Load Data from Kafka

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Securely Connect to Kafka from SingleStore

Overview

When running a CREATE PIPELINE ... KAFKA ... statement, you may need to make a secure connection to Kafka.

Use Secure Socket Layer (SSL) for the connection and Simple Authentication and Security Layer (SASL) to authenticate. Using SASL for authentication is optional.

  • GSSAPI (Kerberos)

  • PLAIN

  • SCRAM-SHA-256

  • SCRAM-SHA-512

Kerberos may used for authentication.

This topic assumes SSL and/or Kerberos have been set up, configured, and enabled on the Kafka brokers. For information on how to enable this functionality, see the SSL and SASL sections in the Kafka documentation.

Convert Java Keystore (JKS) to Privacy Enhanced Mail (PEM) Key

To use SSL encryption for SingleStore pipelines, JKS keys need to be converted to PEM keys.

Note

In the steps below, the < > symbols indicate a variable and any information provided between these symbols is an example.

  1. Create the key and keystore. You will be prompted to enter details such as name, organizational unit, city, state, etc.

    keytool -genkey -keyalg RSA -keystore <keystore-name>.jks -storepass <password> -alias "<keystore-alias-name>"
    What is your first and last name?
     [Unknown]:- 
    What is the name of your organizational unit?
     [Unknown]:-
    What is the name of your organization?
     [Unknown]:-
  2. Export the client certificate from the keystore using the same password as in step 1.

    keytool -exportcert -rfc -file <pem-name>.pem -alias <pem-name-alias> -keystore <keystore-name>.jks
    Enter keystore password:  <password>
    Certificate stored in file <pem-name>.pem
  3. Import the client certificate to the truststore located on your Apache Server. Enter a new password for the keystore.

    keytool -keystore <truststore-name>.jks -alias <truststore-name-alias> -import -file <pem-name>.pem
    Enter keystore password:  
    Re-enter new password: 
    
    Trust this certificate? [no]:  yes
    Certificate was added to keystore
  4. Convert the client keystore to Public-Key Cryptography Standards (PKCS12) format. The <key name>.jks and password are the same as in step 1.

    keytool -v -importkeystore -srckeystore <keystore-name>.jks -srcalias <keystore alias> -destkeystore <new-keystore-name>.p12 -deststoretype PKCS12
    Importing keystore <keyname>.jks to <new-keyname>.p12...
    Enter destination keystore password:  
    Re-enter new password: 
    Enter source keystore password:  
    [Storing new-keystore-name.p12]
  5. Extract the client certificate key into a .pem file. Use the import password created in step 4.

    openssl pkcs12 -in <new-keyname>.p12 -nocerts -nodes > <keyname>.pem
    Enter Import Password:

Note

Refer to Generating SSL Certificates for more information.

Steps for Creating a Secure Connection

To create a secure connection from SingleStore to a Kafka cluster, follow these steps in order.

Copy Security Files to the SingleStoreCluster

Securely copy the CA certificate, SSL certificate, and SSL key used for connections between the SingleStorecluster and Kafka brokers from the Kafka cluster to each SingleStore node. You should use a secure file transfer method, such as scp, to copy the files to your SingleStore nodes. The file locations on your SingleStore nodes should be consistent across the cluster.

Setup Your SingleStoreCluster for Kerberos Authentication

To configure a Kafka pipeline to authenticate with Kerberos, you must configure all of your nodes in your SingleStorecluster as clients for Kerberos authentication. To do this, perform the following steps:

  1. Securely copy the keytab file containing the SingleStore service principal (e.g. memsql/host.domain.com@REALM.NAME) from the Kerberos server to each node in your SingleStorecluster. You should use a secure file transfer method, such as scp, to copy the keytab file to your SingleStore nodes. The file location on your SingleStore nodes should be consistent across the cluster.

  2. Make sure your SingleStore nodes can connect to the KDC server using the fully-qualified domain name (FQDN) of the KDC server. This might require configuring network settings or updating /etc/hosts on your nodes.

  3. Also ensure that the memsql service account on each node can access the copied keytab file. This can be accomplished by changing file ownership or permissions. If the memsql account cannot access the keytab file, you will not be able to complete the next step because your master aggregator will not be able to restart after applying configuration updates.

  4. When authenticating with Kerberos, SingleStore needs to authenticate as a client, which means you must also install a Kerberos client onto each node in your cluster. The following installs the krb5-user package for Debian-based Linux distributions.

    sudo apt-get update && apt-get install krb5-user

    When setting up your Kerberos configuration settings, set your default realm, Kerberos admin server, and other options to those defined by your KDC server. In the examples used in this topic, the default realm is EXAMPLE.COM, and the Kerberos server settings are set to the FQDN of the KDC server host.example.com.

Build a String with the Connection Settings

Using the following settings, create a string containing the CONFIG clause and optionally the CREDENTIALS clause of the CREATE PIPELINE ... KAFKA ... or SELECT ... INTO KAFKA ... statement that you will be running.

SSL Connection Settings
  1. In your CONFIG JSON, if you want to enable SSL encryption only, set "security.protocol": "ssl". If you want to enable Kerberos with SSL, or otherwise want to use SASL, set "security.protocol": "sasl_ssl".

  2. Set the remaining SSL configuration in the CONFIG JSON:

    • ssl.ca.location: Path to the CA certificate on the SingleStore node.

    • ssl.certificate.location: Path to the SSL certificate on the SingleStore node.

    • ssl.key.location: Path to the SSL certificate key on the SingleStore node.

  3. If your SSL certificate key is using a password, set it in your CREDENTIALS JSON.

    • ssl.key.password: Password for the SSL certificate key.

SASL Connection Settings
  1. In your CONFIG JSON, set "security.protocol": "sasl_ssl" for SSL connections, or "security.protocol": "sasl_plaintext" if you want to authenticate with Kafka without SSL encryption.

  2. If your Kafka brokers do not use SCRAM for authentication, set "sasl.mechanism": "PLAIN" in your CONFIG JSON. Otherwise, set "sasl.mechanism": "SCRAM-SHA-256" or "sasl.mechanism": "SCRAM-SHA-512".

