Logical topics

Conduktor logical topics are abstractions of real Kafka topics that provide additional functionality that’s not available by default. We offer three types of logical topics:

Alias topics are topics that can be accessed with a name, but point to another real topic behind the scenes. They can be useful in a few scenarios such as topic renaming or cross-virtual cluster topic sharing.
Concentrated topics allow co-locating multiple topics in the same physical topic behind the scenes. This is very useful when you want to regroup many topics with low-volume but a large number of partitions.
SQL topics are using SQL language to query and filter an existing topic. This is very useful when filtering out the records that don’t correspond to your business needs.
Unlimited topics allow you to represent data from a set of topics on a single underlying topic. This means that you won’t overuse your Kafka resources.

Alias topics

Alias topics are logical topics that target a different physical topic. They act like pointers to another physical topic.

Purpose

One of Kafka’s limitations is that you can’t rename topics.

Use

Gateway manages an alias topic mapping in it’s internal configuration by registering a target physical topic. This topic will be presented to Kafka clients like a regular topic. However, all requests for this topic will be forwarded to the physical topic. This means that consumer groups, fetch and produce are shared. Also, the alias topic will not replace the original one. For example, if you create an alias topic, applicationB_orders pointing to a physical topic orders, a client that’s able to access the physical one would be able to see both topics.

Limitations

ACLs using delegated Kafka security SASL delegated security protocols aren’t supported.
Alias topics can’t reference another alias topic.

Concentrated topics

Concentrated topics transparently act as pointers to a single physical topic on your Kafka cluster. They allow you to reduce costs on low-volume topics by co-locating messages. They are completely transparent to consumers and producers and allow you to emulate different partition counts irrespective of the backing physical topic’s partition count.

Use

To create concentrated topics, first deploy ConcentrationRule:

---
kind: ConcentrationRule
metadata:
  name: concentration1
spec:
  pattern: concentrated.*
  physicalTopics:
    delete: physical.topic

Then topics that match the ConcentrationRule spec.pattern:

kafka-topics \
  --bootstrap-server conduktor-gateway:6969 \
  --topic concentrated.topicA \
  --partitions 3

kafka-topics \
  --bootstrap-server conduktor-gateway:6969 \
  --topic concentrated.topicB \
  --partitions 4

We now have two concentrated topics (concentrated.topicA and concentrated.topicB) with partition counts of 3 and 4 respectively, mapped to a single physical topic (physical.topic) with three partitions. To ensure that consumers don’t consume messages from other partitions or from other concentrated topics, we store the concentrated partition and the concentrated topic name in the record headers. Gateway will automatically filter the messages that should be returned to the consumer.

Limitations

Compact and delete+compact topics

You can create concentrated topics with any cleanup.policy, but your ConcentrationRule has to have a backing topic for each of them, otherwise it won’t let you create the topic.

---
kind: ConcentrationRule
metadata:
  name: concentration1
spec:
  pattern: concentrated.*
  physicalTopics:
    delete: physical.topic-delete
    compact: physical.topic-compact
    # deleteCompact: physical.topic-deletecompact

In this example, since the config for spec.deleteCompact is commented out, trying to create this topic will fail:

kafka-topics --create 
    --bootstrap-server conduktor-gateway:6969 \
    --topic <your-topic-name> \
    --partitions 3 \
    --config cleanup.policy=compact,delete
Error while executing topic command : Cleanup Policy is invalid

Backing topic cleanup policies are checked when you deploy a new ConcentrationRule. This prevents you from declaring a backing topic with a cleanup.policy of delete on the ConcentrationRule spec.physicalTopic.compact field.

Restricted topic configurations

The following list of topic properties are the only allowed properties for concentrated topics:

partitions
cleanup.policy
retention.ms
retention.bytes
delete.retention.ms

If any other configuration than the above is set, the topic creation will fail with an error. retention.ms and retention.bytes can be set to values lower or equal to the backing topic. If a user tries to create a topic with a higher value, topic creation will fail with an error:

kafka-topics --create 
    --bootstrap-server conduktor-gateway:6969 \
    --topic <your-topic-name> \
    --partitions 3 \
    --config retention.ms=704800000
Error while executing topic command : Value '704800000' for configuration 'retention.ms' is incompatible with physical topic value '604800000'.

This behavior can be altered with the flag spec.autoManaged.

With concentrated topics, the enforced retention policy is the physical topic’s retention policy, and not the policy requested at the concentrated topic creation time. The retention.ms and retention.bytes are not cleanup but retention guarantees.

Auto-managed backing topics

When autoManaged is enabled:

backing topics are automatically created with the default cluster configuration and partition count.
concentrated topics created with higher retention.ms and retention.bytes are allowed. This automatically extends the configuration of the backing topic.

