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ksql | database purposebuilt for stream processing applications | Stream Processing library

 by   confluentinc Java Version: v0.6.0-docs License: Non-SPDX

 by   confluentinc Java Version: v0.6.0-docs License: Non-SPDX

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kandi X-RAY | ksql Summary

ksql is a Java library typically used in Data Processing, Stream Processing, MongoDB, Kafka applications. ksql has no bugs, it has no vulnerabilities, it has build file available and it has high support. However ksql has a Non-SPDX License. You can download it from GitHub.
ksqlDB is a database for building stream processing applications on top of Apache Kafka. It is distributed, scalable, reliable, and real-time. ksqlDB combines the power of real-time stream processing with the approachable feel of a relational database through a familiar, lightweight SQL syntax. ksqlDB offers these core primitives:.
Support
Support
Quality
Quality
Security
Security
License
License
Reuse
Reuse

kandi-support Support

  • ksql has a highly active ecosystem.
  • It has 4892 star(s) with 932 fork(s). There are 359 watchers for this library.
  • It had no major release in the last 12 months.
  • There are 1048 open issues and 1897 have been closed. On average issues are closed in 271 days. There are 111 open pull requests and 0 closed requests.
  • It has a positive sentiment in the developer community.
  • The latest version of ksql is v0.6.0-docs
ksql Support
Best in #Stream Processing
Average in #Stream Processing
ksql Support
Best in #Stream Processing
Average in #Stream Processing

quality kandi Quality

  • ksql has 0 bugs and 0 code smells.
ksql Quality
Best in #Stream Processing
Average in #Stream Processing
ksql Quality
Best in #Stream Processing
Average in #Stream Processing

securitySecurity

  • ksql has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.
  • ksql code analysis shows 0 unresolved vulnerabilities.
  • There are 0 security hotspots that need review.
ksql Security
Best in #Stream Processing
Average in #Stream Processing
ksql Security
Best in #Stream Processing
Average in #Stream Processing

license License

  • ksql has a Non-SPDX License.
  • Non-SPDX licenses can be open source with a non SPDX compliant license, or non open source licenses, and you need to review them closely before use.
ksql License
Best in #Stream Processing
Average in #Stream Processing
ksql License
Best in #Stream Processing
Average in #Stream Processing

buildReuse

  • ksql releases are available to install and integrate.
  • Build file is available. You can build the component from source.
  • Installation instructions, examples and code snippets are available.
  • ksql saves you 158782 person hours of effort in developing the same functionality from scratch.
  • It has 163174 lines of code, 13333 functions and 1778 files.
  • It has medium code complexity. Code complexity directly impacts maintainability of the code.
ksql Reuse
Best in #Stream Processing
Average in #Stream Processing
ksql Reuse
Best in #Stream Processing
Average in #Stream Processing
Top functions reviewed by kandi - BETA

kandi has reviewed ksql and discovered the below as its top functions. This is intended to give you an instant insight into ksql implemented functionality, and help decide if they suit your requirements.

  • Build the config definition .
  • Adds a Kafka topic to the configuration .
  • Setup the router .
  • Returns the function type information for the given function call .
  • Executes a pull - query .
  • Creates a standard deviation implementation .
  • Executes KSQL statement .
  • Builds the aggregation schema .
  • Resolves generics .
  • Handles KSQL statements .

ksql Key Features

Streams and tables - Create relations with schemas over your Apache Kafka topic data

Materialized views - Define real-time, incrementally updated materialized views over streams using SQL

Push queries- Continuous queries that push incremental results to clients in real time

Pull queries - Query materialized views on demand, much like with a traditional database

Connect - Integrate with any Kafka Connect data source or sink, entirely from within ksqlDB

Materialized views

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CREATE TABLE hourly_metrics AS
  SELECT url, COUNT(*)
  FROM page_views
  WINDOW TUMBLING (SIZE 1 HOUR)
  GROUP BY url EMIT CHANGES;

Streaming ETL

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CREATE STREAM vip_actions AS
  SELECT userid, page, action
  FROM clickstream c
  LEFT JOIN users u ON c.userid = u.user_id
  WHERE u.level = 'Platinum' EMIT CHANGES;