  3. In your CONFIG JSON, provide the username, "sasl.username": "<kafka_credential_username>".

  4. In your CREDENTIALS JSON, provide the password, "sasl.password": "<kafka_credential_password>".

Note

SASL_PLAINTEXT/PLAIN authentication mode with Kafka sends your credentials unencrypted over the network. It is therefore not secure and susceptible to being sniffed.

SASL_PLAINTEXT/SCRAM authentication mode with Kafka will encrypt the credentials information sent over the network, but transport of Kafka messages themselves is not secure.

Kerberos Connection Settings
  1. In your CONFIG JSON, set "sasl.mechanism": "GSSAPI".

  2. Set "security.protocol": "sasl_ssl" for Kerberos and SSL connections, or "security.protocol": "sasl_plaintext" if you want to authenticate with Kerberos without SSL encryption.

  3. Set the remaining Kerberos configuration in CONFIG JSON:

    • sasl.kerberos.service.name: The Kerberos principal name that Kafka runs as. For example, "kafka".

    • sasl.kerberos.keytab: The local file path on the SingleStore node to the authenticating keytab.

    • sasl.kerberos.principal: The service principal name for the SingleStorecluster. For example, "memsql/host.example.com@EXAMPLE.COM".

Kafka Version Setting

Warning

Using SSL and SASL with Kafka requires Kafka protocol version 0.9 or later; therefore, CREATE PIPELINE ... KAFKA ... and SELECT ... INTO KAFKA ... statements using SSL and SASL with Kafka also need to adhere to that version requirement. The Kafka protocol version can be passed in through JSON through the CONFIG clause, similar to this CONFIG '{"kafka_version":"0.10.0.0"}'. Alternatively, the pipelines_kafka_version engine variable controls this parameter for any pipeline without using a Kafka version configuration value in a CREATE PIPELINE ... KAFKA ... statement.

Final Step: Use the Connection String in a SQL Statement

Create your CREATE PIPELINE ... KAFKA ... or SELECT ... INTO KAFKA ... statement, using the string containing the connection settings that you created in the previous steps.

Examples

The following examples make the following assumptions:

  • Port 9092 is a plaintext endpoint

  • Port 9093 is an SSL endpoint

  • Port 9094 is a plaintext SASL endpoint

  • Port 9095 is an SSL SASL endpoint

Note

The examples use CREATE PIPELINE, but the CONFIG and CREDENTIALS clauses shown can be used with SELECT ... INTO ... KAFKA also. SASL OAUTHBEARER is not supported with SELECT ... INTO ... KAFKA.

Plaintext

The following CREATE PIPELINE statements are equivalent:

CREATE PIPELINE `kafka_plaintext`
AS LOAD DATA KAFKA 'host.example.com:9092/test'
CONFIG '{"security.protocol": "plaintext"}'
INTO table t;
CREATE PIPELINE `kafka_no_creds`
AS LOAD DATA KAFKA 'host.example.com:9092/test'
INTO table t;

SSL

CREATE PIPELINE `kafka_ssl`
AS LOAD DATA KAFKA 'host.example.com:9093/test'
CONFIG '{"security.protocol": "ssl",
"ssl.certificate.location": "/var/private/ssl/client_memsql_client.pem",
"ssl.key.location": "/var/private/ssl/client_memsql_client.key",
"ssl.ca.location": "/var/private/ssl/ca-cert.pem"}'
CREDENTIALS '{"ssl.key.password": "abcdefgh"}'
INTO table t;

Kerberos with no SSL

CREATE PIPELINE `kafka_kerberos_no_ssl`
AS LOAD DATA KAFKA 'host.example.com:9094/test'
CONFIG '{""security.protocol": "sasl_plaintext",
"sasl.mechanism": "GSSAPI",
"sasl.kerberos.service.name": "kafka",
"sasl.kerberos.principal": "memsql/host.example.com@EXAMPLE.COM",
"sasl.kerberos.keytab": "/etc/krb5.keytab"}'
INTO table t

Kerberos with SSL

CREATE PIPELINE `kafka_kerberos_ssl`
AS LOAD DATA KAFKA 'host.example.com:9095/test'
CONFIG '{"security.protocol": "sasl_ssl",
"sasl.mechanism": "GSSAPI",
"ssl.certificate.location": "/var/private/ssl/client_memsql_client.pem",
"ssl.key.location": "/var/private/ssl/client_memsql_client.key",
"ssl.ca.location": "/var/private/ssl/ca-cert.pem",
"sasl.kerberos.service.name": "kafka",
"sasl.kerberos.principal": "memsql/host.example.com@EXAMPLE.COM",
"sasl.kerberos.keytab": "/etc/krb5.keytab"}'
CREDENTIALS '{"ssl.key.password": "abcdefgh"}'
INTO table t

SASL/PLAIN with SSL

CREATE PIPELINE `kafka_sasl_ssl_plain`
AS LOAD DATA KAFKA 'host.example.com:9095/test'
CONFIG '{"security.protocol": "sasl_ssl",
"sasl.mechanism": "PLAIN",
"ssl.certificate.location": "/var/private/ssl/client_memsql_client.pem",
"ssl.key.location": "/var/private/ssl/client_memsql_client.key",
"ssl.ca.location": "/var/private/ssl/ca-cert.pem",
"sasl.username": "kafka"}'
CREDENTIALS '{"ssl.key.password": "abcdefgh", "sasl.password": "metamorphosis"}'
INTO table t;

SASL/PLAIN without SSL

CREATE PIPELINE `kafka_sasl_plaintext_plain`
AS LOAD DATA KAFKA 'host.example.com:9094/test'
CONFIG '{"security.protocol": "sasl_plaintext",
"sasl.mechanism": "PLAIN",
"sasl.username": "kafka"}'
CREDENTIALS '{"sasl.password": "metamorphosis"}'
INTO table t;

