Deployment
See also:
The main Jaeger backend components are released as Docker images on Docker Hub and Quay :
There are orchestration templates for running Jaeger with:
- Kubernetes: github.com/jaegertracing/jaeger-kubernetes ,
- OpenShift: github.com/jaegertracing/jaeger-openshift .
Configuration Options
Jaeger binaries can be configured in a number of ways (in the order of decreasing priority):
- command line arguments,
- environment variables,
- configuration files in JSON, TOML, YAML, HCL, or Java properties formats.
To see the complete list of options, run the binary with help
command or refer to the
CLI Flags
page for more information. Options that are specific to a certain storage backend are only listed if the storage type is selected. For example, to see all available options in the Collector with Cassandra storage:
$ docker run --rm \
-e SPAN_STORAGE_TYPE=cassandra \
jaegertracing/jaeger-collector:1.34.0 \
help
In order to provide configuration parameters via environment variables, find the respective command line option and convert its name to UPPER_SNAKE_CASE, for example:
Command line option | Environment variable |
---|---|
--cassandra.connections-per-host | CASSANDRA_CONNECTIONS_PER_HOST |
--metrics-backend | METRICS_BACKEND |
All-in-one
Jaeger all-in-one is a special distribution that combines three Jaeger components,
agent
,
collector
, and
query service/UI
, in a single binary or container image. It is useful for single-node deployments where your trace volume is light enough to be handled by a single instance. By default, all-in-one starts with memory
storage, meaning it will lose all data upon restart. All other
span storage backends
can also be used with all-in-one, but memory
and badger
are exclusive to all-in-one because they cannot be shared between instances.
All-in-one listens to the same ports as the components it contains (described below), with the exception of the admin port.
Port | Protocol | Function |
---|---|---|
14269 | HTTP | admin port: health check at / and metrics at /metrics |
## make sure to expose only the ports you use in your deployment scenario!
docker run -d --name jaeger \
-e COLLECTOR_ZIPKIN_HOST_PORT=:9411 \
-p 5775:5775/udp \
-p 6831:6831/udp \
-p 6832:6832/udp \
-p 5778:5778 \
-p 16686:16686 \
-p 14250:14250 \
-p 14268:14268 \
-p 14269:14269 \
-p 9411:9411 \
jaegertracing/all-in-one:1.34.0
You can navigate to http://localhost:16686
to access the Jaeger UI.
Agent
Jaeger client libraries expect jaeger-agent process to run locally on each host. The agent exposes the following ports:
Port | Protocol | Function |
---|---|---|
6831 | UDP | accept
jaeger.thrift
in compact Thrift protocol used by most current Jaeger clients |
6832 | UDP | accept
jaeger.thrift
in binary Thrift protocol used by Node.js Jaeger client (because
thriftrw
npm package does not support compact protocol) |
5778 | HTTP | serve configs, sampling strategies |
5775 | UDP | accept
zipkin.thrift
in compact Thrift protocol (deprecated; only used by very old Jaeger clients, circa 2016) |
14271 | HTTP | admin port: health check at / and metrics at /metrics |
It can be executed directly on the host or via Docker, as follows:
## make sure to expose only the ports you use in your deployment scenario!
docker run \
--rm \
-p6831:6831/udp \
-p6832:6832/udp \
-p5778:5778/tcp \
-p5775:5775/udp \
jaegertracing/jaeger-agent:1.34.0
Discovery System Integration
The agents can connect point to point to a single collector address, which could be load balanced by another infrastructure component (e.g. DNS) across multiple collectors. The agent can also be configured with a static list of collector addresses.