---
kind: ConcentrationRule
metadata:
  name: concentration1
spec:
  pattern: concentrated.*
  physicalTopics:
    delete: physical.topic
  autoManaged: true

Let’s check the backing topic retention on the physical cluster:

kafka-configs --bootstrap-server kafka:9092 \
    --entity-type topics --entity-name physical.topic \
    --describe
Configs for topic 'physical.topic' are:
  cleanup.policy=delete
  retention.ms=604800000
  retention.bytes=-1

Let’s try to create a concentrated topic with a higher retention on Gateway:

kafka-topics --create 
    --bootstrap-server conduktor-gateway:6969 \
    --topic <your-topic-name> \
    --partitions 3 \
    --config retention.ms=704800000

Let’s review the backing topic again:

kafka-configs --bootstrap-server kafka:9092 \
    --entity-type topics --entity-name physical.topic \
    --describe
Configs for topic 'physical.topic' are:
  cleanup.policy=delete
  retention.ms=704800000
  retention.bytes=-1

As we can see, the retention has been updated.

If one user requests a topic with infinite retention (retention.ms = -1), all the topics with the same cleanup policy associated with the rule will also inherit this extended configuration and have infinite retention.

Message count, lag and offset (in)correctness

By default, concentrated topic reports the offsets of their backing topics. This impacts the calculations of Lag and Message Count that relies on partition EndOffset and group CommitedOffset. Offset Incorrectness

Any tooling will currently display the message count, and the lag relative to the EndOffset of the physical topic. This can create confusion for customers and applications that will see incorrect metrics. To counter this, we’ve implemented a dedicated offset management capability for ConcentrationRules. To enable virtual offsets, add the following line to the ConcentrationRule:

---
kind: ConcentrationRule
metadata:
  name: concentration1
spec:
  pattern: concentrated.*
  physicalTopics:
    delete: physical.topic
  offsetCorrectness: true

Note that:

spec.offsetCorrectness only applies to concentrated topics with the cleanup.policy=delete
spec.offsetCorrectness is not retroactive on previously created Concentrated Topics

Known issues and limitations with offset correctness

Performance

On startup, Gateway has to read the concentrated topic entirely before it’s available to consumers. The end-to-end latency is increased by up to 500 ms (or fetch.max.wait.ms, if non-default).

Memory impact

Gateway consumes about ~250MB of heap memory per million records it’s read in concentrated topics. This value is not bound, so we don’t recommend offset correctness on high-volume topics, and recommend to size your JVM accordingly.

Unsupported Kafka API

DeleteRecords is not supported
Transactions are not supported
Only IsolationLevel.READ_UNCOMMITTED is supported (using IsolationLevel.READ_COMMITTED is undefined behavior)
Partition truncation (upon unclean.leader.election=true) may not be detected by consumers

Very slow consumer group edge case

Do not enable offset correctness when your topic has extended periods of inactivity.

When using topic concentration with offsetCorrectness enabled, there’s currently a limitation for consumer groups where the data in the topics is slow moving, and/or the consumer groups are not committing their offsets frequently. If a consumer group with a committed offset waits for the backing physical topic longer than the retention time (without committing a new offset), there’s a possibility for that consumer group to become blocked. In this scenario, a consumer group whose last committed offset has been removed from the topic, the group becomes blocked only if Gateway restarted before the next offset commit. If this limitation happens, the offsets for the affected consumer group will need to be manually reset for it to continue.

SQL topics

Conduktor Gateway’s SQL topic feature uses a SQL-like language to filter and project messages, based on a simple SQL statement:

SELECT
    type,
    price as amount,
    color,
    CASE
        WHEN color = 'red' AND price > 1000 THEN 'Exceptional'
        WHEN price > 8000 THEN 'Luxury'
        ELSE 'Regular'
    END as quality,
    record.offset as record_offset,
    record.partition as record_partition
  FROM cars

This supports FetchResponse only (i.e., resulting topic is read-only): SELECT [list of fields] FROM [topic name] WHERE [field filter criteria]

Topic names with dash - characters have to be double quoted, as the dash is not a valid character for a SQL name. For example, if you have a topic our-orders, use SELECT * FROM "our-orders" WHERE ...

Other limitations:

With filter records based on more than one condition, only AND operator is supported
Supported predicates: =, >, >=, <, <=, <> and REGEXP (RegExp MySQL Operator)
Case expression is supported
Filtered by:
- Record key (It supports SR):
  - Record key as string: - .. WHERE record.key = 'some thing'
  - Record key as schema: .. WHERE record.key.someValue.someChildValue = 'some thing'
- Record value (It supports SR): .. WHERE $.someValue.someChildValue = 'some thing'
- Partition: .. WHERE record.partition = 1
- Timestamp: .. WHERE record.timestamp = 98717823712
- Header: .. WHERE record.header.someHeaderKey = 'some thing'
- Offset: .. WHERE record.offset = 1

Schemas and projections

If your data uses a schema, then it’s not possible to make use of the projection feature here because the resulting data will no longer match the original schema. For plain JSON topics, you can use the SELECT clause to alter the shape of the data returned; however, for schema’d data (Avro and Protobuf) you must not use a projection, i.e. the select should be in the form: SELECT * FROM ... Filtering with the where clause is still supported.