SASL/SCRAM with SSL

CREATE PIPELINE `kafka_sasl_ssl_scram`
AS LOAD DATA KAFKA 'host.example.com:9095/test'
CONFIG '{"security.protocol": "sasl_ssl",
"sasl.mechanism": "SCRAM-SHA-512",
"ssl.certificate.location": "/var/private/ssl/client_memsql_client.pem",
"ssl.key.location": "/var/private/ssl/client_memsql_client.key",
"ssl.ca.location": "/var/private/ssl/ca-cert.pem",
"sasl.username": "kafka"}'
CREDENTIALS '{"ssl.key.password": "abcdefgh", "sasl.password": "metamorphosis"}'
INTO table t;

SASL/SCRAM without SSL

CREATE PIPELINE `kafka_sasl_plaintext_plain`
AS LOAD DATA KAFKA 'host.example.com:9094/test'
CONFIG '{"security.protocol": "sasl_plaintext",
"sasl.mechanism": "SCRAM-SHA-512",
"sasl.username": "kafka"}'
CREDENTIALS '{"sasl.password": "metamorphosis"}'
INTO table t;

Load Data from the Confluent Kafka Connector

The SingleStore Confluent Kafka Connector is a Kafka Connect connector that allows you to easily ingest AVRO, JSON, and CSV messages from Kafka topics into SingleStore. More specifically, the Confluent Kafka Connector is a Sink (target) connector designed to read data from Kafka topics and write that data to SingleStore tables.

Learn more about the SingleStore Confluent Kafka Connector, including how to install and configure it, in Working with the Kafka Connector.

Working with the Kafka Connector

To understand Kafka’s core concepts and how it works, please read the Kafka documentation. This guide assumes that you understand Kafka’s basic concepts and terminology, and that you have a working Kafka environment up and running.

The Confluent Kafka Connector is available via the Confluent Hub and as a download from SingleStore.

Note: After you have installed the version you want to use, you will need to configure the connector properties.

The rest of this page describes how the connector works.

Note: You can also use a pipeline to Load Data from Kafka Using a Pipeline.

Connector Behavior

See the SingleStore Kafka Connector for information about the connector.

Auto-creation of tables

While loading data, if the table does not exist in SingleStore, it will be created using the information from the first record.

The table name is the name of the topic. The table schema is taken from the record’s valueSchema. If valueSchema is not a struct, then a single column with name data will be created with the schema of the record. Table keys are taken from the tableKey property.

If the table already exists, all records will be loaded directly into it. Automatic schema changes are not supported, so all records should have the same schema.

Exactly once delivery

To achieve exactly once delivery, set singlestore.metadata.allow to true. The kafka_connect_transaction_metadata table will then be created.

This table contains an identifier, count of records, and time of each transaction. The identifier consists of kafka-topic, kafka-partition, and kafka-offset. This combination provides a unique identifier that prevents duplication of data in the SingleStore database. Kafka saves offsets and increases them only if the kafka-connect job succeeds. If the job fails, Kafka will restart the job with the same offset. This means that if the data is written to the database, but the operation fails, Kafka will try to write data with the same offset and metadata identifier to prevent duplication of existing data and simply complete the work successfully.

Data is written to the table and to the kafka_connect_transaction_metadata table in one transaction. Because of this, if an error occurs, no data is added to the database.

To overwrite the name of this table, use the singlestore.metadata.table property.

Data Types

The connector converts Kafka data types to SingleStore data types:

Kafka Type

SingleStore Type

STRUCT

JSON

MAP

JSON

ARRAY

JSON

INT8

TINYINT

INT16

SMALLINT

INT32

INT

INT64

BIGINT

FLOAT32

FLOAT

FLOAT64

DOUBLE

BOOLEAN

TINYINT

BYTES

TEXT

STRING

VARBINARY(1024)

Table Keys

To add a column as a key in SingleStore, use the tableKey property:

Suppose you have an entity:

{
"id" : 123,
"name" : "Alice"
}

If you want to add the id column as a PRIMARY KEY to your SingleStore table, add "tableKey.primary": "id" to your properties configuration.

Doing so will generate the following query during table creation:

CREATE TABLE IF NOT EXISTS `table` (
`id` INT NOT NULL,
`name` TEXT NOT NULL,
PRIMARY KEY (`id`)
)

You can also specify the name of a key by providing it like this: "tableKey.primary.someName" : "id".

This will create a key with a name:

CREATE TABLE IF NOT EXISTS `table` (
`id` INT NOT NULL,
`name` TEXT NOT NULL,
PRIMARY KEY `someName`(`id`)
)

Table Names

By default, the Kafka Connector maps data from topics into SingleStore tables by matching the topic name to the table name. For example, if the Kafka topic is called kafka-example-topic then the connector will load it into the SingleStore table called kafka-example-topic. The table will be created if it does not already exist.

To specify a custom table name, you can use the singlestore.tableName.<topicName> property.

{
...
"singlestore.tableName.foo" : "bar",
...
}

In this example, data from the Kafka topic foo will be written to the SingleStore table called bar.

You can use this method to specify custom table names for multiple topics:

{
...
"singlestore.tableName.kafka-example-topic-1" : "singlestore-table-name-1",
"singlestore.tableName.kafka-example-topic-2" : "singlestore-table-name-2",
...
}

Installing the SingleStore Kafka Connector via Confluent Hub

This guide shows you how to install and configure the SingleStore Kafka Connector in Confluent Hub, via the following process:

  1. Make sure you satisfy the prerequisites.

  2. Install the connector.

  3. Configure the connector.

Prerequisites

Make sure you have met the following prerequisites before installing the connector.

  • MemSQL version 6.8 or newer/SingleStore version 7.1 or newer installed and running.

Install the Connector and Add a Connection in Confluent

Install the SingleStore Kafka Connector via the Confluent Hub.

Run the Confluent Hub CLI installation command as described on the Confluent Hub:

Accept all of the default configuration options while installing.

Now that you have the connector installed, you can create a connection.

  1. Browse to the Confluent Control Center.

  2. Click Connect in the left side menu.

  3. Click Add Connector.

  4. Select SingleStore Sink Connector.

  5. Select the topics from which you want to get data.

  6. Configure the connector properties.

For an explanation of the various configuration properties, see SingleStore Kafka Connector Properties.