Anomaly Detection

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CREATE TABLE possible_fraud AS
  SELECT card_number, count(*)
  FROM authorization_attempts
  WINDOW TUMBLING (SIZE 5 SECONDS)
  GROUP BY card_number
  HAVING count(*) > 3 EMIT CHANGES;

Monitoring

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CREATE TABLE error_counts AS
  SELECT error_code, count(*)
  FROM monitoring_stream
  WINDOW TUMBLING (SIZE 1 MINUTE)
  WHERE  type = 'ERROR'
  GROUP BY error_code EMIT CHANGES;

Integration with External Data Sources and Sinks

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CREATE STREAM clicks_transformed AS
  SELECT userid, page, action
  FROM clickstream c
  LEFT JOIN users u ON c.userid = u.user_id EMIT CHANGES;

How to create an output stream (changelog) based on a table in KSQL correctly?

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show all topics;
CREATE STREAM cdc_window_table_changelog_stream (application_id STRING KEY,
                                                 application_id_count BIGINT)
  WITH (KAFKA_TOPIC='_confluent-ksql-xxx-ksqlquery_CTAS_CDC_WINDOW_TABLE_271-Aggregate-GroupBy-repartition',
        VALUE_FORMAT='JSON');
SELECT *
FROM cdc_window_table_changelog_stream
EMIT CHANGES;
+------------------+-----------------------+
|APPLICATION_ID    |APPLICATION_ID_COUNT   |
+------------------+-----------------------+
|a1                |null                   |
|a1                |null                   |
|a1                |null                   |
-----------------------
show all topics;
CREATE STREAM cdc_window_table_changelog_stream (application_id STRING KEY,
                                                 application_id_count BIGINT)
  WITH (KAFKA_TOPIC='_confluent-ksql-xxx-ksqlquery_CTAS_CDC_WINDOW_TABLE_271-Aggregate-GroupBy-repartition',
        VALUE_FORMAT='JSON');
SELECT *
FROM cdc_window_table_changelog_stream
EMIT CHANGES;
+------------------+-----------------------+
|APPLICATION_ID    |APPLICATION_ID_COUNT   |
+------------------+-----------------------+
|a1                |null                   |
|a1                |null                   |
|a1                |null                   |
-----------------------
show all topics;
CREATE STREAM cdc_window_table_changelog_stream (application_id STRING KEY,
                                                 application_id_count BIGINT)
  WITH (KAFKA_TOPIC='_confluent-ksql-xxx-ksqlquery_CTAS_CDC_WINDOW_TABLE_271-Aggregate-GroupBy-repartition',
        VALUE_FORMAT='JSON');
SELECT *
FROM cdc_window_table_changelog_stream
EMIT CHANGES;
+------------------+-----------------------+
|APPLICATION_ID    |APPLICATION_ID_COUNT   |
+------------------+-----------------------+
|a1                |null                   |
|a1                |null                   |
|a1                |null                   |
-----------------------
show all topics;
CREATE STREAM cdc_window_table_changelog_stream (application_id STRING KEY,
                                                 application_id_count BIGINT)
  WITH (KAFKA_TOPIC='_confluent-ksql-xxx-ksqlquery_CTAS_CDC_WINDOW_TABLE_271-Aggregate-GroupBy-repartition',
        VALUE_FORMAT='JSON');
SELECT *
FROM cdc_window_table_changelog_stream
EMIT CHANGES;
+------------------+-----------------------+
|APPLICATION_ID    |APPLICATION_ID_COUNT   |
+------------------+-----------------------+
|a1                |null                   |
|a1                |null                   |
|a1                |null                   |

How to select value in a JSON string by KSQL?

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SELECT after->id,
       EXTRACTJSONFIELD(after->metadata, '$.operation'),
       COUNT(after->id),
       WINDOWSTART AS window_start,
       WINDOWEND AS window_end
FROM my_stream
WINDOW TUMBLING (SIZE 20 SECONDS)
GROUP BY after
EMIT CHANGES;

Can we select a specific row of records from a confluent kafka topic?

