Kudu Source Connector for Confluent Platform

The Kafka Connect Kudu Source connector allows you to import data to an Apache Kafka® topic from a Kudo columnar relational database using the Impala JDBC driver.

Data is loaded by periodically executing a SQL query and creating an output record for each row in the result set. By default, all tables in a database are copied, each to its own output topic. The database is monitored for new or deleted tables and adapts automatically. When copying data from a table, the connector can load only new or modified rows by specifying which columns should be used to detect new or modified data.

You can configure Java streams applications to deserialize and ingest data in multiple ways, including Kafka console producers, JDBC source connectors, and Java client producers. For full code examples, see Pipelining with Kafka Connect and Kafka Streams.

Configuration Properties

For a complete list of configuration properties for the source connector, see Kudu Source Connector Configuration Properties.

Features

The source connector supports copying tables with a variety of JDBC data types, adding and removing tables from the database dynamically, whitelists and blacklists, varying polling intervals, and other settings. However, the most important features for most users are the settings controlling how data is incrementally copied from the database.

Kafka Connect tracks the latest record it retrieved from each table, so it can start in the correct location on the next iteration (or in case of a crash). The source connector uses this functionality to only get updated rows from a table (or from the output of a custom query) on each iteration. Several modes are supported, each of which differs in how modified rows are detected.

Incremental Query Modes

Each incremental query mode tracks a set of columns for each row, which it uses to keep track of which rows have been processed and which rows are new or have been updated. The mode setting controls this behavior and supports the following options:

• Incrementing Column: A single column containing a unique ID for each row, where newer rows are guaranteed to have larger IDs, i.e. an AUTOINCREMENT column. Note that this mode can only detect new rows. Updates to existing rows cannot be detected, so this mode should only be used for immutable data. One example where you might use this mode is when streaming fact tables in a data warehouse, since those are typically insert-only.
• Timestamp Column: In this mode, a single column containing a modification timestamp is used to track the last time data was processed and to query only for rows that have been modified since that time. Note that because timestamps are no necessarily unique, this mode cannot guarantee all updated data will be delivered: if 2 rows share the same timestamp and are returned by an incremental query, but only one has been processed before a crash, the second update will be missed when the system recovers.
• Timestamp and Incrementing Columns: This is the most robust and accurate mode, combining an incrementing column with a timestamp column. By combining the two, as long as the timestamp is sufficiently granular, each (id, timestamp) tuple will uniquely identify an update to a row. Even if an update fails after partially completing, unprocessed updates will still be correctly detected and delivered when the system recovers.
• Custom Query: The source connector supports using custom queries instead of copying whole tables. With a custom query, one of the other update automatic update modes can be used as long as the necessary WHERE clause can be correctly appended to the query. Alternatively, the specified query may handle filtering to new updates itself; however, note that no offset tracking will be performed (unlike the automatic modes where incrementing and/or timestamp column values are recorded for each record), so the query must track offsets itself.
• Bulk: This mode is unfiltered and therefore not incremental at all. It will load all rows from a table on each iteration. This can be useful if you want to periodically dump an entire table where entries are eventually deleted and the downstream system can safely handle duplicates.

Note that all incremental query modes that use certain columns to detect changes will require indexes on those columns to efficiently perform the queries.

For incremental query modes that use timestamps, the source connector uses a configuration timestamp.delay.interval.ms to control the waiting period after a row with certain timestamp appears before you include it in the result. The additional wait allows transactions with earlier timestamps to complete and the related changes to be included in the result. For more information, see Kudu Source Connector Configuration Properties.

Mapping Column Types

The source connector has a few options for controlling how column types are mapped into Connect field types. By default, the connector maps SQL/JDBC types to the most accurate representation in Java, which is straightforward for many SQL types but maybe a bit unexpected for some types. For example, SQL’s DECIMAL types have very clear semantics controlled by the precision and scale, and the most accurate representation is Connect’s Decimal logical type that uses Java’s BigDecimal representation. Unfortunately, Avro serializes Decimal types as raw bytes that may be difficult to consume.