Installing the SingleStore Kafka Connector via Download

This guide shows you how to get and install the Java-based SingleStore Kafka Connector for connecting with open source Apache Kafka. The process looks like this:

  1. Make sure you satisfy the prerequisites.

  2. Download the connector JAR file.

  3. Configure the connector properties.

Prerequisites

Make sure you have met the following prerequisites before installing the connector.

  • MemSQL version 6.8 or newer/SingleStore version 7.1 or newer installed and running

  • Java Development Kit (JDK) installed

  • Apache Kafka installed and running

  • Kafka Schema Registry configured

  • Kafka Connect

  • For SingleStore Kafka Connector versions prior to 1.1.1, install the MariaDB JDBC driver

    For SingleStore Kafka Connector versions 1.1.1 and newer, Install/configure the latest version of the SingleStore JDBC driver

Download and the SingleStore Kafka Connector

Get the SingleStore Kafka Connector JAR file here.

You will need to have the JDK installed and configured, with JAVA_HOME pointing to where the JDK is installed.

See the README and the Quickstart files on the GitHub page for more information.

Configure the SingleStore Connector Properties

The connector is configurable via a property file or Kafka-REST. The properties should be specified before starting the kafka-connect job.

The connector properties include the standard Kafka properties as well as some SingleStore-specific properties.

For an explanation of the various SingleStore-specific configuration properties, see SingleStore Kafka Connector Properties.

SingleStore Kafka Connector Properties

Configuration for the connector is controlled via the SingleStore Kafka Connector Sink configuration properties.

Confluent users will configure these properties via the Confluent UI.

The properties listed below show the SingleStore-specific properties. For a complete list of properties refer to the Apache Kafka documentation.

SingleStore Kafka Connector Sink Configuration Properties

Property

Description

Default

connection.ddlEndpoint (required)

Hostname or IP address of the SingleStore Master Aggregator in the host[:port] format, where port is optional. For example: master-agg.foo.internal:3308 or master-agg.foo.internal.

connection.dmlEndpoints

Hostname or IP address of the SingleStore Aggregator nodes to run queries against in the host[:port],host[:port],… format, where port is optional and multiple hosts are separated by a comma. For example: child-agg:3308,child-agg2.

ddlEndpoint

connection.database (required)

If set, all connections will default to using this database.

empty)

connection.user

SingleStore username.

root

connection.password

SingleStore password.

no password

params.<name>

Specify a specific MySQL or JDBC parameter which will be injected into the connection URI.

empty

max.retries

The maximum number of times to retry on errors before failing the task.

10

retry.backoff.ms

The time in milliseconds to wait following an error before a retry attempt is made.

3000

tableKey.<index_type>[.name]

Specify additional keys to add to tables created by the connector; value of this property is the comma separated list with names of the columns to apply key; <index_type> one of (PRIMARY, COLUMNSTORE, UNIQUE, SHARD, KEY).

singlestore.loadDataCompression

Compress data on load; one of (GZip, LZ4, Skip).

GZip

singlestore.metadata.allow

Allows or denies the use of an additional meta-table to save the recording results.

true

singlestore.metadata.table

Specify the name of the table to save Kafka transaction metadata.

kafka_connect_transaction_metadata

singlestore.tableName.<topicName>=<tableName>

Specify an explicit table name to use for the specified topic.

Example Configuration

Refer to Config example for a sample configuration.

Load Data from Kafka Using a Pipeline

To create and interact with a Kafka pipeline quickly, follow the instructions in this section. There are two parts to this Quickstart:

  1. Part 1: Sending Messages to Kafka

  2. Part 2: Creating a Kafka Pipeline in SingleStore

Prerequisites

To complete this Quickstart, your environment must meet the following prerequisites:

  • A working Kafka queue.

  • SingleStore installation –or– a SingleStore cluster: You will connect to the database or cluster and create a pipeline to pull data from Kafka queue.

Part 1: Sending Messages to Kafka

In Kafka, create a topic named test and enter the following messages:

the quick
brown fox
jumped over
the lazy dog

In Part 2, you will create a pipeline in SingleStore to ingest these messages.

Part 2: Creating a Kafka Pipeline in SingleStore

Now that your Kafka topic contains some messages, you can create a new pipeline and ingest the messages.

At the SingleStore prompt, execute the following statements:

CREATE DATABASE quickstart_kafka;
USE quickstart_kafka;
CREATE TABLE messages (id text);

These statements create a new table and database that will be used for the Kafka pipeline. Before you can create the pipeline, you will need the IP address of the Kafka cluster.

To create the pipeline, execute the following statement, replacing <kafka-cluster-ip> with your Kafka cluster's IP address:

CREATE PIPELINE quickstart_kafka AS LOAD DATA KAFKA '<kafka-cluster-ip>/test' INTO TABLE messages;

If you are connecting with SSL, SASL, or Kerberos, you will need to specify additional clauses in your CREATE PIPELINE statement. See Securely Connect to Kafka for more information.

The CREATE PIPELINE statement just mentioned creates a new Kafka pipeline named quickstart_kafka, which reads messages from the test Kafka topic and writes it into the messages table. For more information, see the CREATE PIPELINE topic. If the statement was successful, you can test your pipeline. While you can start a pipeline after creating it, it’s always best to test it using a small set of data:

TEST PIPELINE quickstart_kafka LIMIT 1;

If this test was successful and no errors are present, then you are ready to try ingesting data. The following command will run one batch and commit the data to the SingleStore table messages.

START PIPELINE quickstart_kafka FOREGROUND LIMIT 1 BATCHES;

To verify that the data exists in the messages table as expected, execute the following statement. If it was successful, you should see a non-empty result set.

SELECT * FROM messages;
+--------------+
| id           |
+--------------+
| the quick    |
| brown fox    |
| jumped over  |
| the lazy dog |
+--------------+

Now you are ready to start your pipeline as a background process. SingleStore will automatically ingest new messages as they are put into Kafka.