On Docker, a command like the following can be used:
docker run \
--rm \
-p5775:5775/udp \
-p6831:6831/udp \
-p6832:6832/udp \
-p5778:5778/tcp \
jaegertracing/jaeger-agent:1.34.0 \
--reporter.grpc.host-port=jaeger-collector.jaeger-infra.svc:14250
When using gRPC, you have several options for load balancing and name resolution:
- Single connection and no load balancing. This is the default if you specify a single
host:port
. (example:--reporter.grpc.host-port=jaeger-collector.jaeger-infra.svc:14250
) - Static list of hostnames and round-robin load balancing. This is what you get with a comma-separated list of addresses. (example:
reporter.grpc.host-port=jaeger-collector1:14250,jaeger-collector2:14250,jaeger-collector3:14250
) - Dynamic DNS resolution and round-robin load balancing. To get this behavior, prefix the address with
dns:///
and gRPC will attempt to resolve the hostname using SRV records (for external load balancing ), TXT records (for service configs ), and A records. Refer to the gRPC Name Resolution docs and the dns_resolver.go implementation for more info. (example:--reporter.grpc.host-port=dns:///jaeger-collector.jaeger-infra.svc:14250
)
Agent level tags
Jaeger supports agent level tags, that can be added to the process tags of all spans passing through the agent. This is supported through the command line flag --agent.tags=key1=value1,key2=value2,...,keyn=valuen
. Tags can also be set through an environment flag like so - --agent.tags=key=${envFlag:defaultValue}
- The tag value will be set to the value of the envFlag
environment key and defaultValue
if not set.
Collector
The collectors are stateless and thus many instances of jaeger-collector can be run in parallel.
Collectors require almost no configuration, except for storage location, such as
--cassandra.keyspace
and --cassandra.servers
options, or the location of Elasticsearch cluster,
via --es.server-urls
, depending on which storage is specified. See the
CLI Flags
for all
command line options.
At default settings the collector exposes the following ports:
Port | Protocol | Function |
---|---|---|
9411 | HTTP | can accept Zipkin spans in Thrift, JSON and Proto (disabled by default) |
14250 | gRPC | used by jaeger-agent to send spans in model.proto format |
14268 | HTTP | can accept spans directly from clients in jaeger.thrift format over binary thrift protocol |
14269 | HTTP | admin port: health check at / and metrics at /metrics |
Span Storage Backends
Collectors require a persistent storage backend. Cassandra and Elasticsearch are the primary supported distributed storage backends. Additional backends are discussed here .
The storage type can be passed via SPAN_STORAGE_TYPE
environment variable. Valid values are cassandra
, elasticsearch
, kafka
(only as a buffer), grpc-plugin
, badger
(only with all-in-one) and memory
(only with all-in-one).
As of version 1.6.0, it’s possible to use multiple storage types at the same time by providing a comma-separated list of valid types to the SPAN_STORAGE_TYPE
environment variable. It’s important to note that all listed storage types are used for writing, but only the first type in the list will be used for reading and archiving.
For large scale production deployment the Jaeger team recommends Elasticsearch backend over Cassandra .
Memory
The in-memory storage is not intended for production workloads. It’s intended as a simple solution to get started quickly and data will be lost once the process is gone.
By default, there’s no limit in the amount of traces stored in memory but a limit can be established by passing an
integer value via --memory.max-traces
.
Badger - local storage
Experimental since Jaeger 1.9
Badger
is an embedded local storage, only available
with all-in-one distribution. By default it acts as an ephemeral storage using a temporary filesystem.
This can be overridden by using the --badger.ephemeral=false
option.
docker run \
-e SPAN_STORAGE_TYPE=badger \
-e BADGER_EPHEMERAL=false \
-e BADGER_DIRECTORY_VALUE=/badger/data \
-e BADGER_DIRECTORY_KEY=/badger/key \
-v <storage_dir_on_host>:/badger \
-p 16686:16686 \
jaegertracing/all-in-one:1.34.0
Upgrade Badger v1 to v3
In Jaeger 1.24.0, Badger is upgraded from v1.6.2 to v3.2103.0 which changes the underlying data format. Following steps will help in migrating your data:
- In Badger v1, the data looks like:
❯ ls /tmp/badger/
data key
❯ ls /tmp/badger/data/
000001.vlog 000004.vlog 000005.vlog 000008.vlog 000011.vlog 000012.vlog 000013.vlog 000014.vlog 000015.vlog 000016.vlog 000017.vlog
❯ ls /tmp/badger/key/
000038.sst 000048.sst 000049.sst 000050.sst 000051.sst 000059.sst 000060.sst 000061.sst 000063.sst 000064.sst 000065.sst 000066.sst MANIFEST
- Make a backup of your data directory to have a copy incase migration didn’t work successfully.
