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Copy file name to clipboardexpand all lines: modules/get-started/pages/whats-new.adoc
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You now can configure Kafka clients to authenticate using xref:manage:security/authentication#enable-sasl.adoc[SASL/PLAIN] with a single account using the same username and password. Unlike SASL/SCRAM, which uses a challenge response with hashed credentials, SASL/PLAIN transmits plaintext passwords. You enable SASL/PLAIN by appending `PLAIN` to the list of SASL mechanisms.
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== New metrics
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The following metrics are new in this version:
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=== Consumer lag gauges
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Redpanda can now expose dedicated consumer lag gauges that eliminate the need to calculate lag manually. These metrics provide real-time insights into consumer group performance and help identify issues. The following metrics are available:
Reports the maximum lag observed among all partitions for a consumer group. This metric helps pinpoint the partition with the greatest delay, indicating potential performance or configuration issues.
Aggregates the lag across all partitions, providing an overall view of data consumption delay for the consumer group.
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See xref:manage:monitoring.adoc#consumers[Monitor consumer group lag] for more information.
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== New cluster properties
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The following cluster properties are new in this version:
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- xref:reference:properties/cluster-properties.adoc#raft_max_buffered_follower_append_entries_bytes_per_shard[`raft_max_buffered_follower_append_entries_bytes_per_shard`]: Limits the maximum bytes buffered for follower append entries per shard.
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- xref:reference:properties/cluster-properties.adoc#raft_max_inflight_follower_append_entries_requests_per_shard[`raft_max_inflight_follower_append_entries_requests_per_shard`]: Replaces the deprecated `raft_max_concurrent_append_requests_per_follower` to limit in-flight follower append requests per shard.
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=== Tiered Storage
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- xref:reference:properties/object-storage-properties.adoc#cloud_storage_enable_remote_allow_gaps[`cloud_storage_enable_remote_allow_gaps`]: Controls the eviction of locally stored log segments when Tiered Storage uploads are paused.
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- xref:reference:properties/object-storage-properties.adoc#cloud_storage_enable_segment_uploads[`cloud_storage_enable_segment_uploads`]: Controls the upload of log segments to Tiered Storage. If set to `false`, this property temporarily pauses all log segment uploads from the Redpanda cluster.
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=== TLS configuration
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- xref:reference:properties/cluster-properties.adoc#tls_enable_renegotiation[`tls_enable_renegotiation`]: Enables support for TLS renegotiation.
Copy file name to clipboardexpand all lines: modules/manage/partials/monitor-health.adoc
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Redpanda's Raft implementation exchanges periodic status RPCs between a broker and its peers. The xref:reference:public-metrics-reference.adoc#redpanda_node_status_rpcs_timed_out[`redpanda_node_status_rpcs_timed_out`] gauge increases when a status RPC times out for a peer, which indicates that a peer may be unresponsive and may lead to problems with partition replication that Raft manages. Monitor for non-zero values of this gauge, and correlate it with any logged errors or changes in partition replication.
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=== Consumers
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[[consumers]]
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=== Consumer group lag
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==== Consumer group lag
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Consumer group lag is an important performance indicator that measures the difference between the broker's latest (max) offset and the consumer group's last committed offset. The lag indicates how current the consumed data is relative to real-time production. A high or increasing lag means that consumers are processing messages slower than producers are generating them. A decreasing or stable lag implies that consumers are keeping pace with producers, ensuring real-time or near-real-time data consumption.
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When working with Kafka consumer groups, the consumer group lag—the difference between the broker's latest (max) offset and the group's last committed offset—is a performance indicator of how fresh the data being consumed is. While higher lag for archival consumers is expected, high lag for real-time consumers could indicate that the consumers are overloaded and thus may need their topics to be partitioned more, or to spread the load to more consumers.
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By monitoring consumer lag, you can identify performance bottlenecks and make informed decisions about scaling consumers, tuning configurations, and improving processing efficiency.
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To monitor consumer group lag, create a query with the xref:reference:public-metrics-reference.adoc#redpanda_kafka_max_offset[`redpanda_kafka_max_offset`] and xref:reference:public-metrics-reference.adoc#redpanda_kafka_consumer_group_committed_offset[`redpanda_kafka_consumer_group_committed_offset`] gauges:
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A high maximum lag may indicate that a consumer is experiencing connectivity problems or cannot keep up with the incoming workload.
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A high or increasing total lag (lag sum) suggests that the consumer group lacks sufficient resources to process messages at the rate they are produced. In such cases, scaling the number of consumers within the group can help, but only up to the number of partitions available in the topic. If lag persists despite increasing consumers, repartitioning the topic may be necessary to distribute the workload more effectively and improve processing efficiency.
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Redpanda provides the following methods for monitoring consumer group lag:
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- <<dedicated-gauges, Dedicated gauges>>: Redpanda brokers can internally calculate consumer group lag and expose two dedicated gauges. This method is recommended for environments where your observability platform does not support complex queries required to calculate the lag from offset metrics.
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Enabling these gauges may add a small amount of additional processing overhead to the brokers.
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- <<offset-based-calculation, Offset-based calculation>>: You can use your observability platform to calculate consumer group lag from offset metrics. Use this method if your observability platform supports functions, such as `max()`, and you prefer to avoid additional processing overhead on the broker.
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==== Dedicated gauges
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Redpanda can internally calculate consumer group lag and expose it as two dedicated gauges.
Reports the maximum lag observed among all partitions for a consumer group. This metric helps pinpoint the partition with the greatest delay, indicating potential performance or configuration issues.
Aggregates the lag across all partitions, providing an overall view of data consumption delay for the consumer group.