START PIPELINE quickstart_kafka;

Now that the pipeline is up and running, send a few more messages to the Kafka topic. Enter the following lines into your topic:

Lorem ipsum
dolor sit amet

In the SingleStore terminal window, run the SELECT * FROM messages; statement again. Now you will see the following output:

SELECT * FROM messages;
+----------------+
| id             |
+----------------+
| lorem ipsum    |
| dolor sit amet |
| the quick      |
| brown fox      |
| jumped over    |
| the lazy dog   |
+----------------+

Now that your pipeline is running, you can check the status and history of it at any time by querying the PIPELINES_BATCHES_SUMMARY table.

SELECT * FROM information_schema.PIPELINES_BATCHES_SUMMARY;

This system view will give one row for every recent batch the pipeline has run, as well as at-a-glance performance and volume metrics. This information is extremely valuable for monitoring and understanding your production pipeline system.

Note

Foreground pipelines and background pipelines have different intended uses and behave differently. For more information, see the CREATE PIPELINE topic.

Note: When a backup is restored all pipelines in that database will revert to the state (offsets, etc.) they were in when the target backup was generated. If the data isn't in Kafka anymore you will start from the earliest offsets available in Kafka.

Test your Kafka Cluster using kcat (formerly kafkacat)

When you use SingleStore to consume or produce Kafka messages, you may want to test your Kafka cluster directly to verify connectivity and to verify topics and messages. kcat is a command line utility you can use to perform these tasks.

Note

kcat is available at https://github.com/edenhill/kcat. It is a third-party utility and SingleStore does not provide support for it.

Running kcat

Minimal syntax for running kcat is as follows.

kcat <mode> -b <broker> -t <topic>

When you run kcat, you may need to supply additional parameters, such as SASL settings to connect to your Kafka cluster. Specify these parameters using the -X option. Examples of these parameters are shown in the code samples below, which demonstrate how to connect to Confluent Cloud.

<mode>

One of the following:

  • -P: Producer mode. Used to produce messages to a Kafka topic (not demonstrated here).

  • -C: Consumer mode. Used to consume messages from a Kafka topic. Used here to test connectivity to a Kafka cluster and to verify that data is as expected.

  • -L: Metadata listing mode. Used here to test connectivity to a Kafka cluster.

  • -Q: Query mode. Not demonstrated here.

<broker>

The Kafka broker to connect to.

<topic>

The name of the topic to produce to, consume from, or list metadata from.

Verify connectivity to your Kafka Cluster

Running kcat in any mode, where the command returns successfully, is confirmation that you can connect. Following are two examples of how you can verify connectivity.

Verify Connectivity by Returning Metadata

In the following example, kcat connects to a Confluent Cloud cluster using -L mode to return metadata.

kcat -b <Confluent Cloud broker URL> -L \
-X security.protocol=SASL_SSL \
-X sasl.mechanism=PLAIN \
-X ssl.ca.location=<CA certificate file path> \
-X sasl.username=<CLUSTER_API_KEY> \
-X sasl.password=<CLUSTER_API_SECRET>
Metadata for all topics (from broker ...)

20 brokers:
...

10 topics:
...

Verify Connectivity by Consuming One Message from a Topic

In the following example, kcat connects to a Confluent Cloud cluster using -C mode to consume the last message from a topic.

kcat -C -b <Confluent Cloud broker URL> -t <topic> -o -1 -e \
-X security.protocol=SASL_SSL \
-X sasl.mechanism=PLAIN \
-X ssl.ca.location=<CA certificate file path> \
-X sasl.username=<CLUSTER_API_KEY> \
-X sasl.password=<CLUSTER_API_SECRET>

Verify data in your Kafka Cluster

Two situations where you may want to verify data in your Kafka cluster are:

  • Prior to using SingleStore to consume Kafka messages via Pipelines or the Confluent Kafka Connector.

  • After using SingleStore to produce Kafka messages (using SELECT ... INTO KAFKA ...).

In both situations, you can run kcat in -C mode for a given topic.

The following kcat command connects to a Confluent Cloud cluster using -C mode. The command outputs all of the messages for the specified topic, also showing the offset and partition for each message. The output is piped to the file kcat_output.txt. The -e option indicates that kcat should exit once it has consumed the last message from the topic.

kcat -C -b <Confluent Cloud broker URL> -t <topic> -e \
-f 'Partition: %p  Offset: %o  Message: %s\n' \
-X security.protocol=SASL_SSL \
-X sasl.mechanism=PLAIN \
-X ssl.ca.location=<CA certificate file path> \
-X sasl.username=<CLUSTER_API_KEY> \
-X sasl.password=<CLUSTER_API_SECRET>
> kcat_output.txt

Example: Verify messages in Kafka after producing messages using SELECT ... INTO KAFKA ...

Create a table with three columns.

CREATE TABLE numbers(a INT, b INT, c INT);

Insert data into the table.

INSERT INTO numbers(a,b,c) VALUES (10,20,30);
INSERT INTO numbers(a,b,c) VALUES (40,50,60);
INSERT INTO numbers(a,b,c) VALUES (70,80,90);

Use the SELECT ... INTO KAFKA ... command to create a Kafka message for each row of the numbers table. All of the messages are written to the topic numbers-topic in a Confluent Cloud cluster. The field values in each message correspond to the field values in the table, incremented by 10 , 20 or 30.

SELECT a + 10, b + 20, c + 30
FROM numbers INTO KAFKA '<Confluent Cloud broker URL>/numbers-topic'
CONFIG '{
"security.protocol":"SASL_SSL",
"sasl.mechanism":"PLAIN",
"ssl.ca.location":"<CA certificate file path>",
"sasl.username":"<CLUSTER_API_KEY>"
}'
CREDENTIALS '{"sasl.password":"<CLUSTER_API_SECRET>"}'
FIELDS TERMINATED BY ',' ENCLOSED BY '"' ESCAPED BY "\t"
LINES TERMINATED BY '}' STARTING BY '{';

Run the kcat command shown in the section "Verify data in your Kafka cluster", above. View the kcat_output.txt output file and search for {20,40,60} to verify that the message is in the file. You will see the other two messages following {20,40,60}. The messages in the output will look as follows. The partitions and offsets may differ.

