RSA PRIVATE KEY-----
For generating TLS certificates, we recommend certstrap. An operator can follow the following steps to successfully generate the required certificates.
-
Get certstrap
go get github.com/square/certstrap cd $GOPATH/src/github.com/square/certstrap ./build cd bin
-
Initialize a new certificate authority.
$ ./certstrap init --common-name "loggregatorCA" Enter passphrase (empty for no passphrase): <hit enter for no password> Enter same passphrase again: <hit enter for no password> Created out/loggregatorCA.key Created out/loggregatorCA.crt
-
Create and sign a certificate for the Doppler server.
$ ./certstrap request-cert --common-name "doppler" Enter passphrase (empty for no passphrase): <hit enter for no password> Enter same passphrase again: <hit enter for no password> Created out/doppler.key Created out/doppler.csr $ ./certstrap sign doppler --CA loggregatorCA Created out/doppler.crt from out/doppler.csr signed by out/loggregatorCA.key
- The manifest property
properties.doppler.tls.enable
should be set totrue
. - The manifest property
properties.doppler.tls.server_cert
should be set to the certificate inout/doppler.crt
. - The manifest property
properties.doppler.tls.server_key
should be set to the certificate inout/doppler.key
. - The manifest property
properties.loggregator.tls.ca_cert
should be set to the certificate inout/loggregatorCA.crt
.
- The manifest property
-
Create and sign a certificate for metron agents.
$ ./certstrap request-cert --common-name "metron_agent" Enter passphrase (empty for no passphrase): <hit enter for no password> Enter same passphrase again: <hit enter for no password> Created out/metron_agent.key Created out/metron_agent.csr $ ./certstrap sign metron_agent --CA loggregatorCA Created out/metron_agent.crt from out/metron_agent.csr signed by out/loggregatorCA.key
- The manifest property
properties.metron_agent.protocols
should include "tls". For example, ["tls", "udp"] - The manifest property
properties.metron_agent.tls.client_cert
should be set to the certificate inout/metron_agent.crt
, - The manifest property
properties.metron_agent.tls.client_key
should be set to the certificate inout/metron_agent.key
- The manifest property
If you already have a CA, or wish to use your own names for clients and servers, please note that the common-names "loggregatorCA" and "metron_agent" are placeholders and can be renamed.
The server certificate must have the common name doppler
.
By default, metron, doppler, syslog_drain_binder, and loggregator_trafficcontroller all communicate with etcd over http. To enable TLS mutual auth to etcd, you'll need to generate certificates and update your manifest
NOTE: Communicating with etcd over TLS is currently experimental, and will have an impact on other components within CloudFoundry. Enable it at your own discretion. The properties discussed below as well as their behavior might change in the future.
Refer to etcd-release's guide on Encrypting Traffic for etcd's properties.
Property | Required | Description |
---|---|---|
loggregator.etcd.require_ssl |
No Default: false |
Enable ssl for all communcation with etcd |
loggregator.etcd.ca_cert |
Yes if loggregator.etcd.require_ssl is set to true Default: "" |
PEM-encoded CA certificate |
metron_agent.etcd.client_cert |
Yes if loggregator.etcd.require_ssl is set to true Default: "" |
PEM-encoded client certificate |
metron_agent.etcd.client_key |
Yes if loggregator.etcd.require_ssl is set to true Default: "" |
PEM-encoded client key |
Property | Required | Description |
---|---|---|
loggregator.etcd.require_ssl |
No Default: false |
Enable ssl for all communcation with etcd |
loggregator.etcd.ca_cert |
Yes if loggregator.etcd.require_ssl is set to true Default: "" |
PEM-encoded CA certificate |
doppler.etcd.client_cert |