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CREATE STREAM FOO_02 WITH (KAFKA_TOPIC='FOO_02', FORMAT='AVRO');
SET 'auto.offset.reset' = 'earliest';
CREATE TABLE FOO AS
  SELECT COL1, 
         LATEST_BY_OFFSET(COL2) AS COL2
    FROM FOO_02
   WHERE COL1=0
   GROUP BY COL1;
SET 'auto.offset.reset' = 'earliest';

CREATE TABLE FOO AS
[…]
-----------------------
CREATE STREAM FOO_02 WITH (KAFKA_TOPIC='FOO_02', FORMAT='AVRO');
SET 'auto.offset.reset' = 'earliest';
CREATE TABLE FOO AS
  SELECT COL1, 
         LATEST_BY_OFFSET(COL2) AS COL2
    FROM FOO_02
   WHERE COL1=0
   GROUP BY COL1;
SET 'auto.offset.reset' = 'earliest';

CREATE TABLE FOO AS
[…]
-----------------------
CREATE STREAM FOO_02 WITH (KAFKA_TOPIC='FOO_02', FORMAT='AVRO');
SET 'auto.offset.reset' = 'earliest';
CREATE TABLE FOO AS
  SELECT COL1, 
         LATEST_BY_OFFSET(COL2) AS COL2
    FROM FOO_02
   WHERE COL1=0
   GROUP BY COL1;
SET 'auto.offset.reset' = 'earliest';

CREATE TABLE FOO AS
[…]
-----------------------
CREATE STREAM FOO_02 WITH (KAFKA_TOPIC='FOO_02', FORMAT='AVRO');
SET 'auto.offset.reset' = 'earliest';
CREATE TABLE FOO AS
  SELECT COL1, 
         LATEST_BY_OFFSET(COL2) AS COL2
    FROM FOO_02
   WHERE COL1=0
   GROUP BY COL1;
SET 'auto.offset.reset' = 'earliest';

CREATE TABLE FOO AS
[…]

How to manipulate Kafka key documents with KSQLDB?

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$ kcat -b localhost:9092 -t test -P -K!
{"schema":{"type":"string","optional":false},"payload":"history::05000228023411_RO_RO11219082::80"}!{"col1":"foo","col2":"bar","col3":42}
^D
ksql> print 'test' from beginning;
Key format: JSON or SESSION(KAFKA_STRING) or HOPPING(KAFKA_STRING) or TUMBLING(KAFKA_STRING) or KAFKA_STRING
Value format: JSON or KAFKA_STRING
rowtime: 2022/03/04 14:14:01.539 Z, key: {"schema":{"type":"string","optional":false},"payload":"history::05000228023411_RO_RO11219082::80"}, value: {"col1":"foo","col2":"bar","col3":42}, partition: 0
CREATE STREAM my_test (
  my_key_col STRUCT < payload VARCHAR > KEY,
  col1 VARCHAR,
  col2 VARCHAR,
  col3 INT
) WITH (KAFKA_TOPIC = 'test', FORMAT = 'JSON');
SET 'auto.offset.reset' = 'earliest';

SELECT my_key_col->payload, col1, col2, col3
  FROM my_test
 WHERE my_key_col->payload LIKE 'history%'
EMIT CHANGES;

+--------------------------------------------+-------+------+-------+
|PAYLOAD                                     |COL1   |COL2  |COL3   |
+--------------------------------------------+-------+------+-------+
|history::05000228023411_RO_RO11219082::80   |foo    |bar   |42     |
-----------------------
$ kcat -b localhost:9092 -t test -P -K!
{"schema":{"type":"string","optional":false},"payload":"history::05000228023411_RO_RO11219082::80"}!{"col1":"foo","col2":"bar","col3":42}
^D
ksql> print 'test' from beginning;
Key format: JSON or SESSION(KAFKA_STRING) or HOPPING(KAFKA_STRING) or TUMBLING(KAFKA_STRING) or KAFKA_STRING
Value format: JSON or KAFKA_STRING
rowtime: 2022/03/04 14:14:01.539 Z, key: {"schema":{"type":"string","optional":false},"payload":"history::05000228023411_RO_RO11219082::80"}, value: {"col1":"foo","col2":"bar","col3":42}, partition: 0
CREATE STREAM my_test (
  my_key_col STRUCT < payload VARCHAR > KEY,
  col1 VARCHAR,
  col2 VARCHAR,
  col3 INT
) WITH (KAFKA_TOPIC = 'test', FORMAT = 'JSON');
SET 'auto.offset.reset' = 'earliest';

SELECT my_key_col->payload, col1, col2, col3
  FROM my_test
 WHERE my_key_col->payload LIKE 'history%'
EMIT CHANGES;