❯ cp -r /tmp/badger /tmp/badger.bk
- Download, extract and compile the source code of badger v1: https://github.com/dgraph-io/badger/archive/refs/tags/v1.6.2.tar.gz
❯ tar xvzf badger-1.6.2.tar
❯ cd badger-1.6.2/badger/
❯ go install
This will install the badger command line utility into your $GOBIN path eg ~/go/bin/badger.
- Use badger utility to take backup of data.
❯ ~/go/bin/badger backup --dir /tmp/badger/key --vlog-dir /tmp/badger/data/
Listening for /debug HTTP requests at port: 8080
badger 2021/06/24 22:04:30 INFO: All 12 tables opened in 907ms
badger 2021/06/24 22:04:30 INFO: Replaying file id: 17 at offset: 64584535
badger 2021/06/24 22:04:30 INFO: Replay took: 12.303µs
badger 2021/06/24 22:04:30 DEBUG: Value log discard stats empty
badger 2021/06/24 22:04:30 INFO: DB.Backup Created batch of size: 9.7 kB in 75.907µs.
badger 2021/06/24 22:04:31 INFO: DB.Backup Created batch of size: 4.3 MB in 8.003592ms.
....
....
badger 2021/06/24 22:04:31 INFO: DB.Backup Created batch of size: 30 MB in 74.808075ms.
badger 2021/06/24 22:04:36 INFO: DB.Backup Sent 15495232 keys
badger 2021/06/24 22:04:36 INFO: Got compaction priority: {level:0 score:1.73 dropPrefixes:[]}
This will create a badger.bak file in the current directory.
- Download, extract and compile the source code of badger v3: https://github.com/dgraph-io/badger/archive/refs/tags/v3.2103.0.tar.gz
❯ tar xvzf badger-3.2103.0.tar
❯ cd badger-3.2103.0/badger/
❯ go install
This will install the badger command line utility into your $GOBIN path eg ~/go/bin/badger.
- Restore the data from backup.
❯ ~/go/bin/badger restore --dir jaeger-v3
Listening for /debug HTTP requests at port: 8080
jemalloc enabled: false
Using Go memory
badger 2021/06/24 22:08:29 INFO: All 0 tables opened in 0s
badger 2021/06/24 22:08:29 INFO: Discard stats nextEmptySlot: 0
badger 2021/06/24 22:08:29 INFO: Set nextTxnTs to 0
badger 2021/06/24 22:08:37 INFO: [0] [E] LOG Compact 0->6 (5, 0 -> 50 tables with 1 splits). [00001 00002 00003 00004 00005 . .] -> [00006 00007 00008 00009 00010 00011 00012 00013 00014 00015 00016 00017 00018 00019 00020 00021 00022 00023 00024 00025 00026 00028 00029 00030 00031 00032 00033 00034 00035 00036 00037 00038 00039 00040 00041 00043 00044 00045 00046 00047 00048 00049 00050 00051 00052 00053 00054 00055 00056 00057 .], took 2.597s
badger 2021/06/24 22:08:53 INFO: Lifetime L0 stalled for: 0s
badger 2021/06/24 22:08:55 INFO:
Level 0 [ ]: NumTables: 00. Size: 0 B of 0 B. Score: 0.00->0.00 StaleData: 0 B Target FileSize: 64 MiB
Level 1 [ ]: NumTables: 00. Size: 0 B of 10 MiB. Score: 0.00->0.00 StaleData: 0 B Target FileSize: 2.0 MiB
Level 2 [ ]: NumTables: 00. Size: 0 B of 10 MiB. Score: 0.00->0.00 StaleData: 0 B Target FileSize: 2.0 MiB
