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Quickstart with Docker-Compose ​

Requirement ​

For optimal performance, we suggest utilizing a Linux kernel version of 4.14 or higher when initializing an HStreamDB Cluster.

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In the case it is not possible for the user to use a Linux kernel version of 4.14 or above, we recommend adding the option --enable-dscp-reflection=false to HStore while starting the HStreamDB Cluster.

Installation ​

Install docker ​

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If you have already installed docker, you can skip this step.

See Install Docker Engine, and install it for your operating system. Please carefully check that you have met all prerequisites.

Confirm that the Docker daemon is running:

sh
docker version

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On Linux, Docker needs root privileges. You can also run Docker as a non-root user, see Post-installation steps for Linux.

Install docker compose ​

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If you have already installed docker compose, you can skip this step.

See Install Docker Compose, and install it for your operating system. Please carefully check that you met all prerequisites.

sh
docker-compose version

Start HStreamDB Services ​

WARNING

Do NOT use this configuration in your production environment!

Create a docker-compose.yaml file for docker compose, you can download or paste the following contents:

yaml
version: "3.5"

services:
  hserver:
    image: hstreamdb/hstream:latest
    depends_on:
      - zookeeper
      - hstore
    ports:
      - "127.0.0.1:6570:6570"
    expose:
      - 6570
    networks:
      - hstream-quickstart
    volumes:
      - /var/run/docker.sock:/var/run/docker.sock
      - /tmp:/tmp
      - data_store:/data/store
    command:
      - bash
      - "-c"
      - |
        set -e
        /usr/local/script/wait-for-storage.sh hstore 6440 zookeeper 2181 600 \
        /usr/local/bin/hstream-server \
        --bind-address 0.0.0.0 --port 6570 \
        --internal-port 6571 \
        --server-id 100 \
        --seed-nodes "$$(hostname -I | awk '{print $$1}'):6571" \
        --advertised-address $$(hostname -I | awk '{print $$1}') \
        --metastore-uri zk://zookeeper:2181 \
        --store-config /data/store/logdevice.conf \
        --store-admin-host hstore --store-admin-port 6440 \
        --store-log-level warning \
        --io-tasks-path /tmp/io/tasks \
        --io-tasks-network hstream-quickstart

  hstore:
    image: hstreamdb/hstream:latest
    networks:
      - hstream-quickstart
    volumes:
      - data_store:/data/store
    command:
      - bash
      - "-c"
      - |
        set -ex
        # N.B. "enable-dscp-reflection=false" is required for linux kernel which
        # doesn't support dscp reflection, e.g. centos7.
        /usr/local/bin/ld-dev-cluster --root /data/store \
        --use-tcp --tcp-host $$(hostname -I | awk '{print $$1}') \
        --user-admin-port 6440 \
        --param enable-dscp-reflection=false \
        --no-interactive

  zookeeper:
    image: zookeeper
    expose:
      - 2181
    networks:
      - hstream-quickstart
    volumes:
      - data_zk_data:/data
      - data_zk_datalog:/datalog

networks:
  hstream-quickstart:
    name: hstream-quickstart

volumes:
  data_store:
    name: quickstart_data_store
  data_zk_data:
    name: quickstart_data_zk_data
  data_zk_datalog:
    name: quickstart_data_zk_datalog

then run:

sh
docker-compose -f quick-start.yaml up

If you see some thing like this, then you have a running hstream:

txt
hserver_1    | [INFO][2021-11-22T09:15:18+0000][app/server.hs:137:3][thread#67]************************
hserver_1    | [INFO][2021-11-22T09:15:18+0000][app/server.hs:145:3][thread#67]Server started on port 6570
hserver_1    | [INFO][2021-11-22T09:15:18+0000][app/server.hs:146:3][thread#67]*************************

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You can also run in background.

sh
docker-compose -f quick-start.yaml up -d

And if you want to show logs of server, run:

sh
docker-compose -f quick-start.yaml logs -f hserver

Start HStreamDB's interactive SQL CLI ​

sh
docker run -it --rm --name some-hstream-cli --network host hstreamdb/hstream:latest hstream --port 6570 sql

If everything works fine, you will enter an interactive CLI and see help information like

txt
__  _________________  _________    __  ___
     / / / / ___/_  __/ __ \/ ____/   |  /  |/  /
    / /_/ /\__ \ / / / /_/ / __/ / /| | / /|_/ /
   / __  /___/ // / / _, _/ /___/ ___ |/ /  / /
  /_/ /_//____//_/ /_/ |_/_____/_/  |_/_/  /_/

Command
  :h                           To show these help info
  :q                           To exit command line interface
  :help [sql_operation]        To show full usage of sql statement

SQL STATEMENTS:
  To create a simplest stream:
    CREATE STREAM stream_name;

  To create a query select all fields from a stream:
    SELECT * FROM stream_name EMIT CHANGES;

  To insert values to a stream:
    INSERT INTO stream_name (field1, field2) VALUES (1, 2);

>

Create a stream ​

What we are going to do first is create a stream by CREATE STREAM statement.

sql
CREATE STREAM demo;

Run a continuous query over the stream ​

Now we can run a continuous query over the stream we just created by SELECT query.

The query will output all records from the demo stream whose humidity is above 70 percent.

sql
SELECT * FROM demo WHERE humidity > 70 EMIT CHANGES;

It seems that nothing happened. But do not worry because there is no data in the stream now. Next, we will fill the stream with some data so the query can produce output we want.

Start another CLI session ​

Start another CLI session, this CLI will be used for inserting data into the stream.

sh
docker exec -it some-hstream-cli hstream --port 6570 sql

Insert data into the stream ​

Run each of the given INSERT statement in the new CLI session and keep an eye on the CLI session created in (2).

sql
INSERT INTO demo (temperature, humidity) VALUES (22, 80);
INSERT INTO demo (temperature, humidity) VALUES (15, 20);
INSERT INTO demo (temperature, humidity) VALUES (31, 76);
INSERT INTO demo (temperature, humidity) VALUES ( 5, 45);
INSERT INTO demo (temperature, humidity) VALUES (27, 82);
INSERT INTO demo (temperature, humidity) VALUES (28, 86);

If everything works fine, the continuous query will output matching records in real time:

json
{"temperature":22,"humidity":80}
{"temperature":31,"humidity":76}
{"temperature":27,"humidity":82}
{"temperature":28,"humidity":86}