Topic list

The Topic page lets you search for any topic on your currently selected Kafka cluster.

To restrict user/group access to view or manage certain topics, configure RBAC.

Multiple search capabilities can be combined to help you find to the topic you want faster. Filtering is possible on:

Topic name (case-insensitive, matches any part of the name; wildcards and regular expressions aren’t supported)
Show/Hide Internal topics (starts with _)
Show/Hide Kafka stream topics (ends with -repartition or -changelog)
Cleanup policy
Labels (click on a label to add/remove it from the filters. Find out about managing topic labels).

Sorting is possible on all columns. Use the Display menu to choose the active columns and the number of rows per page (20, 50 or 100).

Your current filters, active sort, visible columns and rows per page are stored in your browser’s local storage for each Kafka cluster and persist across sessions.

Operations

Several actions are also available from the topic List: create, empty or delete topics and add partitions.

Create topic

Click New topic to open the Create topic panel, where you can provide all the necessary information to create a topic.

The default choices made by are generally safe for most typical Kafka production deployments. Find out about choosing the replication factor and partition count and Kafka cleanup policies .

Start from template If your platform team has defined topic templates, pick one from Start from template to pre-fill the form with a vetted set of defaults. You can change any value before you create the topic. A template may include {{placeholder}} values, such as a team name — replace them before you submit. See a topic template pre-fill the Create Topic form:

This is an excerpt from the full templates walkthrough. Topic name As per Kafka specification , topic name must only contain the following characters [a-zA-Z0-9._-] and not exceed 249 characters. Partitions This lets you define how scalable your topic will be for your consumers. In general you want a multiple of your number of brokers. Default: 3 Replication factor This configuration helps prevent data loss by writing the same data to more than one broker. Default: min (3, number of brokers)

Use the Insights dashboard to identify topics with low replication factors or partition distribution issues that may pose risks to your infrastructure.

Labels Use labels to organize your topics and facilitate searching them in Console. Each label is a key-value pair. Advanced configuration Enable this option to use all the available options, including the Cleanup policy, which controls how the retention of your messages is done. Find out more about the Apache Kafka topic configuration

Add partitions

Increase the number of partitions for your topic. Number of partitions cannot be decreased.

Adding partitions reshuffles the target partition of messages with a given key. Existing data will stay on the previous partition. Consumers that rely on partition ordering could be impacted.

Empty topic

This lets you delete all records from a topic. This operation is permanent and irreversible. If you want to only delete all records from given partition, there’s a dedicated operation on the partitions tab of the topic detail.

Delete topic

This lets you delete the topic from Kafka. This operation is permanent and irreversible.

Manage topic labels

You can help categorize your topics further using key-value pairs called labels. To manage your topic’s labels via the UI, open the topic and click + Add label (or Edit if the topic already has labels) from the topic details view.

A side panel shows the current labels of the topic and a button to add more. To add a new key or value, type it and select Create new label key (or value) from the list. To remove a label, click the trash icon.

Anyone who can view a topic can edit its labels and description by default. To restrict editing to users with a dedicated permission, enable metadata governance.

Topic produce overview

Configure producer
Produced messages panel
Operations

The produce page lets you configure all the details necessary to produce a record in your current Kafka topic. It is already configured with sensible defaults that you can customize if necessary.

Configure producer

Each section from the accordion menu will allow you to configure the Kafka producer further: key, value, headers, flow and additional options.

Key and value

This section is similar for both key and value. The menu lets you choose your serializer to encode your message: String, Boolean, Bytes, Short, Integer, Long, Float, Double, JSON, UUID, Null, or Avro, Json or Protobuf when a schema registry is connected.

The default serializer is String for the key and JSON for the value, unless you have a matching subject name in your schema registry (with the TopicNameStrategy). In that case, the serializer will be set automatically to the proper registry type (Avro, Json or Protobuf) with the subject name of <topic-name>-key for the key and <topic-name>-value for the value.