Level 3 [ ]: NumTables: 00. Size: 0 B of 10 MiB. Score: 0.00->0.00 StaleData: 0 B Target FileSize: 2.0 MiB
Level 4 [B]: NumTables: 45. Size: 86 MiB of 10 MiB. Score: 8.64->10.21 StaleData: 0 B Target FileSize: 2.0 MiB
Level 5 [ ]: NumTables: 08. Size: 29 MiB of 34 MiB. Score: 0.00->0.00 StaleData: 0 B Target FileSize: 4.0 MiB
Level 6 [ ]: NumTables: 63. Size: 340 MiB of 340 MiB. Score: 0.00->0.00 StaleData: 0 B Target FileSize: 8.0 MiB
Level Done
Num Allocated Bytes at program end: 0 B
This will restore the data in jaeger-v3 directory. It will look like this
❯ ls ./jaeger-v3
000001.vlog 000180.sst 000257.sst 000276.sst 000294.sst 000327.sst 000336.sst 000349.sst 000356.sst 000364.sst 000371.sst 000378.sst 000385.sst 000392.sst 000399.sst 000406.sst 000413.sst MANIFEST
000006.sst 000181.sst 000259.sst 000277.sst 000302.sst 000328.sst 000339.sst 000350.sst 000357.sst 000365.sst 000372.sst 000379.sst 000386.sst 000393.sst 000400.sst 000407.sst 000414.sst
000007.sst 000195.sst 000261.sst 000278.sst 000305.sst 000330.sst 000340.sst 000351.sst 000359.sst 000366.sst 000373.sst 000380.sst 000387.sst 000394.sst 000401.sst 000408.sst 000415.sst
000008.sst 000218.sst 000265.sst 000279.sst 000315.sst 000331.sst 000341.sst 000352.sst 000360.sst 000367.sst 000374.sst 000381.sst 000388.sst 000395.sst 000402.sst 000409.sst 000416.sst
000061.sst 000227.sst 000267.sst 000282.sst 000324.sst 000332.sst 000343.sst 000353.sst 000361.sst 000368.sst 000375.sst 000382.sst 000389.sst 000396.sst 000403.sst 000410.sst 000417.sst
000134.sst 000249.sst 000272.sst 000285.sst 000325.sst 000333.sst 000344.sst 000354.sst 000362.sst 000369.sst 000376.sst 000383.sst 000390.sst 000397.sst 000404.sst 000411.sst DISCARD
000154.sst 000255.sst 000275.sst 000289.sst 000326.sst 000334.sst 000348.sst 000355.sst 000363.sst 000370.sst 000377.sst 000384.sst 000391.sst 000398.sst 000405.sst 000412.sst KEYREGISTRY
- Separate out the key and data directories.
❯ rm -rf /tmp/badger
❯ mv ./jaeger-v3 /tmp/badger
❯ mkdir /tmp/badger/data /tmp/badger/key
❯ mv /tmp/badger/*.vlog /tmp/badger/data/
❯ mv /tmp/badger/*.sst /tmp/badger/key/
❯ mv /tmp/badger/MANIFEST /tmp/badger/DISCARD /tmp/badger/KEYREGISTRY /tmp/badger/key/
- Start Jaeger v1.24.0. It should start well.
Cassandra
Supported versions: 3.4+
Deploying Cassandra itself is out of scope for our documentation. One good source of documentation is the Apache Cassandra Docs .
Configuration
Minimal
docker run \
-e SPAN_STORAGE_TYPE=cassandra \
-e CASSANDRA_SERVERS=<...> \
jaegertracing/jaeger-collector:1.34.0
Note: White space characters are allowed in CASSANDRA_SERVERS
. For Example: Servers can be passed as `CASSANDRA_SERVERS=“1.2.3.4, 5.6.7.8” for better readability.
