Creating a ClickHouse Table

ClickHouse implements the replicated table mechanism based on the ReplicatedMergeTree engine and ZooKeeper. When creating a table, you can specify an engine to determine whether the table is highly available. Shards and replicas of each table are independent of each other.

ClickHouse also implements the distributed table mechanism based on the Distributed engine. Views are created on all shards (local tables) for distributed query, which is easy to use. ClickHouse has the concept of data sharding, which is one of the features of distributed storage. That is, parallel read and write are used to improve efficiency.

The ClickHouse cluster table engine that uses Kunpeng as the CPU architecture does not support HDFS and Kafka.

Viewing cluster and Other Environment Parameters of ClickHouse

Use the ClickHouse client to connect to the ClickHouse server by referring to Using ClickHouse from Scratch.

Query the cluster identifier and other information about the environment parameters.

select cluster,shard_num,replica_num,host_name from system.clusters;

SELECT 
    cluster, 
    shard_num, 
    replica_num, 
    host_name
FROM system.clusters

┌─cluster───────────┬─shard_num─┬─replica_num─┬─host_name──────── ┐
│ default_cluster_1             │         1   │           1   │ node-master1dOnG           │
│ default_cluster_1             │         1   │           2   │ node-group-1tXED0001       │
│ default_cluster_1             │         2   │           1   │ node-master2OXQS           │
│ default_cluster_1             │         2   │           2   │ node-group-1tXED0002       │
│ default_cluster_1             │         3   │           1   │ node-master3QsRI           │
│ default_cluster_1             │         3   │           2   │ node-group-1tXED0003       │
└─────────────── ┴────── ┴─────── ┴──────────────┘

6 rows in set. Elapsed: 0.001 sec.

Query the shard and replica identifiers.

select * from system.macros;

SELECT *
FROM system.macros

┌─macro───┬─substitution─────┐
│ id          │ 76                     │
│ replica     │ 2                      │
│ shard       │ 3                      │
└────── ┴────────────┘

3 rows in set. Elapsed: 0.001 sec.

Creating a Local Replicated Table and a distributed Table

Log in to the ClickHouse node using the client, for example, clickhouse client --host node-master3QsRI --multiline --port 9440 --secure;

node-master3QsRI is the value of host_name obtained in 2 in Viewing cluster and Other Environment Parameters of ClickHouse.

Create a replicated table using the ReplicatedMergeTree engine.

For example, run the following commands to create a ReplicatedMergeTree table named test on the default_cluster_1 node and in the default database:

CREATE TABLE default.test ON CLUSTER default_cluster_1

(

`EventDate` DateTime,

`id` UInt64

)

ENGINE = ReplicatedMergeTree('/clickhouse/tables/{shard}/default/test', '{replica}')

PARTITION BY toYYYYMM(EventDate)

ORDER BY id;

The parameters are described as follows:

The ON CLUSTER syntax indicates the distributed DDL, that is, the same local table can be created on all instances in the cluster after the statement is executed once.
default_cluster_1 is the cluster identifier obtained in 2 in Viewing cluster and Other Environment Parameters of ClickHouse.
ReplicatedMergeTree engine receives the following two parameters:
- Storage path of the table data in ZooKeeper
  The path must be in the /clickhouse directory. Otherwise, data insertion may fail due to insufficient ZooKeeper quota.
  
  To avoid data conflict between different tables in ZooKeeper, the directory must be in the following format:
  
  /clickhouse/tables/{shard}/default/test, in which /clickhouse/tables/{shard} is fixed, default indicates the database name, and text indicates the name of the created table.
- Replica name: Generally, {replica} is used.

CREATE TABLE default.test ON CLUSTER default_cluster_1
(
    `EventDate` DateTime, 
    `id` UInt64
)
ENGINE = ReplicatedMergeTree('/clickhouse/tables/{shard}/default/test', '{replica}')
PARTITION BY toYYYYMM(EventDate)
ORDER BY id

┌─host─────────────────┬─port─┬─status─┬─error─┬─num_hosts_remaining─┬─num_hosts_active─┐
│ node-group-1tXED0002                   │  9000  │      0   │         │                   5   │                3   │
│ node-group-1tXED0003                   │  9000  │      0   │         │                   4   │                3   │
│ node-master1dOnG                       │  9000  │      0   │         │                   3   │                3   │
└────────────────────┴────┴─────┴──── ┴─────────── ┴──────────┘
┌─host─────────────────┬─port─┬─status─┬─error─┬─num_hosts_remaining─┬─num_hosts_active─┐
│ node-master3QsRI                       │  9000  │      0   │         │                   2   │                0   │
│ node-group-1tXED0001                   │  9000  │      0   │         │                   1   │                0   │
│ node-master2OXQS                       │  9000  │      0   │         │                   0   │                0   │
└────────────────────┴────┴─────┴──── ┴─────────── ┴──────────┘

6 rows in set. Elapsed: 0.189 sec.