+--------------------------------------------+-------+------+-------+
|PAYLOAD                                     |COL1   |COL2  |COL3   |
+--------------------------------------------+-------+------+-------+
|history::05000228023411_RO_RO11219082::80   |foo    |bar   |42     |
-----------------------
$ kcat -b localhost:9092 -t test -P -K!
{"schema":{"type":"string","optional":false},"payload":"history::05000228023411_RO_RO11219082::80"}!{"col1":"foo","col2":"bar","col3":42}
^D
ksql> print 'test' from beginning;
Key format: JSON or SESSION(KAFKA_STRING) or HOPPING(KAFKA_STRING) or TUMBLING(KAFKA_STRING) or KAFKA_STRING
Value format: JSON or KAFKA_STRING
rowtime: 2022/03/04 14:14:01.539 Z, key: {"schema":{"type":"string","optional":false},"payload":"history::05000228023411_RO_RO11219082::80"}, value: {"col1":"foo","col2":"bar","col3":42}, partition: 0
CREATE STREAM my_test (
  my_key_col STRUCT < payload VARCHAR > KEY,
  col1 VARCHAR,
  col2 VARCHAR,
  col3 INT
) WITH (KAFKA_TOPIC = 'test', FORMAT = 'JSON');
SET 'auto.offset.reset' = 'earliest';

SELECT my_key_col->payload, col1, col2, col3
  FROM my_test
 WHERE my_key_col->payload LIKE 'history%'
EMIT CHANGES;

+--------------------------------------------+-------+------+-------+
|PAYLOAD                                     |COL1   |COL2  |COL3   |
+--------------------------------------------+-------+------+-------+
|history::05000228023411_RO_RO11219082::80   |foo    |bar   |42     |
-----------------------
$ kcat -b localhost:9092 -t test -P -K!
{"schema":{"type":"string","optional":false},"payload":"history::05000228023411_RO_RO11219082::80"}!{"col1":"foo","col2":"bar","col3":42}
^D
ksql> print 'test' from beginning;
Key format: JSON or SESSION(KAFKA_STRING) or HOPPING(KAFKA_STRING) or TUMBLING(KAFKA_STRING) or KAFKA_STRING
Value format: JSON or KAFKA_STRING
rowtime: 2022/03/04 14:14:01.539 Z, key: {"schema":{"type":"string","optional":false},"payload":"history::05000228023411_RO_RO11219082::80"}, value: {"col1":"foo","col2":"bar","col3":42}, partition: 0
CREATE STREAM my_test (
  my_key_col STRUCT < payload VARCHAR > KEY,
  col1 VARCHAR,
  col2 VARCHAR,
  col3 INT
) WITH (KAFKA_TOPIC = 'test', FORMAT = 'JSON');
SET 'auto.offset.reset' = 'earliest';

SELECT my_key_col->payload, col1, col2, col3
  FROM my_test
 WHERE my_key_col->payload LIKE 'history%'
EMIT CHANGES;

+--------------------------------------------+-------+------+-------+
|PAYLOAD                                     |COL1   |COL2  |COL3   |
+--------------------------------------------+-------+------+-------+
|history::05000228023411_RO_RO11219082::80   |foo    |bar   |42     |

Why is `Properties` not a valid field name in ksql?

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SELECT `table`.`properties` FROM `table` ...

How to copy and transform all messages from one kafka topic (in avro format) to another topic (in json format)

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CREATE STREAM MY_AVRO_SOURCE
  WITH (KAFKA_TOPIC='my_source_topic', FORMAT='AVRO');
SET 'auto.offset.reset' = 'earliest';
CREATE STREAM MY_JSON_TARGET 
  WITH (FORMAT='JSON') 
  AS SELECT * FROM MY_AVRO_SOURCE;
-----------------------
CREATE STREAM MY_AVRO_SOURCE
  WITH (KAFKA_TOPIC='my_source_topic', FORMAT='AVRO');
SET 'auto.offset.reset' = 'earliest';
CREATE STREAM MY_JSON_TARGET 
  WITH (FORMAT='JSON') 
  AS SELECT * FROM MY_AVRO_SOURCE;
-----------------------
CREATE STREAM MY_AVRO_SOURCE
  WITH (KAFKA_TOPIC='my_source_topic', FORMAT='AVRO');
SET 'auto.offset.reset' = 'earliest';
CREATE STREAM MY_JSON_TARGET 
  WITH (FORMAT='JSON') 
  AS SELECT * FROM MY_AVRO_SOURCE;

Does anybody have a KSQL query that counts event in a topic on a per hour basis?