All options
To view the full list of configuration options, you can run the following command:
docker run \
-e SPAN_STORAGE_TYPE=cassandra \
jaegertracing/jaeger-collector:1.34.0 \
--help
Schema script
A script is provided to initialize Cassandra keyspace and schema
using Cassandra’s interactive shell
cqlsh
:
MODE=test sh ./plugin/storage/cassandra/schema/create.sh | cqlsh
For production deployment, pass MODE=prod DATACENTER={datacenter}
arguments to the script,
where {datacenter}
is the name used in the Cassandra configuration / network topology.
The script also allows overriding TTL, keyspace name, replication factor, etc. Run the script without arguments to see the full list of recognized parameters.
TLS support
Jaeger supports TLS client to node connections as long as you’ve configured
your Cassandra cluster correctly. After verifying with e.g. cqlsh
, you can
configure the collector and query like so:
docker run \
-e CASSANDRA_SERVERS=<...> \
-e CASSANDRA_TLS=true \
-e CASSANDRA_TLS_SERVER_NAME="CN-in-certificate" \
-e CASSANDRA_TLS_KEY=<path to client key file> \
-e CASSANDRA_TLS_CERT=<path to client cert file> \
-e CASSANDRA_TLS_CA=<path to your CA cert file> \
jaegertracing/jaeger-collector:1.34.0
The schema tool also supports TLS. You need to make a custom cqlshrc file like so:
# Creating schema in a cassandra cluster requiring client TLS certificates.
#
# Create a volume for the schema docker container containing four files:
# cqlshrc: this file
# ca-cert: the cert authority for your keys
# client-key: the keyfile for your client
# client-cert: the cert file matching client-key
#
# if there is any sort of DNS mismatch and you want to ignore server validation
# issues, then uncomment validate = false below.
#
# When running the container, map this volume to /root/.cassandra and set the
# environment variable CQLSH_SSL=--ssl
[ssl]
certfile = ~/.cassandra/ca-cert
userkey = ~/.cassandra/client-key
usercert = ~/.cassandra/client-cert
# validate = false
Elasticsearch
Supported in Jaeger since 0.6.0 Supported versions: 5.x, 6.x, 7.x
Elasticsearch version is automatically retrieved from root/ping endpoint.
Based on this version Jaeger uses compatible index mappings and Elasticsearch REST API.
The version can be explicitly provided via --es.version=
flag.
Elasticsearch does not require initialization other than installing and running Elasticsearch . Once it is running, pass the correct configuration values to the Jaeger collector and query service.
Configuration
Minimal
docker run \
-e SPAN_STORAGE_TYPE=elasticsearch \
-e ES_SERVER_URLS=<...> \
jaegertracing/jaeger-collector:1.34.0
All options
To view the full list of configuration options, you can run the following command:
docker run \
-e SPAN_STORAGE_TYPE=elasticsearch \
jaegertracing/jaeger-collector:1.34.0 \
--help
Shards and Replicas for Elasticsearch indices
Shards and replicas are some configuration values to take special attention to, because this is decided upon index creation. This article goes into more information about choosing how many shards should be chosen for optimization.
Elasticsearch Rollover
Elasticsearch rollover
is an index management strategy that optimizes use of resources allocated to indices.
For example, indices that do not contain any data still allocate shards, and conversely, a single index might contain significantly more data than the others.
Jaeger by default stores data in daily indices which might not optimally utilize resources. Rollover feature can be enabled by --es.use-aliases=true
.
Rollover lets you configure when to roll over to a new index based on one or more of the following criteria:
max_age
- the maximum age of the index. It uses time units :d
,h
,m
.max_docs
- the maximum documents in the index.max_size
- the maximum estimated size of primary shards (since Elasticsearch 6.x). It uses byte size unitstb
,gb
,mb
.
