ClickHouse Connector
Overview
The ClickHouse connector allows querying on an external ClickHouse database. This can be used to join data between different systems like ClickHouse and Hive, or between two different ClickHouse instances.
Configuration
Basic configuration
To configure the ClickHouse connector, create a catalog properties file inetc/catalog
named, for example, clickhouse.properties
, to mount the ClickHouse connector as the clickhouse
catalog. Create the file with the following
contents, replacing the connection properties as appropriate for your setup.
Base property setting:
connector.name=clickhouse
connection-url=jdbc:clickhouse://example.net:8123
connection-user=username
connection-password=yourpassword
- Allow ClickHouse connector to drop table or not
allow-drop-table=true
- Enable the query push down feature or not
The push down feature of ClickHouse connector is turn on by default, and you can also set as below:
clickhouse.query.pushdown.enabled=true
- Table name is case sensitive or not.
The syntax of ClickHouse is case sensitive. If there are uppercase fields in your database table, you can set them as follows.
case-insensitive-name-matching=true
Multiple ClickHouse Servers
You can have as many catalogs as you need, so if you have additional ClickHouse servers, simply add another properties file to etc/catalog
with a different name (making sure it ends in .properties
). For example, if
you name the property file clickhouse2.properties
, openLooKeng will create a catalog named clickhouse2
using the configured connector.
Querying ClickHouse through openLooKeng
The ClickHouse connector provides a schema for every ClickHouse database. You can see the available ClickHouse databases by running SHOW SCHEMAS
:
SHOW SCHEMAS FROM clickhouse;
If you have a ClickHouse database named data
, you can view the tables in this database by running SHOW TABLES
:
SHOW TABLES FROM clickhouse.data;
You can see a list of the columns in the hello
table in the data
database using either of the following:
DESCRIBE clickhouse.data.hello;
SHOW COLUMNS FROM clickhouse.data.hello;
Finally, you can also access the hello
table in the data
database:
SELECT * FROM clickhouse.data.hello;
If you used a different name for your catalog properties file, use that catalog name instead of clickhouse
in the above examples.
Mapping Data Types Between openLooKeng and ClickHouse
ClickHouse-to-openLooKeng Type Mapping
openLooKeng support selecting the following ClickHouse Detabase types. The table shows the mapping from ClickHouse data type.
Data type projection table:
ClickHouse type | openLooKeng type |
---|---|
Int8 | TINYINT |
Int16 | SMALLINT |
Int32 | INTEGER |
Int64 | BIGINT |
float32 | REAL |
float64 | DOUBLE |
DECIMAL(P,S) | DECIMAL(P,S) |
DECIMAL32(S) | DECIMAL(P,S) |
DECIMAL64(S) | DECIMAL(P,S) |
DECIMAL128(S) | DECIMAL(P,S) |
String | VARCHAR |
DateTime | TIME |
Fixedstring(N) | CHAR |
UInt8 | SMALLINT |
UInt16 | INT |
UInt32 | BIGINT |
UInt64 | NA |
Int128,Int256,UInt256 | NA |
openLooKeng-to-ClickHouse Type Mapping
openLooKeng support creating tables with the following type into a ClickHouse Database. The table shows the mapping from openLooKeng to ClickHouse data types.
openLooKeng type | ClickHouse type |
---|---|
BOOLEAN | Int8 |
TINYINT | Int8 |
SMALLINT | Int16 |
INTEGER | Int32 |
BIGINT | Int64 |
REAL | float32 |
DOUBLE | float64 |
DECIMAL(P,S) | DECIMAL(P,S) |
varchar | String |
varchar(n) | String |
CHAR(n) | FixedString(n) |
VARBINARY | String |
JSON | NA |
DATE | Date |
TIME | DateTime |
TIME WITH TIME ZONE | NA |
TIMESTAMP | TIMESTAMP |
TIMESTAMP WITH TIME ZONE | NA |
Functions that support pushdown
Note: The "$n" is placeholder to present an argument in a function.
Aggregate Functions
count($1)
min($1)
max($1)
sum($1)
avg($1)
CORR($1,$2)
STDDEV($1)
stddev_pop($1)
stddev_samp($1)
skewness($1)
kurtosis($1)
VARIANCE($1)
var_samp($1)
Math functions
ABS($1)
ACOS($1)
ASIN($1)
ATAN($1)
ATAN2($1,$2)
CEIL($1)
CEILING($1)
COS($1)
e()
EXP($1)
FLOOR($1)
LN($1)
LOG10($1)
LOG2($1)
MOD($1,$2)
pi()
POW($1,$2)
POWER($1,$2)
RAND()
RANDOM()
ROUND($1)
ROUND($1,$2)
SIGN($1)
SIN($1)
SQRT($1)
TAN($1)
Functions for Working with Strings
CONCAT($1,$2)
LENGTH($1)
LOWER($1)
LTRIM($1)
REPLACE($1,$2)
REPLACE($1,$2,$3)
RTRIM($1)
STRPOS($1,$2)
SUBSTR($1,$2,$3)
POSITION($1,$2)
TRIM($1)
UPPER($1)
Functions for Working with Dates and Times
YEAR($1)
MONTH($1)
QUARTER($1)
WEEK($1)
DAY($1)
HOUR($1)
MINUTE($1)
SECOND($1)
DAY_OF_WEEK($1)
DAY_OF_MONTH($1)
DAY_OF_YEAR($1)
Note: The functions supported by openLooKeng can also be used in the ClickHouse connector, but functions not in the above list will not be pushed down.
ClickHouse Connector Limitations
Syntax
CREATE TABLE statement is not supported.
The INSERT statement needs to use CAST, for example, the data type in the table_name_test table is smallint:
insert into table_name_test values (cast(1 as small int));
The ClickHouse syntax supports the use of aliases in where clauses, but not in openLooKeng.
Type
Types such as uuid in ClickHouse are not supported, and all supported types are listed in the mapping table.