Configuration

Key	Type	Description
virtualTopic	String	if virtualTopic exists, fetch this topic will get the data from the statement without configure it’s own statement.
statement	String	SQL Statement
schemaRegistryConfig	Schema Registry	Schema Registry Config

Schema registry

Key	Type	Default	Description
`type`	string	`CONFLUENT`	The type of schema registry to use: choose `CONFLUENT` (for Confluent-like schema registries including OSS Kafka) or `AWS` for AWS Glue schema registries.
`additionalConfigs`	map		Additional properties maps to specific security-related parameters. For enhanced security, you can hide the sensitive values using environment variables as secrets.
Confluent Like			Configuration for Confluent-like schema registries
`host`	string		URL of your schema registry.
`cacheSize`	string	`50`	Number of schemas that can be cached locally by this interceptor so that it doesn’t have to query the schema registry every time.
AWS Glue			Configuration for AWS Glue schema registries
`region`	string		The AWS region for the schema registry, e.g. `us-east-1`
`registryName`	string		The name of the schema registry in AWS (leave blank for the AWS default of `default-registry`)
`basicCredentials`	string		Access credentials for AWS (see below for structure)
AWS Credentials			AWS credentials configuration
`accessKey`	string		The access key for the connection to the schema registry.
`secretKey`	string		The secret key for the connection to the schema registry.
`validateCredentials`	bool	`true`	`true` / `false` flag to determine whether the credentials provided should be validated when set.
`accountId`	string		The Id for the AWS account to use.

If you don’t supply a basicCredentials section for the AWS Glue schema registry, the client used to connect will attempt to find the connection information from the environment. The required credentials can be passed to Gateway in this way as part of core configuration. Find out more in AWS documentation.

Example

{
  "name": "mySqlTopicPlugin",
  "pluginClass": "io.conduktor.gateway.interceptor.VirtualSqlTopicPlugin",
  "priority": 100,
  "config": {
    "virtualTopic": "legal_user",
    "statement": "SELECT * FROM users WHERE age > 18",
    "schemaRegistryConfig": {
       "host": "http://schema-registry:8081"
    }
  }
}

Schema Registry with secured template

{
  "name": "mySqlTopicPlugin",
  "pluginClass": "io.conduktor.gateway.interceptor.VirtualSqlTopicPlugin",
  "priority": 100,
  "config": {
    "virtualTopic": "legal_user",
    "statement": "SELECT * FROM users WHERE age > 18",
    "schemaRegistryConfig": {
      "host": "http://schema-registry:8081",
      "additionalConfigs": {
        "schema.registry.url": "${SR_URL}",
        "basic.auth.credentials.source": "${SR_BASIC_AUTH_CRED_SRC}",
        "basic.auth.user.info": "${SR_BASIC_AUTH_USER_INFO}"
      }
    }
  }
}

Filter topics with CEL

Conduktor Gateway’s CEL topic feature uses CEL (Common Expression Language) expression to filter messages, based on a simple CEL Expression in the form. Currently

Filtered by:
- Record key (It supports SR):
  - Record key as string: - .. record.key == 'some thing'
  - Record key as schema: .. record.key.someValue.someChildValue == 'some thing'
- Record value (It supports SR): .. record.value.someValue.someChildValue == 'some thing'
- Partition: .. record.partition == 1
- Timestamp: .. record.timestamp == 98717823712
- Header: .. record.header.someHeaderKey == 'some thing'
- Offset: .. record.offset == 1

Configuration

key	type	description
virtualTopic	String	When accessed, this virtual topic retrieves filtered data from the specified client topic, applying a CEL expression for data filtering.
topic	String	Specifies the client topic from which data is fetched.
expression	String	A CEL expression that returns BOOLEAN to filter data. This determines which data from the topic is relevant based on the given criteria.
schemaRegistryConfig	Schema Registry	Schema Registry Config
celCacheSize	int	In memory cache size for cel expressions, balancing speed and resource use, optimize performance.