Rollover index management strategy is more complex than using the default daily indices and it requires an initialization job to prepare the storage and two cron jobs to manage indices.
To learn more about rollover index management in Jaeger refer to this article .
For automated rollover, please refer to Elasticsearch ILM support .
Initialize
The following command prepares Elasticsearch for rollover deployment by creating index aliases, indices, and index templates:
docker run -it --rm --net=host jaegertracing/jaeger-es-rollover:latest init http://localhost:9200 # <1>
If you need to initialize archive storage, add -e ARCHIVE=true
.
After the initialization Jaeger can be deployed with --es.use-aliases=true
.
Rolling over to a new index
The next step is to periodically execute the rollover API which rolls the write alias to a new index based on supplied conditions. The command also adds a new index to the read alias to make new data available for search.
docker run -it --rm --net=host -e CONDITIONS='{"max_age": "2d"}' jaegertracing/jaeger-es-rollover:latest rollover http://localhost:9200 # <1>
<1> The command rolls the alias over to a new index if the age of the current write index is older than 2 days. For more conditions see Elasticsearch docs .
The next step is to remove old indices from read aliases. It means that old data will not be available for search. This imitates the behavior of --es.max-span-age
flag used in the default index-per-day deployment. This step could be optional and old indices could be simply removed by index cleaner in the next step.
docker run -it --rm --net=host -e UNIT=days -e UNIT_COUNT=7 jaegertracing/jaeger-es-rollover:latest lookback http://localhost:9200 # <1>
<1> Removes indices older than 7 days from read alias.
Remove old data
The historical data can be removed with the jaeger-es-index-cleaner
that is also used for daily indices.
docker run -it --rm --net=host -e ROLLOVER=true jaegertracing/jaeger-es-index-cleaner:latest 14 http://localhost:9200 # <1>
<1> Remove indices older than 14 days.
Elasticsearch ILM support
Experimental feature added in release v1.22.0 .
Supported Elasticsearch versions: 7.x
For example:
- Rollover to a new index by size (bytes or number of documents) or age, archiving previous indices
- Delete stale indices to enforce data retention standards
Enabling ILM support
Create an ILM policy in elasticsearch named jaeger-ilm-policy.
For example, the following policy will rollover the “active” index when it is older than 1m and delete indices that are older than 2m.
curl -X PUT http://localhost:9200/_ilm/policy/jaeger-ilm-policy \ -H 'Content-Type: application/json; charset=utf-8' \ --data-binary @- << EOF { "policy": { "phases": { "hot": { "min_age": "0ms", "actions": { "rollover": { "max_age": "1m" }, "set_priority": { "priority": 100 } } }, "delete": { "min_age": "2m", "actions": { "delete": {} } } } } } EOF
Run elasticsearch initializer with
ES_USE_ILM=true
:docker run -it --rm --net=host -e ES_USE_ILM=true jaegertracing/jaeger-es-rollover:latest init http://localhost:9200 # <1>
<1> If you need to initialize archive storage, add
-e ARCHIVE=true
.While initializing with ILM support, make sure that an ILM policy named
jaeger-ilm-policy
is created in Elasticsearch beforehand (see the previous step), otherwise the following error message will be shown:“ILM policy jaeger-ilm-policy doesn’t exist in Elasticsearch. Please create it and rerun init”
After the initialization, deploy Jaeger with
--es.use-ilm=true
and--es.use-aliases=true
.
Upgrade Elasticsearch version
Elasticsearch defines wire and index compatibility versions. The index compatibility defines the minimal version a node can read data from. For example Elasticsearch 7 can read indices created by Elasticsearch 6, however it cannot read indices created by Elasticsearch 5 even though they use the same index mappings. Therefore upgrade from Elasticsearch 6 to 7 does not require any data migration. However, upgrade from Elasticsearch 5 to 7 has to be done through Elasticsearch 6 and wait until indices created by ES 5.x are removed or explicitly reindexed.