Confluent subject strategies

When producing messages with a schema registry, Conduktor supports Confluent subject strategies, providing control over how schemas are referenced. This gives users flexibility in schema organization and naming conventions, enabling support for different strategies based on your use case: Topic Name, Record Name, Topic + Record Name or Custom. When you pick a schema registry serializer, select the strategy from the Strategy menu, above the Subject field.

Random data generator

Click Generate once to generate a message that conforms to the selected serializer. This works with schema registry serializers as well:

Headers

This section lets you add headers to your message. Header key and header value both expect valid UTF8 string.

Flow

Using the flow mode, you can produce multiple records in one go or configure a live producer that will produce records at a configurable rate. Send N records at a time Define how many messages should be produced every time you click Produce. Default: 1 Range: [1, 10] Generate Random Key and Generate Random Value Enable these options to generate a different key or value each time you click Produce. When enabled, they override the key or value configured above and will rely on the random data generator to produce messages (same as if you clicked Generate once before producing a record). When disabled, the producer will use the Key/Value configured above Default: disabled Producer mode Manual mode starts a single Kafka produce each time you click the Produce. Automatic mode starts a long running process that batches several Kafka produce. Interval (ms): The interval between each produce batch in milliseconds. Range: [1000, 60000] Stop conditions: The first met condition stops the producer

Records produced
Stops the producer after that many records have been produced.
Range: [0, 1000000]
Elapsed time (ms)
Stops the producer after a set period of time.
Range: [0, 60000]

Here’s an example of a produce flow that will generate a batch of 10 records, every second for a minute. Uses the same key but a random value (based on the Avro schema linked to the topic) for every record:

Additional options

Force partition This option lets you choose the partition where to produce your record. If set to all, it will use the DefaultPartitioner from KafkaClient 3.6+

StickyPartitioner when Key is null or Utils.murmur2(serializedKey) % numPartitions otherwise.

Default: all Compression type This option lets you compress your record(s) using any of the available CompressionType from the KafkaClient Options: Gzip, Lz4, None, Snappy, Zstd Default: none Acks This lets you change the acks property of the producer. Learn more about Kafka producer acks . Default: all Idempotence Enable this option to make the producer idempotent (enable.idempotence=true), so that retries don’t write duplicate records. Enabling it sets Acks to All. Default: disabled (Console sets enable.idempotence=false unless you enable it)

Sensible defaults

The following are pre-configured by default:

If you have connected a schema registry and there’s a subject named <topic-name>-key and <topic-name>-value, the serializers will be populated automatically to the right type (Avro/Protobuf/JsonSchema); otherwise, the StringSerializer will be picked.
A single header app_name=Conduktor will be added

Produced messages panel

Kafka records produced through this screen will be available from the produced message panel, which acts similarly as the consume page, allowing you to review your produced record and check the key, value, headers and metadata.

Click on a record to see its content and metadata:

Operations

Import CSV

This feature lets you produce a batch of Kafka records based on a CSV file. The three required inputs are: the key and value serializer and the input file itself. Supported serializers are JSON, String, and Avro, Json Schema or Protobuf with a schema registry. With a schema registry serializer, also pick the subject naming strategy and the subject name.

CSV import doesn’t support Protobuf yet: Console lists the Protobuf (Schema Registry) serializer, but the import fails with a Not yet supported error.

The file has to have this structure:

named headers key and value must be present. Additional columns will be ignored.
the separator must be ;
double-quoting a field " is optional unless it contains either " or ;, then it’s mandatory
escape double-quotes by doubling them ""

Examples

# Null key (empty)
key;value
;value without key

# Unused headers
topic;partition;key;value
test;0;my-key;my-value

# Mandatory Double quoting
key;value
order-123;"item1;item2"

# Json data
key;value
order-123;"{""item1"": ""value1"", ""item2"":""value2""}"

Click Import to start the process. While in progress, you’ll see a loading state. Once the import is finished, you’ll get a summary message.

Save and load producer templates

If you are regularly using the same set of producer configuration, you can save your current settings as a template for reuse. At the bottom of the produce page, click the save icon and enter the name for this template. Click the folder icon to see the available templates, then click Apply to load one.

