- Docker >= 17.03
- Docker Compose >= 1.6.0
Note: Legacy Docker Toolbox users must migrate to Docker for Mac, since it is tested that tidb-docker-compose cannot be started on Docker Toolbox and Docker Machine.
$ git clone https://github.com/pingcap/tidb-docker-compose.git
$ cd tidb-docker-compose && docker-compose pull # Get the latest Docker images
$ docker-compose up -d
$ mysql -h 127.0.0.1 -P 4000 -u root
- Access monitor at http://localhost:3000
Default user/password: admin/admin
-
Access tidb-vision at http://localhost:8010
-
Access Spark Web UI at http://localhost:8080 and access TiSpark through spark://127.0.0.1:7077
- config/pd.toml is copied from PD repo
- config/tikv.toml is copied from TiKV repo
- config/tidb.toml is copied from TiDB repo
If you find these configuration files outdated or mismatch with TiDB version, you can copy these files from their upstream repos and change their metrics addr with pushgateway:9091
. Also max-open-files
are configured to 1024
in tikv.toml to simplify quick start on Linux, because setting up ulimit on Linux with docker is quite tedious.
And config/*-dashboard.json are copied from TiDB-Ansible repo
You can customize TiDB cluster configuration by editing docker-compose.yml and the above config files if you know what you're doing.
But edit these files manually is tedious and error-prone, a template engine is strongly recommended. See the following steps
Helm is used as a template render engine
curl https://raw.githubusercontent.com/kubernetes/helm/master/scripts/get | bash
Or if you use Mac, you can use homebrew to install Helm by brew install kubernetes-helm
$ git clone https://github.com/pingcap/tidb-docker-compose.git
$ cd tidb-docker-compose
$ vi compose/values.yaml # custom cluster size, docker image, port mapping etc
$ helm template compose > generated-docker-compose.yaml
$ docker-compose -f generated-docker-compose.yaml pull # Get the latest Docker images
$ docker-compose -f generated-docker-compose.yaml up -d
You can build docker image yourself for development test.
-
Build from binary
For pd, tikv and tidb, comment their
image
andbuildPath
fields out. And then copy their binary files to pd/bin/pd-server, tikv/bin/tikv-server and tidb/bin/tidb-server.These binary files can be built locally or downloaded from https://download.pingcap.org/tidb-latest-linux-amd64.tar.gz
For tidbVision, comment its
image
andbuildPath
fields out. And then copy tidb-vision repo to tidb-vision/tidb-vision. -
Build from source
Leave pd, tikv, tidb and tidbVision
image
field empty and set theirbuildPath
field to their source directory.For example, if your local tikv source directory is $GOPATH/src/github.com/pingcap/tikv, just set tikv
buildPath
to$GOPATH/src/github.com/pingcap/tikv
Note: Compiling tikv from source consumes lots of memory, memory of Docker for Mac needs to be adjusted to greater than 6GB
tidb-vision is a visiualization page of TiDB Cluster, it's WIP project and can be disabled by commenting tidbVision
out.
TiSpark is a thin layer built for running Apache Spark on top of TiDB/TiKV to answer the complex OLAP queries.
Note: Docker for Mac uses a Linux virtual machine, host network mode will not expose any services to host machine. So it's useless to use this mode.
When using TiKV directly without TiDB, host network mode must be enabled. This way all services use host network without isolation. So you can access all services on the host machine.
You can enable this mode by setting networkMode: host
in compose/values.yaml and regenerate docker-compose.yml. When in this mode, prometheus address in configuration files should be changed from prometheus:9090
to 127.0.0.1:9090
, and pushgateway address should be changed from pushgateway:9091
to 127.0.0.1:9091
.
These modification can be done by:
# Note: this only needed when networkMode is `host`
sed -i 's/pushgateway:9091/127.0.0.1:9091/g' config/*
sed -i 's/prometheus:9090/127.0.0.1:9090/g' config/*
After all the above is done, you can start tidb-cluster as usual by docker-compose -f generated-docker-compose.yml up -d
Prerequisites:
Pprof: This is a tool for visualization and analysis of profiling data. Follow these instructions to install pprof.
Graphviz: http://www.graphviz.org/, used to generate graphic visualizations of profiles.
- debug TiDB or PD instances
### Use the following command to starts a web server for graphic visualizations of golang program profiles
$ ./tool/container_debug -s pd0 -p /pd-server -w
The above command will produce graphic visualizations of profiles of pd0
that can be accessed through the browser.
- debug TiKV instances
### step 1: select a tikv instance(here is tikv0) and specify the binary path in container to enter debug container
$ ./tool/container_debug -s tikv0 -p /tikv-server
### after step 1, we can generate flame graph for tikv0 in debug container
$ ./run_flamegraph.sh 1 # 1 is the tikv0's process id
### also can fetch tikv0's stack informations with GDB in debug container
$ gdb /tikv-server 1 -batch -ex "thread apply all bt" -ex "info threads"
TiDB uses ports: 4000(mysql) and 10080(status) by default
$ mysql -h 127.0.0.1 -P 4000 -u root
And Grafana uses port 3000 by default, so open your browser at http://localhost:3000 to view monitor dashboard
If you enabled tidb-vision, you can view it at http://localhost:8010
Insert some sample data to the TiDB cluster:
$ docker-compose exec tispark-master bash
$ cd /opt/spark/data/tispark-sample-data
$ mysql -h tidb -P 4000 -u root < dss.ddl
After the sample data is loaded into the TiDB cluster, you can access Spark Shell by docker-compose exec tispark-master /opt/spark/bin/spark-shell
.
$ docker-compose exec tispark-master /opt/spark/bin/spark-shell
...
Spark context available as 'sc' (master = local[*], app id = local-1527045927617).
Spark session available as 'spark'.
Welcome to
____ __
/ __/__ ___ _____/ /__
_\ \/ _ \/ _ `/ __/ '_/
/___/ .__/\_,_/_/ /_/\_\ version 2.1.1
/_/
Using Scala version 2.11.8 (Java HotSpot(TM) 64-Bit Server VM, Java 1.8.0_172)
Type in expressions to have them evaluated.
Type :help for more information.
scala> import org.apache.spark.sql.TiContext
...
scala> val ti = new TiContext(spark)
...
scala> ti.tidbMapDatabase("TPCH_001")
...
scala> spark.sql("select count(*) from lineitem").show
+--------+
|count(1)|
+--------+
| 60175|
+--------+
You can also access Spark with Python or R using the following commands:
docker-compose exec tispark-master /opt/spark/bin/pyspark
docker-compose exec tispark-master /opt/spark/bin/sparkR
More documents about TiSpark can be found here.