Refer to the Elasticsearch documentation for wire and index compatibility versions. Generally this information can be retrieved from root/ping REST endpoint.
Reindex
Manual reindexing can be used when upgrading from Elasticsearch 5 to 7 (through Elasticsearch 6) without waiting until indices created by Elasticsearch 5 are removed.
Reindex all span indices to new indices with suffix
-1
:curl -ivX POST -H "Content-Type: application/json" http://localhost:9200/_reindex -d @reindex.json { "source": { "index": "jaeger-span-*" }, "dest": { "index": "jaeger-span" }, "script": { "lang": "painless", "source": "ctx._index = 'jaeger-span-' + (ctx._index.substring('jaeger-span-'.length(), ctx._index.length())) + '-1'" } }
Delete indices with old mapping:
curl -ivX DELETE -H "Content-Type: application/json" http://localhost:9200/jaeger-span-\*,-\*-1
Create indices without
-1
suffix:curl -ivX POST -H "Content-Type: application/json" http://localhost:9200/_reindex -d @reindex.json { "source": { "index": "jaeger-span-*" }, "dest": { "index": "jaeger-span" }, "script": { "lang": "painless", "source": "ctx._index = 'jaeger-span-' + (ctx._index.substring('jaeger-span-'.length(), ctx._index.length() - 2))" } }
Remove suffixed indices:
curl -ivX DELETE -H "Content-Type: application/json" http://localhost:9200/jaeger-span-\*-1
Run the commands analogically for other Jaeger indices.
There might exist more effective migration procedure. Please share with the community any findings.
Kafka
Supported in Jaeger since 1.6.0 Supported Kafka versions: 0.9+
Kafka can be used as an intermediary buffer between collector and an actual storage.
The collector is configured with SPAN_STORAGE_TYPE=kafka
that makes it write all received spans
into a Kafka topic. A new component
Ingester
, added in version 1.7.0, is used to read from
Kafka and store spans in another storage backend (Elasticsearch or Cassandra).
Writing to Kafka is particularly useful for building post-processing data pipelines.
Configuration
Minimal
docker run \
-e SPAN_STORAGE_TYPE=kafka \
-e KAFKA_PRODUCER_BROKERS=<...> \
-e KAFKA_TOPIC=<...> \
jaegertracing/jaeger-collector:1.34.0
All options
To view the full list of configuration options, you can run the following command:
docker run \
-e SPAN_STORAGE_TYPE=kafka \
jaegertracing/jaeger-collector:1.34.0 \
--help
Topic & partitions
Unless your Kafka cluster is configured to automatically create topics, you will need to create it ahead of time. You can refer to the Kafka quickstart documentation to learn how.
You can find more information about topics and partitions in general in the official documentation . This article provide more details about how to choose the number of partitions.
Storage plugin
Jaeger supports an extension mechanism that allows the storage to be implemented as a gRPC server. The server can run either as a child process of Jaeger components (the Hashicorp go-plugin model), or as a remote gRPC service (since Jaeger v1.30). For more information, please refer to jaeger/plugin/storage/grpc .
Available plugins:
- InfluxDB - time series database.
- Logz.io - secure, scalable, managed, cloud-based ELK storage.
- ClickHouse - fast open-source OLAP DBMS.
docker run \
-e SPAN_STORAGE_TYPE=grpc-plugin \
-e GRPC_STORAGE_PLUGIN_BINARY=<...> \
-e GRPC_STORAGE_PLUGIN_CONFIGURATION_FILE=<...> \
jaegertracing/all-in-one:1.34.0
Metrics Storage Backends
Jaeger Query is capable of querying aggregated R.E.D metrics from a storage backend, visualizing them on the Monitor tab . It should be emphasized that the configured metrics storage type is for reading only and therefore, only applies to the Jaeger Query component (and All In One, which contains Jaeger Query).
The storage type can be passed via METRICS_STORAGE_TYPE
environment variable.
Valid values are: prometheus
.