Schema Registry

Key	Type	Default	Description
`type`	string	`CONFLUENT`	The type of schema registry to use: choose `CONFLUENT` (for Confluent-like schema registries including OSS Kafka) or `AWS` for AWS Glue schema registries.
`additionalConfigs`	map		Additional properties maps to specific security-related parameters. For enhanced security, you can hide the sensitive values using environment variables as secrets.
Confluent Like			Configuration for Confluent-like schema registries
`host`	string		URL of your schema registry.
`cacheSize`	string	`50`	Number of schemas that can be cached locally by this interceptor so that it doesn’t have to query the schema registry every time.
AWS Glue			Configuration for AWS Glue schema registries
`region`	string		The AWS region for the schema registry, e.g. `us-east-1`
`registryName`	string		The name of the schema registry in AWS (leave blank for the AWS default of `default-registry`)
`basicCredentials`	string		Access credentials for AWS (see below section for structure)
AWS Credentials			AWS Credentials Configuration
`accessKey`	string		The access key for the connection to the schema registry.
`secretKey`	string		The secret key for the connection to the schema registry.
`validateCredentials`	bool	`true`	`true` / `false` flag to determine whether the credentials provided should be validated when set.
`accountId`	string		The Id for the AWS account to use.

Example

{
  "name": "myCelTopicPlugin",
  "pluginClass": "io.conduktor.gateway.interceptor.CelTopicPlugin",
  "priority": 100,
  "config": {
    "virtualTopic": "legal_user",
    "topic": "users",
    "expression": "record.value.age > 18",
    "schemaRegistryConfig": {
      "host": "http://schema-registry:8081"
    },
    "celCacheSize": 100
  }
}

Schema Registry with secured template

{
  "name": "myCelTopicPlugin",
  "pluginClass": "io.conduktor.gateway.interceptor.CelTopicPlugin",
  "priority": 100,
  "config": {
    "virtualTopic": "legal_user",
    "topic": "users",
    "statement": "record.value.age > 18",
    "schemaRegistryConfig": {
      "host": "http://schema-registry:8081",
      "additionalConfigs": {
        "schema.registry.url": "${SR_URL}",
        "basic.auth.credentials.source": "${SR_BASIC_AUTH_CRED_SRC}",
        "basic.auth.user.info": "${SR_BASIC_AUTH_USER_INFO}"
      }
    },
    "celCacheSize": 100
  }
}

Unlimited topics

Occasionally, topics have to be created for logical, rather than technical reasons (e.g. to differentiate between business units) which can result in considerable overuse of Kafka resources. Conduktor’s topic concentration allows data from a set of topics to be represented on a single underlying topic. Clients connecting through Conduktor Gateway can use concentrated topics as usual without any additional configuration. For example, let’s say we have the following topics:

us_east_orders - 100 partitions
us_west_orders - 100 partitions
emea_orders - 100 partitions
latam_orders - 100 partitions

The total Kafka resource requirement is 400 partitions. With topic concentration, all of these topics can be concentrated to a single topic, using only 1/4 of resources:

concentrated_orders - 100 partitions

Configuration

Topic concentration is configured via Gateway Enterprise Edition’s admin HTTP API.

Property name	Type	Description
logicalTopicRegex	string	A regular expression to match all topic names that will be concentrated, specified as a URL parameter
topicName	string	The name of the underlying topic to concentrate to

Example

This will match all the topics that end with orders and concentrate them to concentrated_orders.

curl
  -u "superUser:superUser"
  --request POST 'conduktor-gateway:8888/topicMappings/someTenant/.*orders'
  --header 'Content-Type: application/json'
  --data-raw '{
    "topicName": "concentrated_orders",
    "isConcentrated": true
  }'

Consumer offsets

When consuming from a concentrated topic, messages and ordering is always preserved but any metadata calculations (primarily lag and message count) are unlikely to be as expected. This is because the associated metadata is from the backing Kafka topic, rather than the concentrated topic seen from the perspective of the consumer. This is a known limitation.

Conduktor concepts

​Logical topics

​Alias topics

​Purpose

​Use

​Limitations

​Concentrated topics

​Use

​Limitations

Compact and delete+compact topics

Restricted topic configurations

​Auto-managed backing topics

​Message count, lag and offset (in)correctness

​Known issues and limitations with offset correctness

Performance

Memory impact

Unsupported Kafka API

Very slow consumer group edge case

​SQL topics

​Schemas and projections

​Configuration

​Schema registry

​Example

​Schema Registry with secured template

​Filter topics with CEL

​Configuration

​Schema Registry

​Example

​Schema Registry with secured template

​Unlimited topics

​Configuration

​Example

​Consumer offsets

​Related resources

Logical topics

Alias topics

Purpose

Use

Limitations

Concentrated topics

Use

Limitations

Auto-managed backing topics

Message count, lag and offset (in)correctness

Known issues and limitations with offset correctness

SQL topics

Schemas and projections

Configuration

Schema registry

Example

Schema Registry with secured template

Filter topics with CEL

Configuration

Schema Registry

Example

Schema Registry with secured template

Unlimited topics

Configuration

Example

Consumer offsets

Related resources