Topic partitions

The Partitions tab shows all the partition information associated with the topic. You can switch from the default Per partition view to the Per broker view. The Per partition view show data available for each partition:

Total number of records (estimated using EndOffset - BeginOffset)
Partition size
Begin and end offsets
Broker Ids of the partition leader (green) and followers (grey)

The Per broker view pivots the data to show for each broker:

partitions where the broker is Leader
partitions where the broker is Follower

Empty Partition

In the Per partition view, you can click on the trash icon to remove the Kafka records from this specific partition.

If you need to delete all records from all partitions, click ... next to the Graphs toggle and select Empty topic.

Topic linked resources

If you need to find related resources, your can use one of the following tabs to display all the Kafka resources associated to this specific topic.

Linked consumer groups

The topic consumer groups tab displays the consumer groups associated to the current topic.

Linked schema registry subjects

The Schema tab shows the key subject and value subject associated to your topic, assuming you’ve defined them using TopicNameStrategy .

Linked ACLs

The ACL tab displays the list of Kafka permissions associated with the current topic.

Only the permissions from the default Kafka authorizer implementation AclAuthorizer available using AdminClient are listed. If you’re using one of Console’s other supported ACLs (Aiven, Confluent Cloud), we recommend using the dedicated service account page instead.

Linked role bindings

The Role Bindings tab lists the Confluent Cloud and Confluent Platform role bindings that grant access to the current topic. Each row shows the principal, the role, and the resource pattern that produced the binding.

The tab appears on Confluent Cloud and Confluent Platform clusters. If provider settings are incomplete, Console tells you what to set rather than hiding the tab. Find out how to enable role bindings on Confluent Cloud or Confluent Platform.

Topic graphs

When you browse any topic details page, turn on the Graphs toggle to see the associated graphs for this topic:

produce and consume rate
number of records
disk usage

Graphs can be visualized over one hour, one day, seven days or 30 days.

You can create an alert for each metric. Alerts aren’t available in Console Community Edition.

Alerts tab

The topic Alert tab lets you see all the active alerts associated with this topic. You can edit or toggle them on/off.

Topic consume

The topic consume page lets you read records as they arrive, narrow them down with filters and open any individual record to inspect its key, value, headers and metadata. Watch how to tail a topic in real time and filter records as they arrive:

View data quality policy violations

Available for Gateway clusters with Console v1.42 or later.

When viewing topics on Gateway clusters with data quality Policies and the mark action applied, Console will display the Policy violation information:

A Policy badge appears in the topic header (for example, “Data Quality” or “Encryption”)
The consume table includes a Marking violations column showing Policy and Rule violations for each message
Hover over Policy names to see the Policy ID and violated Rules (deleted Rules appear greyed out)
Click Policy names to navigate to Policy details
Open any message to see full violation details in the Metadata tab’s Violations section, including Policy IDs, Rule IDs, and links to Policy details

Find out about creating and managing data quality Policies

Configure the Kafka consumer

When you access a topic from the topic list page for the first time, a consumer is automatically triggered with default settings:

show from: Most recent
limit: 500 records
partitions: All

This default setup lets you quickly browse through the 500 most recent messages produced in the topic.

Show from

Show From defines the starting point for the Kafka consumer in your topic.

Possible values:

Most recent option works differently depending on the Limit that you select
- with Number of records limit (let’s say 500), it sets the starting point in your topic backward relative to Now, in order to get your the 500 most recent records.
- with None (live consume), it simply set the starting point to Now. This lets you consume only the messages produced after the consumer was started.
Latest hour, Today, Yesterday to start the consumer, respectively:
- 60 minutes ago
- at the beginning of the day at 00:00:00 (local timezone based on your browser)
- at the beginning of the day before at 00:00:00 (local timezone)
Beginning to start the consumer from the very beginning of the topic.
Date and Timestamp to start from a specific point in time datetime or an epoch
- Date: ISO 8601 DateTime format with offset 2024-12-21T00:00:00+00:00
- Timestamp: Unix timestamp in milliseconds 1734949697000
Offset to start the consumer at a specific offset, ideal for use with a single Partition setting.
Consumer group to start the consumer from the last offsets committed by a consumer group on this topic.