Create a distributed table using the Distributed engine.

For example, run the following commands to create a distributed table named test_all on the default_cluster_1 node and in the default database:

CREATE TABLE default.test_all ON CLUSTER default_cluster_1

(

`EventDate` DateTime,

`id` UInt64

)

ENGINE = Distributed(default_cluster_1, default, test, rand());

CREATE TABLE default.test_all ON CLUSTER default_cluster_1
(
    `EventDate` DateTime, 
    `id` UInt64
)
ENGINE = Distributed(default_cluster_1, default, test, rand())

┌─host─────────────────┬─port─┬─status─┬─error─┬─num_hosts_remaining─┬─num_hosts_active─┐
│ node-group-1tXED0002                   │  9000  │      0   │         │                   5   │                0   │
│ node-master3QsRI                       │  9000  │      0   │         │                   4   │                0   │
│ node-group-1tXED0003                   │  9000  │      0   │         │                   3   │                0   │
│ node-group-1tXED0001                   │  9000  │      0   │         │                   2   │                0   │
│ node-master1dOnG                       │  9000  │      0   │         │                   1   │                0   │
│ node-master2OXQS                       │  9000  │      0   │         │                   0   │                0   │
└────────────────────┴────┴─────┴──── ┴─────────── ┴──────────┘

6 rows in set. Elapsed: 0.115 sec.

Distributed requires the following parameters:

default_cluster_1 is the cluster identifier obtained in 2 in Viewing cluster and Other Environment Parameters of ClickHouse.
default indicates the name of the database where the local table is located.
test indicates the name of the local table. In this example, it is the name of the table created in 2.
(Optional) Sharding key
This key and the weight configured in the config.xml file determine the route for writing data to the distributed table, that is, the physical table to which the data is written. It can be the original data (for example, site_id) of a column in the table or the result of the function call, for example, rand() is used in the preceding SQL statement. Note that data must be evenly distributed in this key. Another common operation is to use the hash value of a column with a large difference, for example, intHash64(user_id).

ClickHouse Table Data Operations

Log in to the ClickHouse node on the client. Example:

clickhouse client --host node-master3QsRI --multiline --port 9440 --secure;

node-master3QsRI is the value of host_name obtained in 2 in Viewing cluster and Other Environment Parameters of ClickHouse.
After creating a table by referring to Creating a Local Replicated Table and a distributed Table, you can insert data to the local table.

For example, run the following command to insert data to the local table test:

insert into test values(toDateTime(now()), rand());

Query the local table information.

For example, run the following command to query data information of the table test in 2:

select * from test;

SELECT *
FROM test

┌───────────EventDate─┬─────────id─┐
│ 2020-11-05 21:10:42             │ 1596238076           │
└──────────────── ┴───────────┘

1 rows in set. Elapsed: 0.002 sec.

Query the distributed table.

For example, the distributed table test_all is created based on table test in 3. Therefore, the same data in table test can also be queried in table test_all.

select * from test_all;

SELECT *
FROM test_all

┌───────────EventDate─┬─────────id─┐
│ 2020-11-05 21:10:42             │ 1596238076           │
└──────────────── ┴───────────┘

1 rows in set. Elapsed: 0.004 sec.

Switch to the shard node with the same shard_num and query the information about the current table. The same table data can be queried.

For example, run the exit; command to exit the original node.

Run the following command to switch to the node-group-1tXED0003 node:

clickhouse client --host node-group-1tXED0003 --multiline --port 9440 --secure;

The shard_num values of node-group-1tXED0003 and node-master3QsRI are the same by performing 2.

show tables;
```
SHOW TABLES

┌─name─────┐
│ test           │
│ test_all       │
└────────┘
 
```

Query the local table data. For example, run the following command to query data in table test on the node-group-1tXED0003 node:

select * from test;

SELECT *
FROM test

┌───────────EventDate─┬─────────id─┐
│ 2020-11-05 21:10:42             │ 1596238076           │
└──────────────── ┴───────────┘

1 rows in set. Elapsed: 0.005 sec.

Switch to the shard node with different shard_num value and query the data of the created table.

For example, run the following command to exit the node-group-1tXED0003 node:

exit;

Switch to the node-group-1tXED0001 node. The shard_num values of node-group-1tXED0001 and node-master3QsRI are different by performing 2.

clickhouse client --host node-group-1tXED0001 --multiline --port 9440 --secure;

Query the local table test. Data cannot be queried on the different shard node because table test is a local table.

select * from test;
```
SELECT *
FROM test

Ok.
```
Query data in the distributed table test_all. The data can be queried properly.

select * from test_all;
```
SELECT *
FROM test

┌───────────EventDate─┬─────────id─┐
│ 2020-11-05 21:12:19             │ 3686805070           │
└──────────────── ┴───────────┘

1 rows in set. Elapsed: 0.002 sec. 
 
```