Quick Start

To see the basic functionality of the connector, you copy a single table from a local Kudu database. You can assume each entry in the table is assigned a unique ID and is not modified after creation.

Note

For an example of how to get Kafka Connect connected to Confluent Cloud, see Distributed Cluster.

Prerequisites

• Confluent Platform is installed and services are running by using the Confluent CLI. This quick start assumes that you are using the Confluent CLI. By default ZooKeeper, Kafka, Schema Registry, Kafka Connect REST API, and Kafka Connect are started with the confluent local services start command. For more information, see Confluent Platform.
• Kudu and Impala are installed and configured properly (Using Kudu with Impala). For DECIMAL type support, we need at least Kudu 1.7.0, and Impala 3.0.
• Verify that the Impala JDBC driver is available on the Kafka Connect process’s CLASSPATH.
• Kafka and Schema Registry are running locally on the default ports.

Create Table and Load Data

1. Start Impala shell.

   impala-shell -i localhost:21000 -l -u <ldap-username> --ldap_password_cmd="echo -n <ldap-password>" --auth_creds_ok_in_clear
   

2. Create a database with this command:

   CREATE DATABASE test;
   

3. Use a database with this command:

   USE test;
   

4. Create a table and seed it with some data:

   CREATE TABLE accounts (
       id BIGINT,
       name STRING,
       PRIMARY KEY(id)
       ) PARTITION BY HASH PARTITIONS 16 STORED AS KUDU TBLPROPERTIES ("kudu.master_addresses" = "127.0.0.1","kudu.num_tablet_replicas" = "1");
   

   INSERT INTO accounts (id, name) VALUES (1, 'alice');
   

   INSERT INTO accounts (id, name) VALUES (2, 'bob');
   

   Tip

   You can run SELECT * from accounts; to verify your table has been created.

Load the Kudu Source Connector

Load the predefined Kudu source connector.

1. Optional: View the available predefined connectors with this command:

   confluent local services connect connector list
   

   Your output should resemble:

   Bundled Predefined Connectors (edit configuration under etc/):
     elasticsearch-sink
     file-source
     file-sink
     jdbc-source
     jdbc-sink
     kudu-source
     kudu-sink
     hdfs-sink
     s3-sink
   

2. Create a kudu-source.json file for your Kudu Source Connector.

   {
       "name": "kudu-source",
       "config": {
         "connector.class": "io.confluent.connect.kudu.KuduSourceConnector",
         "tasks.max": "1",
         "impala.server": "127.0.0.1",
         "impala.port": "21050",
         "kudu.database": "test",
         "mode": "incrementing",
         "incrementing.column.name": "id",
         "topic.prefix": "test-kudu-",
         "table.whitelist": "accounts",
   
         "key.converter": "io.confluent.connect.avro.AvroConverter",
         "key.converter.schema.registry.url": "http://localhost:8081",
         "value.converter": "io.confluent.connect.avro.AvroConverter",
         "value.converter.schema.registry.url": "http://localhost:8081",
         "confluent.topic.bootstrap.servers": "localhost:9092",
         "confluent.topic.replication.factor": "1",
         "impala.ldap.password": "secret",
         "impala.ldap.user": "kudu",
         "name": "kudu-source"
       }
   }
   

3. Load the kudu-source connector. The test file must be in the same directory where Connect is started.

   confluent local services connect connector load kudu-source --config kudu-source.json
   

   Your output should resemble:

   {
      "name": "kudu-source",
      "config": {
        "connector.class": "io.confluent.connect.kudu.KuduSourceConnector",
        "tasks.max": "1",
        "impala.server": "127.0.0.1",
        "impala.port": "21050",
        "kudu.database": "test",
        "mode": "incrementing",
        "incrementing.column.name": "id",
        "topic.prefix": "test-kudu-",
        "table.whitelist": "accounts",
        "key.converter": "io.confluent.connect.avro.AvroConverter",
        "key.converter.schema.registry.url": "http://localhost:8081",
        "value.converter": "io.confluent.connect.avro.AvroConverter",
        "value.converter.schema.registry.url": "http://localhost:8081",
        "confluent.topic.bootstrap.servers": "localhost:9092",
        "confluent.topic.replication.factor": "1",
        "impala.ldap.password": "<ldap-password>",
        "impala.ldap.user": "<ldap-user>",
        "name": "kudu-source"
      },
      "tasks": [],
      "type": "source"
    }
   

   To check that it has copied the data that was present when you started Kafka Connect, start a console consumer, reading from the beginning of the topic:

     ./bin/kafka-avro-console-consumer --bootstrap-server localhost:9092 --topic test-kudu-accounts --from-beginning
   {"id":1,"name":{"string":"alice"}}
   {"id":2,"name":{"string":"bob"}}
   

The output shows the two records as expected, one per line, in the JSON encoding of the Avro records. Each row is represented as an Avro record and each column is a field in the record. You can see both columns in the table, id and name. The IDs were auto-generated and the column is of type INTEGER NOT NULL, which can be encoded directly as an integer. The name column has type STRING and can be NULL. The JSON encoding of Avro encodes the strings in the format {"type": value}, so you can see that both rows have string values with the names specified when you inserted the data.

Add a Record to the Consumer

Add another record via the Impala shell:

INSERT INTO accounts (id, name) VALUES (3, 'cathy');

You can switch back to the console consumer and see the new record is added and, importantly, the old entries are not repeated:

{"id":3,"name":{"string":"cathy"}}

Note that the default polling interval is five seconds, so it may take a few seconds to show up. Depending on your expected rate of updates or desired latency, a smaller poll interval could be used to deliver updates more quickly.

All the features of Kafka Connect, including offset management and fault tolerance, work with the source connector. You can restart and kill the processes and they will pick up where they left off, copying only new data (as defined by the mode setting).

Configuration

The full set of configuration options are listed in Kudu Source Connector Configuration Properties, but here are a few template configurations that cover some common usage scenarios.

Use a whitelist to limit changes to a subset of tables in a Kudu database, using id and modified columns that are standard on all whitelisted tables to detect rows that have been modified. This mode is the most robust because it can combine the unique, immutable row IDs with modification timestamps to guarantee modifications are not missed even if the process dies in the middle of an incremental update query.

name=whitelist-timestamp-source
connector.class=io.confluent.connect.kudu.KuduSourceConnector
tasks.max=10

connection.url=jdbc:impala://<Impala server>:21050/my_database
table.whitelist=users,products,transactions

mode=timestamp+incrementing
timestamp.column.name=modified
incrementing.column.name=id

topic.prefix=kudu-

Use a custom query instead of loading tables, allowing you to join data from multiple tables. As long as the query does not include its own filtering, you can still use the built-in modes for incremental queries (in this case, using a timestamp column). Note that this limits you to a single output per connector and because there is no table name, the topic “prefix” is actually the full topic name in this case.

name=whitelist-timestamp-source
connector.class=io.confluent.connect.kudu.KuduSourceConnector
tasks.max=10

connection.url=jdbc:impala://<Impala server>:21050/my_database
query=SELECT users.id, users.name, transactions.timestamp, transactions.user_id, transactions.payment FROM users JOIN transactions ON (users.id = transactions.user_id)
mode=timestamp
timestamp.column.name=timestamp

topic.prefix=kudu-joined-data

Troubleshooting

HiveServer2 error

When you run this connector, you might see the following error message.

java.sql.SQLException: [Cloudera][ImpalaJDBCDriver](500176) Error connecting to HiveServer2, please verify connection settings.

It means you haven’t set an LDAP in Impala or a username and a password for LDAP is not valid.

Additional documentation

• Kudu Source Connector Configuration Properties