Yes if loggregator.etcd.require_ssl is set to true Default: "" |
PEM-encoded client certificate |
doppler.etcd.client_key |
Yes if loggregator.etcd.require_ssl is set to true Default: "" |
PEM-encoded client key |
Property | Required | Description |
---|---|---|
loggregator.etcd.require_ssl |
No Default: false |
Enable ssl for all communcation with etcd |
loggregator.etcd.ca_cert |
Yes if loggregator.etcd.require_ssl is set to true Default: "" |
PEM-encoded CA certificate |
traffic_controller.etcd.client_cert |
Yes if loggregator.etcd.require_ssl is set to true Default: "" |
PEM-encoded client certificate |
traffic_controller.etcd.client_key |
Yes if loggregator.etcd.require_ssl is set to true Default: "" |
PEM-encoded client key |
Property | Required | Description |
---|---|---|
loggregator.etcd.require_ssl |
No Default: false |
Enable ssl for all communcation with etcd |
loggregator.etcd.ca_cert |
Yes if loggregator.etcd.require_ssl is set to true Default: "" |
PEM-encoded CA certificate |
syslog_drain_binder.etcd.client_cert |
Yes if loggregator.etcd.require_ssl is set to true Default: "" |
PEM-encoded client certificate |
syslog_drain_binder.etcd.client_key |
Yes if loggregator.etcd.require_ssl is set to true Default: "" |
PEM-encoded client key |
An example manifest is given below:
loggregator:
etcd:
require_ssl: true
ca_cert: |
-----BEGIN CERTIFICATE-----
ETCD CA CERTIFICATE
-----END CERTIFICATE-----
metron_agent:
etcd:
client_cert: |
-----BEGIN CERTIFICATE-----
METRON AGENT CERTIFICATE
-----END CERTIFICATE-----
client_key: |
-----BEGIN RSA PRIVATE KEY-----
METRON AGENT KEY
-----END RSA PRIVATE KEY-----
doppler:
etcd:
client_cert: |
-----BEGIN CERTIFICATE-----
DOPPLER CERTIFICATE
-----END CERTIFICATE-----
client_key: |
-----BEGIN RSA PRIVATE KEY-----
DOPPLER KEY
-----END RSA PRIVATE KEY-----
traffic_controller:
etcd:
client_cert: |
-----BEGIN CERTIFICATE-----
TRAFFIC CONTROLLER CERTIFICATE
-----END CERTIFICATE-----
client_key: |
-----BEGIN RSA PRIVATE KEY-----
TRAFFIC CONTROLLER KEY
-----END RSA PRIVATE KEY-----
syslog_drain_binder:
etcd:
client_cert: |
-----BEGIN CERTIFICATE-----
SYSLOG DRAIN BINDER CERTIFICATE
-----END CERTIFICATE-----
client_key: |
-----BEGIN RSA PRIVATE KEY-----
SYSLOG DRAIN BINDER KEY
-----END RSA PRIVATE KEY-----
Below are example snippets for deploying the DEA Logging Agent (source), Doppler, and Loggregator Traffic Controller via BOSH.
jobs:
- name: dea_next
templates:
- name: dea_next
release: cf
- name: dea_logging_agent
release: cf
- name: metron_agent
release: cf
instances: 1
resource_pool: dea
networks:
- name: cf1
default:
- dns
- gateway
properties:
dea_next:
zone: z1
metron_agent:
zone: z1
networks:
apps: cf1
- name: doppler_z1 # Add "doppler_zX" jobs if you have runners in zX
templates:
- name: doppler
release: cf
- name: syslog_drain_binder
release: cf
- name: metron_agent
release: cf
instances: 1 # Scale out as neccessary
resource_pool: common
networks:
- name: cf1
properties:
doppler:
zone: z1
networks:
apps: cf1
- name: loggregator_trafficcontroller_z1
templates:
- name: loggregator_trafficcontroller
release: cf
- name: metron_agent
release: cf
instances: 1 # Scale out as necessary
resource_pool: common
networks:
- name: cf1
properties:
traffic_controller:
zone: z1 # Denoting which one of the redundancy zones this traffic controller is servicing
metron_agent:
zone: z1
networks:
apps: cf1
properties:
loggregator:
servers:
z1: # A list of loggregator servers for every redundancy zone
- 10.10.16.14
incoming_port: 3456
outgoing_port: 8080
loggregator_endpoint: # The end point sources will connect to
shared_secret: loggregatorEndPointSharedSecret
host: 10.10.16.16
port: 3456
The firehose feature includes the combined stream of logs from all apps, plus metrics data from CF components, and is intended to be used by operators and administrators.