Partition: 1 Offset: 0 Message: {20,40,60}
Partition: 1 Offset: 1 Message: {50,70,90}
Partition: 1 Offset: 2 Message: {80,100,120}
...

Troubleshooting Kafka AWS Pipelines

There are a number of factors that can contribute to Kafka pipeline errors. Some of the more common errors and solutions are provided below:

Error 1933: Cannot get source metadata for pipeline.

ERROR 1933 ER_EXTRACTOR_EXTRACTOR_GET_LATEST_OFFSETS: Cannot get source metadata for pipeline. Could not fetch Kafka metadata; are the Kafka brokers reachable from the Master Aggregator? ssl.ca.location is missing. Kafka error Local: Broker transport failure.

Error 1933 means that there is a communication error between SingleStore and the Kafka broker.

The cause could be something as simple as the Kafka broker is offline. Some things to check are:

  • Broker Status: Confirm Kafka broker is in an online state.

  • Port number: check to see if the port number is open and not in use by any other connection endpoint.

  • Hostname: check if there is a typo in the hostname.

  • Firewall: verify the firewall's rules are configured to allow incoming and outgoing traffic on the correct port(s).

  • SSL configs: ensure that the SSL configurations are not missing and are correct.

How to Analyze the Debugging Log

This section shows how to enable debugging (if not already enabled), how to analyze the output to determine the cause, and assist in solving the error.

  1. Enable the engine variable: pipelines_extractor_debug_logging for verbose error logging.

    singlestore> set global pipelines_extractor_debug_logging=ON;
    Query OK, 0 rows affected (0.02 sec)
  2. Flush the extractor pools.

    singlestore> flush extractor pools;
    Query OK, 0 rows affected (0.00 sec)
  3. Run the CREATE PIPELINE statement and wait to see if the pipeline fails. Below is a Kafka broker unreachable sample error.

    CREATE PIPELINE my_pipeline as
    LOAD DATA KAFKA 'ec2-xx-xxx-xx-xx.us-west-2.compute.amazonaws.com/singlestore'
    INTO TABLE messages;
    ERROR 1933 (HY000): Cannot get source metadata for pipeline.  
      Could not fetch Kafka metadata; are the Kafka brokers reachable from the Master Aggregator?  
      ssl.ca.location is missing. Kafka error Local: Broker transport failure.
  4. Run SHOW WARNINGS. The output provides information as to what is causing the error. As shown in this example output, there is no communication with the Kafka broker on ec2-xx-xxx-xx-xx.us-west-2.compute.amazonaws.com:9092. This may be due to no usable broker is present or the broker is not running.

    singlestore> SHOW WARNINGS;
    +---------+------+---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
    | Level   | Code | Message                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                         |
    +---------+------+---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
    | Warning | 1933 |
    --- TRUNCATED ---
     ec2-xx-xxx-xx-xx.us-west-2.compute.amazonaws.com:9092/bootstrap: Selected for cluster connection: application metadata request (broker has 567 connection attempt(s))
    CONNECT [thrd:ec2-xx-xxx-xx-xx.us-west-2.compute.amazonaws.com:9092/bootstrap]: 127.0.0.1:9092/bootstrap: Received CONNECT op
    STATE [thrd:ec2-xx-xxx-xx-xx.us-west-2.compute.amazonaws.com:9092/bootstrap]: 127.0.0.1:9092/bootstrap: Broker changed state INIT -> TRY_CONNECT
    BROADCAST [thrd:ec2-xx-xxx-xx-xx.us-west-2.compute.amazonaws.com:9092/bootstrap]: Broadcasting state change
    
    ...
    
    "FAIL [thrd:ec2-xx-xxx-xx-xx.us-west-2.compute.amazonaws.com:9092/bootstrap]: ec2-xx-xxx-xx-xx.us-west-2.compute.amazonaws.com:9092/bootstrap: Connect to ipv4#192.xxx.x.xx:9092 failed: Connection refused (after 55ms in state CONNECT) (_TRANSPORT): identical to last error: error log suppressed
    STATE [thrd:ec2-xx-xxx-xx-xx.us-west-2.compute.amazonaws.com:9092/bootstrap]: ec2-xx-xxx-xx-xx.us-west-2.compute.amazonaws.com:9092/bootstrap: Broker changed state CONNECT -> DOWN"
    "CONNECT [thrd:main]: Not selecting any broker for cluster connection: still suppressed for 36ms: refresh unavailable topics
    METADATA [thrd:main]: Hinted cache of 1/1 topic(s) being queried
    METADATA [thrd:main]: Skipping metadata refresh of 1 topic(s): refresh unavailable topics: no usable brokers
    CONNECT [thrd:main]: Not selecting any broker for cluster connection: still suppressed for 36ms: no cluster connection"
    "CONNECT [thrd:ec2-xx-xxx-xx-xx.us-west-2.compute.amazonaws.com:9092/bootstrap]: ec2-xx-xxx-xx-xx.us-west-2.compute.amazonaws.com:9092/bootstrap: broker in state TRY_CONNECT connecting
    STATE [thrd:ec2-xx-xxx-xx-xx.us-west-2.compute.amazonaws.com:9092/bootstrap]: ec2-xx-xxx-xx-xx.us-west-2.compute.amazonaws.com:9092/bootstrap: Broker changed state TRY_CONNECT -> CONNECT
    BROADCAST [thrd:ec2-xx-xxx-xx-xx.us-west-2.compute.amazonaws.com:9092/bootstrap]: Broadcasting state change"
  5. To turn off pipelines_extractor_debug_logging:

    singlestore> SET GLOBAL pipelines_extractor_debug_logging = OFF;
    Query OK, 0 rows affected (0.04 sec)

When the variable pipelines_extractor_debug_logging is OFF, the warning messages are concise but still helpful. The output does not show all the available information. When this variable is ON, there is more information. Often an internet search will yield a solution based on the information provided in the output.

Confirm Listeners in Kafka server.properties is Correct

If the error: Cannot get source metadata for pipeline. Could not fetch Kafka metadata keeps occurring on pipeline creation, confirm that the hostname is correct in the Kafka server.properties file.