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CREATE STREAM my_stream (NAME VARCHAR, MESSAGE VARCHAR)
  WITH (KAFKA_TOPIC='my_topic', FORMAT='JSON');
SELECT TIMESTAMPTOSTRING(WINDOWSTART,'yyyy-MM-dd HH:mm:ss','Europe/London')
         AS WINDOW_START_TS,
       COUNT(*) AS RECORD_CT
  FROM my_stream
        WINDOW TUMBLING (SIZE 1 HOURS)
  GROUP BY 1
  EMIT CHANGES;
CREATE STREAM my_stream (NAME VARCHAR, MESSAGE VARCHAR, ETS BIGINT)
  WITH (KAFKA_TOPIC='my_topic', FORMAT='JSON', TIMESTAMP='ets');
-----------------------
CREATE STREAM my_stream (NAME VARCHAR, MESSAGE VARCHAR)
  WITH (KAFKA_TOPIC='my_topic', FORMAT='JSON');
SELECT TIMESTAMPTOSTRING(WINDOWSTART,'yyyy-MM-dd HH:mm:ss','Europe/London')
         AS WINDOW_START_TS,
       COUNT(*) AS RECORD_CT
  FROM my_stream
        WINDOW TUMBLING (SIZE 1 HOURS)
  GROUP BY 1
  EMIT CHANGES;
CREATE STREAM my_stream (NAME VARCHAR, MESSAGE VARCHAR, ETS BIGINT)
  WITH (KAFKA_TOPIC='my_topic', FORMAT='JSON', TIMESTAMP='ets');
-----------------------
CREATE STREAM my_stream (NAME VARCHAR, MESSAGE VARCHAR)
  WITH (KAFKA_TOPIC='my_topic', FORMAT='JSON');
SELECT TIMESTAMPTOSTRING(WINDOWSTART,'yyyy-MM-dd HH:mm:ss','Europe/London')
         AS WINDOW_START_TS,
       COUNT(*) AS RECORD_CT
  FROM my_stream
        WINDOW TUMBLING (SIZE 1 HOURS)
  GROUP BY 1
  EMIT CHANGES;
CREATE STREAM my_stream (NAME VARCHAR, MESSAGE VARCHAR, ETS BIGINT)
  WITH (KAFKA_TOPIC='my_topic', FORMAT='JSON', TIMESTAMP='ets');

org.apache.kafka.connect.errors.DataException: Converting byte[] to Kafka Connect data failed due to serialization error of topic

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'value.converter.schema.registry.url' = 'http://localhost:8081',
'value.converter.schema.registry.url' = 'http://schema-registry:8081',
-----------------------
'value.converter.schema.registry.url' = 'http://localhost:8081',
'value.converter.schema.registry.url' = 'http://schema-registry:8081',

Confluent connect 5.5.1 is throwing Exception: java.lang.OutOfMemoryError UncaughtExceptionHandler in thread kafka-coordinator-heartbeat-thread |