Limit

Limit defines when your consumer has to stop.

Available options:

Latest offset - stop the consumer upon reaching the end of the topic. The end offsets are calculated when you trigger the search. Records produced after that point will not appear in the search results.
None (live consume) - start a live consumer that will look for messages indefinitely.
Number of records - stop the consumer after having sent a certain number of records to the browser. When you have active filters, non-matching records will not count toward this limit.
Date - stop the consumer after reaching the configured date. The ISO 8601 DateTime format with offset 2023-12-21T00:00:00+00:00.

Partitions

Partitions lets you restrict the consumer to only consume from certain partitions of your topic. By default, records from all partitions are consumed.

Key and value format

Key format and Value format lets you force the deserializer for your topic. Both offer Automatic, JSON, String, Bytes, Boolean, UUID, None (ignore) and Number (Short, Integer, Long, Float or Double). Value format also offers Schema Registry and Custom deserializers.

Automatic deserializer

This is the default deserializer. Automatic infers the correct deserializer in the following order:

schema registry deserializers (Avro, Protobuf, Json Schema)
JsonDeserializer
StringDeserializer
ByteDeserializer (fallback)

Automatic deserializer applies to all the records within a topic, based on the one that matches the first record it encounters.

Schema IDs in record headers

Confluent Platform 8.2 introduced the option to reference a schema with a globally unique identifier (GUID) in the record headers, instead of prepending a schema ID to the payload. Producers opt in to this by setting value.schema.id.serializer to io.confluent.kafka.serializers.schema.id.HeaderSchemaIdSerializer. Console deserializes these records with the Automatic deserializer and the schema registry deserializers, so consume and filter them as you would any other record.

Console reads records that carry a schema GUID in their headers, but doesn’t yet write them. When you produce or re-process a record, Console prepends the schema ID to the payload. Full GUID support across Console comes in a future release.

Custom deserializer

If you have installed them, your custom deserializers will appear here. Optionally, configure them using the Properties text and your messages will show as expected. Check out the tutorial on installing and configuring custom deserializers in Console.

JSON deserializer

JSON deserializer will explicitly fail on records that doesn’t match a JSON type.

Bytes deserializer

Bytes deserializer helps you visualize your records by printing the non-ASCII characters as hexadecimal escape sequences. For instance, the following sequence of bytes:

00 00 00 00 07 10 49 27 6D 20 41 56 52 4F

corresponding to the wire format of a Schema Registry AVRO message :

                          0   Magic Byte (0)
00 00 07                   1-4 Schema ID (7)
49 27 6D 20 41 56 52 4F    5+  serialized AVRO data

will be represented like this:

\x00\x00\x00\x00\x07\x10I'm AVRO

Filter records

Console provides three methods to define filters that will be executed on the server and will only return the records that match. This is a very powerful feature that allows you to quickly see the records that matter to you, especially in large topics.

Global search

Global search is the most simple type of filter you can use.

Specify whether to look in the Key or in the value.
Pick an operator (contains, does not contain, equals, not equals).
Type your search term.

Global search matches your term against the whole key or value as plain text, and matching is case-sensitive. Contains and does not contain look for your term anywhere in the text, while equals and not equals compare the entire key or value: with a JSON record, equals has to match the whole JSON text, not a field value. To match a field value, search in a specific field. Wildcards (*, ?) and regular expressions aren’t supported: Console matches them as literal characters.

Search in a specific field

You can make your search more fine-grained by activating “Search in a specific field”.

Console will generate an autocomplete list by looking at the most recent 50 messages in the topic. If the key you’re looking for is not here, you can type it manually. Examples:data.event.name
data.event["correlation-id"]
data.clientAddress[0].ip

When you search in a specific field, Console compares the field’s value without JSON quotes, and the operators depend on the field type:

Text fields: equals, not equals, contains, does not contain and starts with, all case-sensitive.
Number fields: equals, not equals, greater and less than, compared as numbers.
Boolean fields: true or false.