Access to the firehose requires a user with the doppler.firehose
scope.
The "cf" UAA client needs permission to grant this custom scope to users.
The configuration of the uaa
job in Cloud Foundry adds this scope by default.
However, if your Cloud Foundry instance overrides the properties.uaa.clients.cf
property in a stub, you need to add doppler.firehose
to the scope list in the properties.uaa.clients.cf.scope
property.
In your deployment manifest, add
properties:
…
uaa:
…
clients:
…
cf:
scope: …,doppler.firehose
…
doppler:
override: true
authorities: uaa.resource
secret: YOUR-DOPPLER-SECRET
(The properties.uaa.clients.doppler.id
key should be populated automatically.) These are also set by default in cf-properties.yml.
Before continuing, you should be familiar with the uaac
tool.
- Ensure that doppler is a UAA client. If
uaac client get doppler
returns output like
scope: uaa.none
client_id: doppler
resource_ids: none
authorized_grant_types: authorization_code refresh_token
authorities: uaa.resource
then you're set.
- If it does not exist, run
uaac client add doppler --scope uaa.none --authorized_grant_types authorization_code,refresh_token --authorities uaa.resource
(and set its secret). - If it exists but with incorrect properties, run
uaac client update doppler --scope uaa.none --authorized_grant_types "authorization_code refresh_token" --authorities uaa.resource
. - Grant firehose access to the
cf
client. - Check the scopes assigned to
cf
withuaac client get cf
, e.g.
```
scope: cloud_controller.admin cloud_controller.read cloud_controller.write openid password.write scim.read scim.userids scim.write
client_id: cf
resource_ids: none
authorized_grant_types: implicit password refresh_token
access_token_validity: 600
refresh_token_validity: 2592000
authorities: uaa.none
autoapprove: true
```
- Copy the existing scope and add
doppler.firehose
, then update the client
```
uaac client update cf --scope "cloud_controller.admin cloud_controller.read cloud_controller.write openid password.write scim.read scim.userids scim.write doppler.firehose"
```
The NOAA Client library, written in Golang, can be used by Go applications to consume app log data as well as the log + metrics firehose. If you wish to write your own client application using this library, please refer to the NOAA source and documentation.
Multiple subscribers may connect to the firehose endpoint, each with a unique subscription_id. Each subscriber (in practice, a pool of clients with a common subscription_id) receives the entire stream. For each subscription_id, all data will be distributed evenly among that subscriber's client pool. In the event any subscription connections are lost, the messages will be rerouted to the remaining conenctions in the client pool.
The Cloud Foundry team uses GitHub and accepts contributions via pull request.
Follow these steps to make a contribution to any of our open source repositories:
-
Complete our CLA Agreement for individuals or corporations
-
Set your name and email
git config --global user.name "Firstname Lastname" git config --global user.email "your_email@youremail.com"
-
Fork the repo (from
develop
branch to get the latest changes) -
Make your changes on a topic branch, commit, and push to github and open a pull request against the
develop
branch.
Once your commits are approved by Travis CI and reviewed by the core team, they will be merged.
As of version 43a6dd94d06cf1e2e1b64ffba19566ab4b74755e, all Loggregator components are deployed to Cloud Foundry with Go 1.6.1. As of that revision, support for earlier versions of the language are not guaranteed.
Use brew and do
brew install go --cross-compile-all
brew install direnv
Make sure you add the proper entry to load direnv into your shell. See brew info direnv
for details. To be safe, close the terminal window that you are using to make sure the
changes to your shell are applied.
git clone https://github.com/cloudfoundry/loggregator
cd loggregator # When you cd into the loggregator dir for the first time direnv will prompt you to trust the config file
git submodule update --init
Please run bin/install-git-hooks
before committing for the first time. The pre-commit hook that this installs will ensure that all dependencies are properly listed in the bosh/packages
directory. (Of course, you should probably convince yourself that the hooks are safe before installing them.) Without this script, it is possible to commit a version of the repository that will not compile.