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rest.advertised.host.name=<FULLY QUALIFIED HOST NAME> OR <IP.ADDRESS>
rest.advertised.port=8083
[kafka@abchostnamekk01 logs]$ grep -ri 'info advertised' connectDistributed.out
    [2021-08-12 14:06:50,809] INFO Advertised URI: http://abchostnamekk01.domain.com:8083
-----------------------
rest.advertised.host.name=<FULLY QUALIFIED HOST NAME> OR <IP.ADDRESS>
rest.advertised.port=8083
[kafka@abchostnamekk01 logs]$ grep -ri 'info advertised' connectDistributed.out
    [2021-08-12 14:06:50,809] INFO Advertised URI: http://abchostnamekk01.domain.com:8083
-----------------------
./connect-distributed:  export KAFKA_HEAP_OPTS="-Xms6G -Xmx6G"
KiB Mem : 32304516 total,   288648 free, 17298612 used, 
./connect-distributed:export KAFKA_HEAP_OPTS="-Xmx28G -Xms24G"
kill -SIGTERM <PID> 
/apps/confluent-5.5.1/bin/connect-distributed -daemon /apps/confluent-5.5.1/etc/kafka/connect-distributed-worker1.properties
-----------------------
./connect-distributed:  export KAFKA_HEAP_OPTS="-Xms6G -Xmx6G"
KiB Mem : 32304516 total,   288648 free, 17298612 used, 
./connect-distributed:export KAFKA_HEAP_OPTS="-Xmx28G -Xms24G"
kill -SIGTERM <PID> 
/apps/confluent-5.5.1/bin/connect-distributed -daemon /apps/confluent-5.5.1/etc/kafka/connect-distributed-worker1.properties
-----------------------
./connect-distributed:  export KAFKA_HEAP_OPTS="-Xms6G -Xmx6G"
KiB Mem : 32304516 total,   288648 free, 17298612 used, 
./connect-distributed:export KAFKA_HEAP_OPTS="-Xmx28G -Xms24G"
kill -SIGTERM <PID> 
/apps/confluent-5.5.1/bin/connect-distributed -daemon /apps/confluent-5.5.1/etc/kafka/connect-distributed-worker1.properties
-----------------------
./connect-distributed:  export KAFKA_HEAP_OPTS="-Xms6G -Xmx6G"
KiB Mem : 32304516 total,   288648 free, 17298612 used, 
./connect-distributed:export KAFKA_HEAP_OPTS="-Xmx28G -Xms24G"
kill -SIGTERM <PID> 
/apps/confluent-5.5.1/bin/connect-distributed -daemon /apps/confluent-5.5.1/etc/kafka/connect-distributed-worker1.properties
-----------------------
./connect-distributed:  export KAFKA_HEAP_OPTS="-Xms6G -Xmx6G"
KiB Mem : 32304516 total,   288648 free, 17298612 used, 
./connect-distributed:export KAFKA_HEAP_OPTS="-Xmx28G -Xms24G"
kill -SIGTERM <PID> 
/apps/confluent-5.5.1/bin/connect-distributed -daemon /apps/confluent-5.5.1/etc/kafka/connect-distributed-worker1.properties

Joining and enriching Kafka topic with in memory data (Dictionary, Hashmap, Dataframe)?

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final Map m = new Hashmap();
builder.stream(topic).mapValues(v -> m.get(v)).to(out);

Community Discussions

Trending Discussions on ksql
  • KSQL UDF access ROWPARTITION and similar information
  • How to create an output stream (changelog) based on a table in KSQL correctly?
  • How to select value in a JSON string by KSQL?
  • Can we select a specific row of records from a confluent kafka topic?
  • Confluent Platform - how to properly use ksql-datagen?
  • How to manipulate Kafka key documents with KSQLDB?
  • Kafka-connect to PostgreSQL - org.apache.kafka.connect.errors.DataException: Failed to deserialize topic to to Avro
  • Why is `Properties` not a valid field name in ksql?
  • ksqlDB - How to set batch.size and linger.ms for producers to optimise compression
  • How to copy and transform all messages from one kafka topic (in avro format) to another topic (in json format)
Trending Discussions on ksql

QUESTION

KSQL UDF access ROWPARTITION and similar information

Asked 2022-Apr-14 at 19:27

I have a custom UDF that I can pass a struct to:

select my_udf(a.my_data) from MY_STREAM a;

What I would like to do, is pass all info from my stream to that custom UDF:

select my_udf(a) from MY_STREAM a;

That way I can access the row partition, time, offset, etc. Unfortunately, KSQL does not understand my intent:

SELECT column 'A' cannot be resolved

Any idea how I could work around this?

ANSWER

Answered 2022-Apr-14 at 19:27

It's not possible to pass in a full row into a UDF, only columns, and a is the name of the stream, not a column name.

You can change your UDF, to accept multiple parameters, eg, my_udf(my_data, ROWTIME, ROWPARTITION) to pass in an the needed metadata individually.

Source https://stackoverflow.com/questions/71871349

Community Discussions, Code Snippets contain sources that include Stack Exchange Network

Vulnerabilities

No vulnerabilities reported

Install ksql

Follow the ksqlDB quickstart to get started in just a few minutes.
Read through the ksqlDB documentation.
Take a look at some ksqlDB use case recipes for examples of common patterns.

Support

See the ksqlDB documentation for the latest stable release.

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