For example, a pipeline was created using the following DDL statement:

CREATE PIPELINE kafka
AS LOAD DATA KAFKA
'ec2-xx-xxx-xx-xx.us-west-2.compute.amazonaws.com/singlestore'
INTO TABLE messages;

This pipeline creation statement resulted in the following error:

ERROR 1933 ER_EXTRACTOR_EXTRACTOR_GET_LATEST_OFFSETS: Cannot get
source metadata for pipeline. Could not fetch Kafka metadata; are the Kafka
brokers reachable from the Master Aggregator? Ssl.ca.location is missing.
Kafka error Local Broker transport failure

To rectify this error, the server.properties file will need to be edited to reflect the correct hostname and to check for any other missing or incorrect settings. The location of the server.properties file may differ based on the version of Linux and Kafka that are being used.

  1. Specify the broker’s hostname and include the port number it uses.

    listeners=PLAINTEXT://ec2-xx-xxx-xx-xx.us-west-2.compute.amazonaws.com:9092
  2. Restart the broker:

    ubuntu@kafka:~/kafka/config$ sudo systemctl restart kafka
  3. Confirm the broker is running:

    ubuntu@kafka:~/kafka/config$ sudo systemctl status kafka | grep Active
       Active: active (running) since Wed 2022-12-21 01:29:29 UTC; 48s ago
  4. Try running the create pipeline statement again:

    CREATE PIPELINE kafka
    AS LOAD DATA KAFKA'ec2-xx-xxx-xx-xx.us-west-2.compute.amazonaws.com/singlestore'
    INTO TABLE messages;
  5. Check if the pipeline is ingesting data from Kafka:

    STOP PIPELINE kafka;
    Test PIPELINE kafka LIMIT 1;
    singlestore> Test PIPELINE kafka limit 1;
    +----------------------+------------------------------------------------------------------------------------------------------+
    | id                   | tweet                                                                                                |                                                                                                                                                                                          
    +----------------------+------------------------------------------------------------------------------------------------------+ 
    | 1612846720953581568  | {"created_at":1673367598,"favorite_count":0,"id":1612846720953581568,"retweet_count":0,"text":"RT    |
    |                      | @YE0NSSI: never getting over mr. jazz and boracut https://t.co/sHjsOaQ21S","username":"angelyoongo"} |
    +----------------------+------------------------------------------------------------------------------------------------------+
    1 row in set (0.49 sec)

Failed to get watermark offsets from Kafka with error Local: Unknown partition

Troubleshooting an Error 1933 error when trying to create a pipeline:

singlestore> create pipeline debug as load data kafka
ec2-xx-xxx-xx-xx.us-west-2.compute.amazonaws.com:9092,
ec2-xx-xxx-xx-xx.us-west-2.compute.amazonaws.com:9093/multibroker'
into table messages;
ERROR 1933 (HY000): Cannot get source metadata for pipeline. 
Failed to get watermark offsets from Kafka with error Local:
Unknown partition

Confirm all Kafka Brokers are Running

One potential cause of the watermark offsets error is the Kafka broker is not running.

In the example error above there are two brokers:

ec2-xx-xxx-xx-xx.us-west-2.compute.amazonaws.com:9092
ec2-xx-xxx-xx-xx.us-west-2.compute.amazonaws.com:9093

Confirm all brokers are running using the command sudo systemctl status <broker_name>. For demonstration purposes the broker names are broker1 and broker2.

The Kafka broker1 is active and running, broker2 is active but failed.

ubuntu@kafka:~/kafka/scripts$ sudo systemctl status broker1 | grep
Active
     Active: active (running) since Wed 2023-01-04 01:01:56 UTC; 14min ago
ubuntu@kafka:~/kafka/scripts$ sudo systemctl status broker2 | grep
Active
     Active: failed (Result: exit-code) since Wed 2023-01-04 01:01:04 UTC; 15min ago

Start the offline broker using the command: sudo systemctl start <broker_name>;.

ubuntu@kafka:~/kafka/scripts$ sudo systemctl start broker2
ubuntu@kafka:~/kafka/scripts$ sudo systemctl status broker2 | grep Active
   Active: active (running) since Wed 2023-01-04 01:26:21 UTC; 1min 14s ago

Once all the brokers have been started and are running, creating a pipeline should work.

singlestore> CREATE PIPELIN debug AS LOAD DATA kafka
'ec2-xx-xxx-xx-xx.us-west-2.compute.amazonaws.com:9092,
ec2-xx-xxx-xx-xx.us-west-2.compute.amazonaws.com:9093/multibroker'
INTO TABLE messages;Query OK, 0 rows affected (0.35 sec)

Note

If all the brokers are in an offline state, the error would be: Error 1933:  Cannot get source metadata for pipeline.

ERROR 1933: Cannot get source metadata for pipeline.  Zero partitions for topic.  Topic may not exist, or it may still be being created.

The error shown below is  likely caused by a problem with the topic or the topic is still recovering from a broker reboot.

singlestore> system date
Thu Jan  5 23:38:30 UTC 2023
singlestore> CREATE PIPELINE debug AS LOAD DATA kafka
'ec2-xx-xxx-xx-xx.us-west-2.compute.amazonaws.com:9092/debug'
INTO TABLE messages;
ERROR 1933 (HY000): Cannot get source metadata for pipeline. 
Zero partitions for topic.  Topic may not exist, or it may still
be being created.