Prometheus
Any PromQL-compatible backend is supported by Jaeger Query. A list of these have been compiled by Julius Volz in: https://promlabs.com/blog/2020/11/26/an-update-on-promql-compatibility-across-vendors
Configuration
Minimal
docker run \
-e METRICS_STORAGE_TYPE=prometheus \
jaegertracing/jaeger-query:1.34.0
All options
To view the full list of configuration options, you can run the following command:
docker run \
-e METRICS_STORAGE_TYPE=prometheus \
jaegertracing/jaeger-query:1.34.0 \
--help
TLS support
Jaeger supports TLS client to Prometheus server connections as long as you’ve configured your Prometheus server correctly. You can configure Jaeger Query like so:
docker run \
-e METRICS_STORAGE_TYPE=prometheus \
-e PROMETHEUS_SERVER_URL=<...> \
-e PROMETHEUS_TLS_ENABLED=true \
-e PROMETHEUS_TLS_CA=<path to your CA cert file> \
jaegertracing/jaeger-query:1.34.0
Ingester
jaeger-ingester is a service which reads span data from Kafka topic and writes it to another storage backend (Elasticsearch or Cassandra).
Port | Protocol | Function |
---|---|---|
14270 | HTTP | admin port: health check at / and metrics at /metrics |
To view all exposed configuration options run the following command:
docker run \
-e SPAN_STORAGE_TYPE=cassandra \
jaegertracing/jaeger-ingester:1.34.0
--help
Query Service & UI
jaeger-query serves the API endpoints and a React/Javascript UI. The service is stateless and is typically run behind a load balancer, such as NGINX .
At default settings the query service exposes the following port(s):
Port | Protocol | Function |
---|---|---|
16685 | gRPC | Protobuf/gRPC QueryService |
16686 | HTTP | /api/* endpoints and Jaeger UI at / |
16687 | HTTP | admin port: health check at / and metrics at /metrics |
Minimal deployment example (Elasticsearch backend):
docker run -d --rm \
-p 16685:16685 \
-p 16686:16686 \
-p 16687:16687 \
-e SPAN_STORAGE_TYPE=elasticsearch \
-e ES_SERVER_URLS=http://<ES_SERVER_IP>:<ES_SERVER_PORT> \
jaegertracing/jaeger-query:1.34.0
Clock Skew Adjustment
Jaeger backend combines trace data from applications that are usually running on different hosts. The hardware clocks on the hosts often experience relative drift, known as the clock skew effect . Clock skew can make it difficult to reason about traces, for example, when a server span may appear to start earlier than the client span, which should not be possible. The query service implements a clock skew adjustment algorithm ( code ) to correct for clock drift, using the knowledge about causal relationships between spans. All adjusted spans have a warning displayed in the UI that provides the exact clock skew delta applied to its timestamps.
Sometimes these adjustments themselves make the trace hard to understand. For example, when repositioning the server span within the bounds of its parent span, Jaeger does not know the exact relationship between the request and response latencies, so it assumes then to be equal and places the child span in the middle of the parent span (see issue #961 ).
The query service supports a configuration flag --query.max-clock-skew-adjustment
that controls how much clock skew adjustment should be allowed. Setting this parameter to zero (0s
) disables clock skew adjustment completely. This setting applies to all traces retrieved from the given query service. There is an open
ticket #197
to support toggling the adjustment on and off directly in the UI.
UI Base Path
The base path for all jaeger-query HTTP routes can be set to a non-root value, e.g. /jaeger
would cause all UI URLs to start with /jaeger
. This can be useful when running jaeger-query behind a reverse proxy.
The base path can be configured via the --query.base-path
command line parameter or the QUERY_BASE_PATH
environment variable.
UI Customization and Embedding
Please refer to the dedicated Frontend/UI page .
Aggregation Jobs for Service Dependencies
Production deployments need an external process which aggregates data and creates dependency links between services. Project spark-dependencies is a Spark job which derives dependency links and stores them directly to the storage.