If you add several filters, a record has to match all of them. For regular expressions or case-insensitive matching, use a JS filter.

JS search

If you need to construct more advanced filters, you can switch to the advanced view and use plain Javascript to build your filter.

While it is the option that can potentially address the most complex use-cases, it is not the recommended or the fastest one.

Statistics pop-up

While the consumer is processing, you’ll see the following:

Browse records

From the main table

Once the search starts, you’ll see messages appearing in the main table with three columns: timestamp, key and value. Click Display to choose which columns to show (Timestamp, Partition, Offset, Key, Key and Value sizes in bytes, Marking Violations) and how many records to show per page (20, 50 or 100). Click Reset to default to restore the default columns. Console keeps your column choices for each topic name in your browser’s local storage, so topics with the same name on different clusters share them. The number of records per page isn’t saved: it goes back to 20 when you reload the page or open another topic. Console also saves your consumer settings (Show from, Limit, Partitions, Key format, Value format and filters) for each topic and cluster, so they’re applied again when you come back.

The timestamp column uses the local timezone of the user. For example, if you’re producing a message from Dublin, Ireland (UTC+1) at 14:57:38 local time and you then consume this message from your browser (in Dublin), you’ll see 14:57:38.However, if another user consumes the same message in Console but from Paris, France (UTC+2), they’ll see 15:57:38.

Individual records

Click on a record from the list to see the entire record. Use the up/down arrow keys to navigate between messages. The record opens in a panel: the key and value are on the left, and the Metadata and Headers tabs are in a side panel on the right. Use the side panel icon to show or hide it.

Click Reprocess to send the record to the same or another topic, or click Share to open a standalone page for the record that you can share with colleagues. On the standalone page, you can also change the Key format and Value format used to display the record.

Record key and value

If your record value is serialized with JSON or using a schema registry, it’s presented in the table view by default. You can also switch to the JSON view if necessary.

The two views offer different features.

Table view lets you visualize your message field by field and allows you to restrict your search further by applying more filters on individual fields. Filter types are include and exclude and are available for: string, number and boolean fields (disabled for null and fields contained within lists).
JSON view lets you visualize your message. Click JQ Filter to create a different projection of your record value.

The basic syntax lets you focus on sub-elements of your record:

{ foo: .bar }  // Renders {"foo": "value of .bar"}
.meta.domain // Renders a single String
{ id, meta } // Renders a new JSON with both elements

Find out more about JQ syntax reference and object construction for advanced use cases

Record headers

The Headers tab shows all the headers of your Kafka record. To find other records with the same header value, filter on headers with a JS filter.

While Kafka header values are internally stored as byte[], Console decodes them as UTF-8 strings to display and filter them. If your producer doesn’t write header values as UTF-8 strings, this tab might not render properly and header filters might not work as expected.

Record metadata

The Metadata tab provides all other information regarding your record that could be useful:

record partition
record offset
record timestamp and timestamp type
the key and value serializer inferred by the automatic deserializer
key size and value size (how it’s serialized on the broker)
compression type
schema ID, if any

Operations

Export records in CSV and JSON

You can export records in either JSON or CSV format. CSV is particularly useful because you can use Console to re-import the records either in a new topic or in the same topic after modifications (if required). Click on the three dots to see the options:

The resulting files will look like this:

The export includes every record currently held in the consume view, not only the current page. The view keeps at most the 5,000 most recent records, so an export contains at most 5,000 records. To export a specific range, narrow the consume view with Show from and filters before you export.

Reprocess records

This feature lets you pick a record from the list and reprocess it either in the same or in a different topic, while letting you change its content beforehand. You can start from the consumed-message drawer or the standalone page opened with Share. Click Reprocess (1), pick a destination topic in the Target topic list (2), which takes you to the Produce tab (3) with your message pre-filled. From there you can either produce the message directly or make adjustments:

See Reprocess messages for the complete workflow.

