5

第 1 步：运行生产者以创建示例数据

./bin/kafka-avro-console-producer \
         --broker-list localhost:9092 --topic stream-test-topic \
         --property schema.registry.url=http://localhost:8081 \
         --property value.schema='{"type":"record","name":"dealRecord","fields":[{"name":"DEAL_ID","type":"string"},{"name":"DEAL_EXPENSE_CODE","type":"string"},{"name":"DEAL_BRANCH","type":"string"}]}'

样本数据 ：

{"DEAL_ID":"deal002", "DEAL_EXPENSE_CODE":"EXP002", "DEAL_BRANCH":"AMSTERDAM"}
{"DEAL_ID":"deal003", "DEAL_EXPENSE_CODE":"EXP003", "DEAL_BRANCH":"AMSTERDAM"}
{"DEAL_ID":"deal004", "DEAL_EXPENSE_CODE":"EXP004", "DEAL_BRANCH":"AMSTERDAM"}
{"DEAL_ID":"deal005", "DEAL_EXPENSE_CODE":"EXP005", "DEAL_BRANCH":"AMSTERDAM"}
{"DEAL_ID":"deal006", "DEAL_EXPENSE_CODE":"EXP006", "DEAL_BRANCH":"AMSTERDAM"}
{"DEAL_ID":"deal007", "DEAL_EXPENSE_CODE":"EXP001", "DEAL_BRANCH":"AMSTERDAM"}
{"DEAL_ID":"deal008", "DEAL_EXPENSE_CODE":"EXP002", "DEAL_BRANCH":"AMSTERDAM"}
{"DEAL_ID":"deal009", "DEAL_EXPENSE_CODE":"EXP003", "DEAL_BRANCH":"AMSTERDAM"}
{"DEAL_ID":"deal010", "DEAL_EXPENSE_CODE":"EXP004", "DEAL_BRANCH":"AMSTERDAM"}
{"DEAL_ID":"deal011", "DEAL_EXPENSE_CODE":"EXP005", "DEAL_BRANCH":"AMSTERDAM"}
{"DEAL_ID":"deal012", "DEAL_EXPENSE_CODE":"EXP006", "DEAL_BRANCH":"AMSTERDAM"}

步骤 2：打开另一个终端并运行消费者来测试数据。 

./bin/kafka-avro-console-consumer --topic stream-test-topic \
         --bootstrap-server localhost:9092 \
         --property schema.registry.url=http://localhost:8081 \
         --from-beginning

步骤 3：打开另一个终端并运行生产者。

./bin/kafka-avro-console-producer \
         --broker-list localhost:9092 --topic expense-test-topic \
--property "parse.key=true" \
--property "key.separator=:" \
--property schema.registry.url=http://localhost:8081 \
--property key.schema='"string"' \
         --property value.schema='{"type":"record","name":"dealRecord","fields":[{"name":"EXPENSE_CODE","type":"string"},{"name":"EXPENSE_DESC","type":"string"}]}'

数据：

"pk1":{"EXPENSE_CODE":"EXP001", "EXPENSE_DESC":"Regulatory Deposit"}
"pk2":{"EXPENSE_CODE":"EXP002", "EXPENSE_DESC":"ABC - Sofia"}
"pk3":{"EXPENSE_CODE":"EXP003", "EXPENSE_DESC":"Apple Corporation"}
"pk4":{"EXPENSE_CODE":"EXP004", "EXPENSE_DESC":"Confluent Europe"}
"pk5":{"EXPENSE_CODE":"EXP005", "EXPENSE_DESC":"Air India"}
"pk6":{"EXPENSE_CODE":"EXP006", "EXPENSE_DESC":"KLM International"}

第 4 步：打开另一个终端并运行消费者

./bin/kafka-avro-console-consumer --topic expense-test-topic \
         --bootstrap-server localhost:9092 \
--property "parse.key=true" \
--property "key.separator=:" \
--property schema.registry.url=http://localhost:8081 \
         --from-beginning

第 5 步：登录到 KSQL 客户端。

./bin/ksql http://localhost:8088

创建以下流和表并运行连接查询。

KSQL：

溪流：

    CREATE STREAM SAMPLE_STREAM 
       (DEAL_ID VARCHAR, DEAL_EXPENSE_CODE varchar, DEAL_BRANCH VARCHAR) 
       WITH (kafka_topic='stream-test-topic',value_format='AVRO', key = 'DEAL_ID');

桌子：

CREATE TABLE SAMPLE_TABLE 
   (EXPENSE_CODE varchar, EXPENSE_DESC VARCHAR)
   WITH (kafka_topic='expense-test-topic',value_format='AVRO', key = 'EXPENSE_CODE');

以下是输出：

ksql> SELECT STREAM1.DEAL_EXPENSE_CODE, TABLE1.EXPENSE_DESC 
       from SAMPLE_STREAM STREAM1 LEFT JOIN SAMPLE_TABLE TABLE1 
       ON STREAM1.DEAL_EXPENSE_CODE = TABLE1.EXPENSE_CODE  
       WINDOW TUMBLING (SIZE 3 MINUTE) 
       GROUP BY STREAM1.DEAL_EXPENSE_CODE, TABLE1.EXPENSE_DESC;

EXP001 | null
EXP001 | null
EXP002 | null
EXP003 | null
EXP004 | null
EXP005 | null
EXP006 | null
EXP002 | null
EXP002 | null
4

1 回答 1

5

tl;dr: Your table data needs to be keyed on the column on which you're joining.