Install go vet and go cover
go get golang.org/x/tools/cmd/vet
go get golang.org/x/tools/cmd/cover
Install gosub
go get github.com/vito/gosub
bin/test
export GOPATH=`pwd` #in the root of the project
go get github.com/onsi/ginkgo/ginkgo
export PATH=$PATH:$GOPATH/bin
cd src/loggregator # or any other component
ginkgo -r
Doppler will dump information about the running goroutines to stdout if sent a USR1
signal.
goroutine 1 [running]:
runtime/pprof.writeGoroutineStacks(0xc2000bc3f0, 0xc200000008, 0xc200000001, 0xca0000c2001fcfc0)
/home/travis/.gvm/gos/go1.1.1/src/pkg/runtime/pprof/pprof.go:511 +0x7a
runtime/pprof.writeGoroutine(0xc2000bc3f0, 0xc200000008, 0x2, 0xca74765c960d5c8f, 0x40bbf7, ...)
/home/travis/.gvm/gos/go1.1.1/src/pkg/runtime/pprof/pprof.go:500 +0x3a
....
There are cases when releases outside of Cloud Foundry would like to emit logs and metrics to the Loggregator system. In such cases we have instructions on using Loggregator as a separate release here.
See this list.
One of the design priciples of Loggregator is to not put "back pressure" on apps. Therefore apps can log faster than the logging infrastructre can transport. In order to implement non-blocking, UDP was chosen as the transport protocol. Because of this, log message delivery is not guaranteed. If you wish to monitor log loss, there are metrics that can inform you.
There are two places loss can occur. The first place is inside the Diego cell where an app is logging so fast the local Metron can’t get the log messages off of the VM fast enough. To monitor that, you have to be on Diego, then the following works.
Metric #1 is rep.logSenderTotalMessagesRead
, the number of messages sent by Diego apps. Metric #2 is MetronAgent.DopplerForwarder.sentMessages
, the number of messages passed on by the Metron. #1-#2 will tell you the loss within the cell. You can monitor it on a cell by cell basis, or across all VMs based on the tags.
The other place for loss is between the Metron and Dopplers. To monitor that (and this works on DEAs too), metric #1 is MetronAgent.DopplerForwarder.sentMessages
(same as #1 above) and metric #2 is DopplerServer.udpListener.receivedMessageCount
. #1-#2 gives you loss between Metron and Doppler.
Using a graphing utility to display these absolute counter values as "rate per second" can give a good visualization of any loss.
The Loggregator team as a plan to improve this. You can read about it in our Reliable Logging Feature Narrative.
The Loggregator system is designed to scale horizontally in order to handle the different load profiles of logs and metrics that are generated by the apps and the system components. As the size of the deployment grows, this logging load gets larger than can be handled by a single VM chain. This scaling is done by adding Dopplers. The logging load coming out of different VMs varies greatly. These VMs range from DEA and Diego app runners to system components such as the Router and the Cloud Controller. In order to evenly distribute the load over the scaled Dopplers, the Metrons distribute their log streams over the different Dopplers. The algorithm used is that for each log line (message), the Metron chooses a random Doppler to send it to. This means that each sequential log message from an application is routed through a different Doppler via a different network path. Because of the different paths, log lines take varying times to arrive at the destination nozzle or syslog and therefore the lines are not guaranteed to be in order.
Having log lines out of order can be painful when monitoring Java stack traces. There is a work around involving modifing the Java log output to have your app reformat your stacktrace messages so any newline characters are replaced by a token; and then have your log parsing code replace that token with newline characters again. The following example is for the Java Logback library and ELK, but you may be able to use the same strategy for other Java log libraries and log aggregators.
With the Java Logback library you do this by adding %replace(%xException){'\n','\u2028'}%nopex
to your logging config , and then use the following logstash conf.
# Replace the unicode newline character \u2028 with \n, which Kibana will display as a new line.
mutate {
gsub => [ "[@message]", '\u2028', "
"]
# ^^^ Seems that passing a string with an actual newline in it is the only way to make gsub work
}
to replace the token with a regular newline again so it displays "properly" in Kibana.
Application logs are emitted via UDP messages from Diego/DEA to Metron. The maximum log message size from a DEA is ~60KiB (assuming overhead for the transporting envelope). Diego breaks up log messages greater than ~60KiB into multiple envelopes to mitigate this constraint.