Save and load views

If you’re regularly using the same set of consume configuration (e.g. show from, limit) and the filters or if you’d like to share the views with others, you can save your current view as a template. To create a view, click Save. This will save your current view as a template:

Give your view a name add a description and select whether it’s private or if you want everyone in your organization to be able to see it and use it.

Once a view is saved, the Save menu lets you save your changes to this view or to a new view. Use the … menu to edit the view details or delete it. To see all views available to you, click the folder:

Most recent 500 messages

When you first land on a topic consume page, the default search shows from Most recent, with a limit of 500 records. The intention is to show you the most relevant messages, split across the partitions. This algorithm guarantees to return some messages irrespective of when the records were produced, which we believe is a good starting point when browsing a topic for the first time. In most cases, it will give you 500 / num_partitions messages, per partition. If your topic has:
10 partitions, Most Recent 500 will give you 50 messages per partition.
2 partitions, Most Recent 500 will give you 250 messages per partition. Edge cases might occur and the algorithm will account for it seamlessly.

Most recent N messages doesn’t work well with filters. This is because the filters will only be applied to those 500 messages instead of a large number of records. We recommend switching to a time-based ShowFrom when using filters

JS filter syntax

JS filter is used to filter Kafka records and is evaluated on each record on the server. It’s powerful and can handle complex filters but requires writing JavaScript code.

We recommend that you use the simpler and more performant filters: the global search in a specific field.

The code has to return a boolean. If your code returns true, the record will be included in the results, otherwise it will be skipped. return value.totalPrice >= 30; // Selects all the orders having a total price superior to or equal to 30

Record attributes

When creating JavaScript filters, you may want to access message data or the metadata. See the parameters in the table below for accessing different message attributes.

Attribute	Types
key	Object
value	Object
headers	Object
serializedKeySize	Number
serializedValueSize	Number
keySchemaId	Number
valueSchemaId	Number
offset	Number
partition	Number

Example filters

In this example, we have these two records in our topic: Record 1:

{
  "key": "order",
  "value": {
    "orderId": 12345,
    "paid": true,
    "totalPrice": 50,
    "items": [
      {
        "id": "9cb5cb81-b678-4f96-84dc-70096038eca9",
        "name": "beers pack"
      },
      {
        "id": "507b5045-eafd-41a6-afb5-1890f08cfd8e",
        "name": "baby diapers pack"
      }
    ]
  },
  "headers": {
    "app": "orders-microservice",
    "trace-id": "9f0f004a-70c5-4301-9d28-bf5d7ebf238d"
  }
}

Record 2:

{
  "key": "order",
  "value": {
    "orderId": 12346,
    "paid": false,
    "totalPrice": 10,
    "items": [
      {
        "id": "7f55662e-5ba2-4ab4-9546-45fdd1ca60ca",
        "name": "shampoo bottle"
      }
    ]
  },
  "headers": {
    "app": "orders-microservice",
    "trace-id": "1076c6dc-bd6c-4d5e-8a11-933b10bd77f5"
  }
}

Here are some sample filters related to these records:

return value.totalPrice >= 30;
// Selects all the orders having a total price superior to or equal to 30

return value.items.length > 1;
// Selects all the orders containing more than one 1 item

return value.orderId == 12345;
// Finds a specific order based in its ID

return !value.paid;
// Selects all the orders that aren't paid

return !headers.includesKey("trace-id");
// Selects all the records not having a trace-id header

const isHighPrice = value.totalPrice >= 30
const moreThanOneItem = value.items.length > 1
return isHighPrice && moreThanOneItem
// Selects all the orders having a total price superior to or equal to 30 and having more than one 1 item

return ["orders-microservice", "billing-microservice"].includes(headers.app);
// Selects all the records whose app header matches one of several values

Filter on headers

The headers attribute is an object that maps each header key to its value:

A header that appears once maps to a string.
A header key that appears several times on the same record maps to an array of strings.
A header with no value maps to null.

