A python wrapper for the KSQL REST API. Easily interact with the KSQL REST API using this library.
Supported KSQLDB version: 0.10.1+ Supported Python version: 3.5+
pip install ksqlOr
git clone https://github.com/bryanyang0528/ksql-python
cd ksql-python
python setup.py installThis is the GITHUB page of KSQL. https://github.com/confluentinc/ksql
If you have installed open source Confluent CLI (e.g. by installing Confluent Open Source or Enterprise Platform), you can start KSQL and its dependencies with one single command:
confluent start ksql-server- Setup for the KSQL API:
from ksql import KSQLAPI
client = KSQLAPI('http://ksql-server:8088')- Setup for KSQl API with logging enabled:
import logging
from ksql import KSQLAPI
logging.basicConfig(level=logging.DEBUG)
client = KSQLAPI('http://ksql-server:8088')- Setup for KSQL API with Basic Authentication
from ksql import KSQLAPI
client = KSQLAPI('http://ksql-server:8088', api_key="your_key", secret="your_secret")| Option | Type | Required | Description |
|---|---|---|---|
url |
string | yes | Your ksql-server url. Example: http://ksql-server:8080 |
timeout |
integer | no | Timout for Requests. Default: 5 |
api_key |
string | no | API Key to use on the requests |
secret |
string | no | Secret to use on the requests |
This method can be used for some KSQL features which are not supported via other specific methods like query, create_stream or create_stream_as. The following example shows how to execute the show tables statement:
client.ksql('show tables')- Example Response
[{'tables': {'statementText': 'show tables;', 'tables': []}}]
It will execute sql query and keep listening streaming data.
client.query('select * from table1')This command returns a generator. It can be printed e.g. by reading its values via next(query) or a for loop. Here is a complete example:
from ksql import KSQLAPI
client = KSQLAPI('http://localhost:8088')
query = client.query('select * from table1')
for item in query: print(item)Example Response
{"row":{"columns":[1512787743388,"key1",1,2,3]},"errorMessage":null} {"row":{"columns":[1512787753200,"key1",1,2,3]},"errorMessage":null} {"row":{"columns":[1512787753488,"key1",1,2,3]},"errorMessage":null} {"row":{"columns":[1512787753888,"key1",1,2,3]},"errorMessage":null}
Execute queries with the new /query-stream endpoint. Documented here
To execute a sql query use the same syntax as the regular query, with the additional use_http2=True parameter.
client.query('select * from table1', use_http2=True)A generator is returned with the following example response
{"queryId":"44d8413c-0018-423d-b58f-3f2064b9a312","columnNames":["ORDER_ID","TOTAL_AMOUNT","CUSTOMER_NAME"],"columnTypes":["INTEGER","DOUBLE","STRING"]} [3,43.0,"Palo Alto"] [3,43.0,"Palo Alto"] [3,43.0,"Palo Alto"]
To terminate the query above use the close_query call. Provide the queryId returned from the query call.
client.close_query("44d8413c-0018-423d-b58f-3f2064b9a312")Uses the new /inserts-stream endpoint. See documentation
rows = [
{"ORDER_ID": 1, "TOTAL_AMOUNT": 23.5, "CUSTOMER_NAME": "abc"},
{"ORDER_ID": 2, "TOTAL_AMOUNT": 3.7, "CUSTOMER_NAME": "xyz"}
]
results = self.api_client.inserts_stream("my_stream_name", rows)An array of object will be returned on success, with the status of each row inserted.
client.create_stream(table_name=table_name,
columns_type=columns_type,
topic=topic,
value_format=value_format)| Option | Type | Required | Description |
|---|---|---|---|
table_name |
string | yes | name of stream/table |
columns_type |
list | yes | ex:['viewtime bigint','userid varchar','pageid varchar'] |
topic |
string | yes | Kafka topic |
value_format |
string | no | JSON (Default) or DELIMITED or AVRO |
key |
string | for Table | Key (used for JOINs) |
- Responses
- If create table/stream succeed
return True
- If failed
raise a CreateError(respose_from_ksql_server)
a simplified api for creating stream as select
client.create_stream_as(table_name=table_name,
select_columns=select_columns,
src_table=src_table,
kafka_topic=kafka_topic,
value_format=value_format,
conditions=conditions,
partition_by=partition_by,
**kwargs)CREATE STREAM <table_name>
[WITH ( kafka_topic=<kafka_topic>, value_format=<value_format>, property_name=expression ... )]
AS SELECT <select_columns>
FROM <src_table>
[WHERE <conditions>]
PARTITION BY <partition_by>];| Option | Type | Required | Description |
|---|---|---|---|
table_name |
string | yes | name of stream/table |
select_columns |
list | yes | you can select [*] or ['columnA', 'columnB'] |
src_table |
string | yes | name of source table |
kafka_topic |
string | no | The name of the Kafka topic of this new stream(table). |
value_format |
string | no | DELIMITED, JSON(Default) or AVRO |
conditions |
string | no | The conditions in the where clause. |
partition_by |
string | no | Data will be distributed across partitions by this column. |
kwargs |
pair | no | please provide key=value pairs. Please see more options. |
KSQL JOINs between Streams and Tables are not supported yet via explicit methods, but you can use the ksql method for this like the following:
client.ksql("CREATE STREAM join_per_user WITH (VALUE_FORMAT='AVRO', KAFKA_TOPIC='join_per_user') AS SELECT Time, Amount FROM source c INNER JOIN users u on c.user = u.userid WHERE u.USERID = 1")Run commands from a .ksql file. Can only support ksql commands and not streaming queries.
from ksql.upload import FileUpload
pointer = FileUpload('http://ksql-server:8080')
pointer.upload('rules.ksql')| Option | Type | Required | Description |
|---|---|---|---|
ksqlfile |
string | yes | name of file containing the rules |
- Responses
- If ksql-commands succesfully executed
return (List of server response for all commands)
- If failed
raise the appropriate error
There are more properties (partitions, replicas, etc...) in the official document.
- Responses
- If create table/stream succeed
return True
- If failed
raise a CreatError(respose_from_ksql_server)
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