Using the sample data above, here's how to investigate and fix.

  1. Use KSQL to check the data in the topics (no need for kafka-avro-console-consumer). Format of the output data is timestamp, key, value

    • stream: 

      ksql> print 'stream-test-topic' from beginning;
Format:AVRO
30/04/18 15:59:13 BST, null, {"DEAL_ID": "deal002", "DEAL_EXPENSE_CODE": "EXP002", "DEAL_BRANCH": "AMSTERDAM"}
30/04/18 15:59:13 BST, null, {"DEAL_ID": "deal003", "DEAL_EXPENSE_CODE": "EXP003", "DEAL_BRANCH": "AMSTERDAM"}
30/04/18 15:59:13 BST, null, {"DEAL_ID": "deal004", "DEAL_EXPENSE_CODE": "EXP004", "DEAL_BRANCH": "AMSTERDAM"}

    • table: 

      ksql> print 'expense-test-topic' from beginning;
Format:AVRO
30/04/18 16:10:52 BST, pk1, {"EXPENSE_CODE": "EXP001", "EXPENSE_DESC": "Regulatory Deposit"}
30/04/18 16:10:52 BST, pk2, {"EXPENSE_CODE": "EXP002", "EXPENSE_DESC": "ABC - Sofia"}
30/04/18 16:10:52 BST, pk3, {"EXPENSE_CODE": "EXP003", "EXPENSE_DESC": "Apple Corporation"}
30/04/18 16:10:52 BST, pk4, {"EXPENSE_CODE": "EXP004", "EXPENSE_DESC": "Confluent Europe"}
30/04/18 16:10:52 BST, pk5, {"EXPENSE_CODE": "EXP005", "EXPENSE_DESC": "Air India"}
30/04/18 16:10:52 BST, pk6, {"EXPENSE_CODE": "EXP006", "EXPENSE_DESC": "KLM International"}

    At this point, note that the key (pk<x>) does not match the column on which we will be joining

  2. Register the two topics: 

    ksql> CREATE STREAM deals WITH (KAFKA_TOPIC='stream-test-topic', VALUE_FORMAT='AVRO');

 Message
----------------
 Stream created
----------------

ksql> CREATE TABLE expense_codes_table WITH (KAFKA_TOPIC='expense-test-topic', VALUE_FORMAT='AVRO', KEY='EXPENSE_CODE');

 Message
---------------
 Table created
---------------

  3. Tell KSQL to query events from the beginning of each topic

    ksql> SET 'auto.offset.reset' = 'earliest';
Successfully changed local property 'auto.offset.reset' from 'null' to 'earliest'

  4. Validate that the table's declared key per the DDL (KEY='EXPENSE_CODE') matches the actual key of the underlying Kafka messages (available through the ROWKEY system column): 

    ksql> SELECT ROWKEY, EXPENSE_CODE FROM expense_codes_table;
pk1 | EXP001
pk2 | EXP002
pk3 | EXP003
pk4 | EXP004
pk5 | EXP005
pk6 | EXP006

    The keys don't match. Our join is doomed!

  5. Magic workaround—let's rekey the topic using KSQL!

    • Register the table's source topic as a KSQL STREAM: 

      ksql> CREATE STREAM expense_codes_stream WITH (KAFKA_TOPIC='expense-test-topic', VALUE_FORMAT='AVRO');

 Message
----------------
 Stream created
----------------

    • Create a derived stream, keyed on the correct colum. This is underpinned by a re-keyed Kafka topic. 

      ksql> CREATE STREAM EXPENSE_CODES_REKEY AS SELECT * FROM expense_codes_stream PARTITION BY EXPENSE_CODE;

 Message
----------------------------
 Stream created and running
----------------------------

    • Re-register the KSQL _TABLE_ on top of the re-keyed topic: 

      ksql> DROP TABLE expense_codes_table;

 Message
----------------------------------------
 Source EXPENSE_CODES_TABLE was dropped
----------------------------------------
ksql> CREATE TABLE expense_codes_table WITH (KAFKA_TOPIC='EXPENSE_CODES_REKEY', VALUE_FORMAT='AVRO', KEY='EXPENSE_CODE');

 Message
---------------
 Table created
---------------

    • Check the keys (declared vs message) match on the new table: 

      ksql> SELECT ROWKEY, EXPENSE_CODE FROM expense_codes_table;
EXP005 | EXP005
EXP001 | EXP001
EXP002 | EXP002
EXP003 | EXP003
EXP006 | EXP006
EXP004 | EXP004

  6. Successful join: 

    ksql> SELECT D.DEAL_EXPENSE_CODE, E.EXPENSE_DESC \
FROM deals D \
  LEFT JOIN expense_codes_table E \
  ON D.DEAL_EXPENSE_CODE = E.EXPENSE_CODE  \
WINDOW TUMBLING (SIZE 3 MINUTE) \
GROUP BY D.DEAL_EXPENSE_CODE, E.EXPENSE_DESC;

EXP006 | KLM International
EXP003 | Apple Corporation
EXP002 | ABC - Sofia
EXP004 | Confluent Europe
EXP001 | Regulatory Deposit
EXP005 | Air India
于 2018-04-30T15:29:55.560 回答