Use bracket notation for header keys that contain dashes or dots:

return headers["trace-id"] === "9f0f004a-70c5-4301-9d28-bf5d7ebf238d";
// Selects the record with this exact trace-id header value

return headers.includesKey("trace-id");
// Selects all the records having a trace-id header

Filter behavior and limits

Console evaluates the filter on each record separately. Don’t rely on state carried from one record to another: a JS filter can’t detect duplicate keys across records, for example.
Records the filter can’t evaluate are skipped. If the filter takes more than one second on a record, or throws an error, Console leaves the record out of the results and shows a Some records were skipped warning with the first error.
The code has to include a return statement. Console runs it in JavaScript strict mode, without console or eval.

The configuration tab

The topic Configuration tab lets you visualize and edit your topic configuration.

On top of the table you have different fields to help you:

Search: filters out the configurations by name. In the example above, we display only configuration with the term retention.
Raw/Friendly: formats the information either in its original form or in a more human readable way. For instance, retention.ms can be represented as raw (43200000) or friendly(12h). Default is Friendly.
Show overrides only: displays only the configurations that are set at topic level, as opposed to the broker level. Default is true.
Raw view: shows all the topic configurations as key value pairs.
...: lets you do the same three operations that are available on the topic list page: add partitions, empty topic and delete topic.

To edit a configuration, click the pen button. The Update topic configuration panel shows the name and description of the topic configuration, the Kafka default value and the current value:

You can either update the value or, for a value set at topic level, click Reset to default to revert to the Kafka default, which will apply the change directly on the Kafka cluster, similar to the kafka-configs commands below.

# Update value
$ kafka-configs --bootstrap-server broker_host:port \
  --entity-type topics --entity-name my_topic_name  \
  --alter --add-config cleanup.policy=<new-value>

# Reset to default
$ kafka-configs --bootstrap-server broker_host:port \
  --entity-type topics --entity-name my_topic_name  \
  --alter --delete-config cleanup.policy

​Topic list

​Operations

​Create topic

​Add partitions

​Empty topic

​Delete topic

​Manage topic labels

​Topic produce overview

​Configure producer

​Key and value

​Confluent subject strategies

​Random data generator

​Headers

​Flow

​Additional options

​Sensible defaults

​Produced messages panel

​Operations

​Import CSV

​Save and load producer templates

​Topic partitions

​Empty Partition

​Topic linked resources

​Linked consumer groups

​Linked schema registry subjects

​Linked ACLs

​Linked role bindings

​Topic graphs

​Alerts tab

​Topic consume

​View data quality policy violations

​Configure the Kafka consumer

​Show from

​Limit

​Partitions

​Key and value format

​Automatic deserializer

​Schema IDs in record headers

​Custom deserializer

​JSON deserializer

​Bytes deserializer

​Filter records

​Global search

​Search in a specific field

​JS search

​Statistics pop-up

​Browse records

​From the main table

​Individual records

​Record key and value

​Record headers

​Record metadata

​Operations

​Export records in CSV and JSON

​Reprocess records

​Save and load views

​Most recent 500 messages

​JS filter syntax

​Record attributes

​Example filters

​Filter on headers

​Filter behavior and limits

​The configuration tab

​Related resources

Topic list

Operations

Create topic

Add partitions

Empty topic

Delete topic

Manage topic labels

Topic produce overview

Configure producer

Key and value

Confluent subject strategies

Random data generator

Headers

Flow

Additional options

Sensible defaults

Produced messages panel

Operations

Import CSV

Save and load producer templates

Topic partitions

Empty Partition

Topic linked resources

Linked consumer groups

Linked schema registry subjects

Linked ACLs

Linked role bindings

Topic graphs

Alerts tab

Topic consume

View data quality policy violations

Configure the Kafka consumer

Show from

Limit

Partitions

Key and value format

Automatic deserializer

Schema IDs in record headers

Custom deserializer

JSON deserializer

Bytes deserializer

Filter records

Global search

Search in a specific field

JS search

Statistics pop-up

Browse records

From the main table

Individual records

Record key and value

Record headers

Record metadata

Operations

Export records in CSV and JSON

Reprocess records

Save and load views

Most recent 500 messages

JS filter syntax

Record attributes

Example filters

Filter on headers

Filter behavior and limits

The configuration tab

Related resources