Check Kafka’s server.log to cross-reference the timestamp when the error occurred with the information present in the Kafka logging. The log should provide helpful information regarding the error. In the  example below, there are traces about topic recovery:

ubuntu@kafka:~/kafka/logs$ rg debug server.log | rg -v multibroker | rg -v test | rg 23:3
[2023-01-05 23:38:29,975] INFO [LogLoader partition=debug-1, dir=/tmp/kafka-logs-broker0] Loading producer state till offset 51457 with message format version 2 (kafka.log.Log$)
[2023-01-05 23:38:29,975] INFO [LogLoader partition=debug-1, dir=/tmp/kafka-logs-broker0] Reloading from producer snapshot and rebuilding producer state from offset 51457 (kafka.log.Log$)
[2023-01-05 23:38:29,998] INFO [ProducerStateManager partition=debug-1] Loading producer state from snapshot file 'SnapshotFile(/tmp/kafka-logs-broker0/debug-1/00000000000000051457.snapshot,51457)' (kafka.log.ProducerStateManager)
[2023-01-05 23:38:30,034] INFO [LogLoader partition=debug-1, dir=/tmp/kafka-logs-broker0] Producer state recovery took 59ms for snapshot load and 0ms for segment recovery from offset 51457 (kafka.log.Log$)
[2023-01-05 23:38:30,049] INFO Completed load of Log(dir=/tmp/kafka-logs-broker0/debug-1, topicId=FeqyeH2ZR_28aMF1x40Z-g, topic=debug, partition=1, highWatermark=0, lastStableOffset=0, logStartOffset=0, logEndOffset=51457) with 1 segments in 107ms (5/17 loaded in /tmp/kafka-logs-broker0) (kafka.log.LogManager)
[2023-01-05 23:38:30,300] INFO [LogLoader partition=debug-0, dir=/tmp/kafka-logs-broker0] Loading producer state till offset 48543 with message format version 2 (kafka.log.Log$)
[2023-01-05 23:38:30,300] INFO [LogLoader partition=debug-0, dir=/tmp/kafka-logs-broker0] Reloading from producer snapshot and rebuilding producer state from offset 48543 (kafka.log.Log$)
[2023-01-05 23:38:30,300] INFO [ProducerStateManager partition=debug-0] Loading producer state from snapshot file 'SnapshotFile(/tmp/kafka-logs-broker0/debug-0/00000000000000048543.snapshot,48543)' (kafka.log.ProducerStateManager)
[2023-01-05 23:38:30,301] INFO [LogLoader partition=debug-0, dir=/tmp/kafka-logs-broker0] Producer state recovery took 1ms for snapshot load and 0ms for segment recovery from offset 48543 (kafka.log.Log$)
[2023-01-05 23:38:30,315] INFO Completed load of Log(dir=/tmp/kafka-logs-broker0/debug-0, topicId=FeqyeH2ZR_28aMF1x40Z-g, topic=debug, partition=0, highWatermark=0, lastStableOffset=0, logStartOffset=0, logEndOffset=48543) with 1 segments in 39ms (12/17 loaded in /tmp/kafka-logs-broker0) (kafka.log.LogManager)
[2023-01-05 23:38:35,951] INFO [Partition debug-0 broker=0] Log loaded for partition debug-0 with initial high watermark 48543 (kafka.cluster.Partition)
[2023-01-05 23:38:36,414] INFO [Partition debug-1 broker=0] Log loaded for partition debug-1 with initial high watermark 51457 (kafka.cluster.Partition)

A recovery topic is usually solved by running the CREATE PIPELINE command again.

singlestore> CREATE PIPELINE debug AS LOAD DATA kafka
'ec2-xx-xxx-xx-xx.us-west-2.compute.amazonaws.com:9092/debug'
INTO TABLE messages;
Query OK, 0 rows affected (0.05 sec)

Cannot get source metadata for pipeline.  Failed to get watermark offsets from Kafka with error Broker: Unknown topic or partition

If an error such as: Cannot get source metadata for pipeline.  Failed to get watermark offsets from Kafka with error Broker: Unknown topic or partition in the information_schema.pipelines_errors table start by checking the Kafka broker(s) server.log around the same time as the error.

*** 1. row ***
            DATABASE_NAME: kafka
           PIPELINE_NAME: debug
     ERROR_UNIX_TIMESTAMP: 1672960138.923225
               ERROR_TYPE: Error
               ERROR_CODE: 1933
            ERROR_MESSAGE: Cannot get source metadata for pipeline.  
Failed to get watermark offsets from Kafka with error Broker: Unknown 
topic or partition
               ERROR_KIND: Extract
                STD_ERROR: 2023-01-05 23:07:51.811 Batch starting 
with new consumer.  rd_kafka_version_str: 1.8.2-PRE6-4-gecdd4d-dirty
2023-01-05 23:08:58.869 Failed to get watermark offsets from Kafka
with error Broker: Unknown topic or partition_$_SERVER_ERROR: 
Failed to get watermark offsets from Kafka with error Broker: 
Unknown topic or partition

In the Kafka server.log there are recovery type traces around the time of the error in the information_schema.pipelines_errors table.

[2023-01-05 23:08:58,276] INFO Kafka version: 3.0.0 (org.apache.kafka.common.utils.AppInfoParser)
[2023-01-05 23:08:58,277] INFO Kafka commitId: 8cb0a5e9d3441962 (org.apache.kafka.common.utils.AppInfoParser)
[2023-01-05 23:08:58,277] INFO Kafka startTimeMs: 1672960138244 (org.apache.kafka.common.utils.AppInfoParser)
[2023-01-05 23:08:58,279] INFO [KafkaServer id=0] started (kafka.server.KafkaServer)
[2023-01-05 23:08:58,581] INFO [BrokerToControllerChannelManager broker=0 name=alterIsr]:
Recorded new controller, from now on will use broker ec2-xx-xxx-xx-xx.us-west-2.compute.amazonaws.com:9092
(id: 0 rack: null) (kafka.server.BrokerToControllerRequestThread)
[2023-01-05 23:08:58,642] INFO [BrokerToControllerChannelManager broker=0 name=forwarding]:
Recorded new controller, from now on will use broker ec2-xx-xxx-xx-xx.us-west-2.compute.amazonaws.com:9092
(id: 0 rack: null) (kafka.server.BrokerToControllerRequestThread)
[2023-01-05 23:08:59,279] INFO [Partition debug-0 broker=0] Log loaded for partition debug-0 with initial high watermark 48543 (kafka.cluster.Partition)

Once the broker is online and fully recovered, confirm if the problematic Kafka pipeline has stop printing the unknown topic or partition errors to the information_schema.pipelines_errors table.

Last modified: October 4, 2023

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