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To enable these dedicated gauges, you must enable consumer group metrics in your cluster properties. Add the following settings to your Redpanda configuration:
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- xref:reference:properties/cluster-properties.adoc#enable_consumer_group_metrics[`enable_consumer_group_metrics`]: A list of properties to enable for consumer group metrics. You must add the `consumer_lag` property to enable consumer group lag metrics.
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- xref:reference:properties/cluster-properties.adoc#consumer_group_lag_collection_interval_sec[`consumer_group_lag_collection_interval_sec`] (optional): The interval in seconds for collecting consumer group lag metrics. The default is 60 seconds.
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Set this value equal to the scrape interval of your metrics collection system. Aligning these intervals ensures synchronized data collection, reducing the likelihood of missing or misaligned lag measurements.
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For example:
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ifndef::env-kubernetes[]
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[,bash]
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----
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rpk cluster config set enable_consumer_group_metrics '["group", "partition", "consumer_lag"]'
When these properties are enabled, Redpanda computes and exposes the `redpanda_kafka_consumer_group_lag_max` and `redpanda_kafka_consumer_group_lag_sum` gauges to the `/public_metrics` endpoint.
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==== Offset-based calculation
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If your environment is sensitive to the performance overhead of the <<dedicated-gauges, dedicated gauges>>, use the offset-based calculation method to calculate consumer group lag. This method requires your observability platform to support functions like `max()`.
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Redpanda provides two metrics to calculate consumer group lag:
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- xref:reference:public-metrics-reference.adoc#redpanda_kafka_max_offset[`redpanda_kafka_max_offset`]: The broker's latest offset for a partition.
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- xref:reference:public-metrics-reference.adoc#redpanda_kafka_consumer_group_committed_offset[`redpanda_kafka_consumer_group_committed_offset`]: The last committed offset for a consumer group on that partition.
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For example, here's a typical query to compute consumer lag:
Copy file name to clipboardexpand all lines: modules/reference/pages/properties/broker-properties.adoc
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*Visibility:* `user`
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*Default:* `null`
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*Default:* `{}`
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*Visibility:* `user`
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*Default:* `null`
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*Default:* `{}`
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=== node_id_overrides
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List of broker IDs and UUID to override at broker startup. Each entry includes the current UUID and desired ID and UUID. Each entry applies to a given broker only if 'current' matches that broker's current UUID.
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*Visibility:* `user`
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*Type:* array
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*Default:* `{}`
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---
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=== openssl_config_file
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Path to the configuration file used by OpenSSL to properly load the FIPS-compliant module.
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*Type:* array
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*Default:* `null`
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*Default:* `{}`
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=== api_doc_dir
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API doc directory.
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Path to the API specifications for the HTTP Proxy API.
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*Requires restart:* Yes
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*Visibility:* `user`
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*Visibility:* `user`
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*Default:* `null`
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*Default:* `{}`
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Copy file name to clipboardexpand all lines: modules/reference/pages/properties/cluster-properties.adoc
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=== consumer_group_lag_collection_interval_sec
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How often to run the collection loop when <<enable_consumer_group_metrics,`enable_consumer_group_metrics`>> contains `consumer_lag`.
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Reducing the value of `consumer_group_lag_collection_interval_sec` increases the metric collection frequency, which may raise resource utilization. In most environments, this impact is minimal, but it's best practice to monitor broker resource usage in high-scale settings.
The maximum number of translations that the datalake scheduler will allow to run at a given time. If a translation is requested, but the number of running translations exceeds this value, the request will be put to sleep temporarily, polling until capacity becomes available.
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*Requires restart:* Yes
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*Visibility:* `tunable`
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*Type:* integer
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*Default:* `4`
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---
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=== datalake_scheduler_time_slice_ms
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Time, in milliseconds, for a datalake translation as scheduled by the datalake scheduler. After a translation is scheduled, it will run until either the time specified has elapsed or all pending records on its source partition have been translated.
List of enabled consumer group metrics. Accepted Values: `group`, `partition`, `consumer_lag`.
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List of enabled consumer group metrics. Accepted values include:
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- `group`: Enables the xref:reference:public-metrics-reference.adoc#redpanda_kafka_consumer_group_consumers[`redpanda_kafka_consumer_group_consumers`] and xref:reference:public-metrics-reference.adoc#redpanda_kafka_consumer_group_topics[`redpanda_kafka_consumer_group_topics`] metrics.
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- `partition`: Enables the xref:reference:public-metrics-reference.adoc#redpanda_kafka_consumer_group_committed_offset[`redpanda_kafka_consumer_group_committed_offset`] metric.
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- `consumer_lag`: Enables the xref:reference:public-metrics-reference.adoc#redpanda_kafka_consumer_group_lag_max[`redpanda_kafka_consumer_group_lag_max`] and xref:reference:public-metrics-reference.adoc#redpanda_kafka_consumer_group_lag_sum[`redpanda_kafka_consumer_group_lag_sum`] metrics
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Enabling `consumer_lag` may add a small amount of additional processing overhead to the brokers, especially in environments with a high number of consumer groups or partitions.
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Use the xref:reference:properties/cluster-properties.adoc#consumer_group_lag_collection_interval_sec[`consumer_group_lag_collection_interval_sec`] property to control the frequency of consumer lag metric collection.
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*Requires restart:* No
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*Default:* `["group", "partition"]`
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*Related topics*:
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- xref:manage:monitoring.adoc#consumers[Monitor consumer group lag]
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=== enable_controller_log_rate_limiting
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=== iceberg_disable_automatic_snapshot_expiry
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Whether to disable automatic Iceberg snapshot expiry. This property may be useful if the Iceberg catalog expects to perform snapshot expiry on its own.
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*Requires restart:* No
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*Visibility:* `user`
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*Type:* boolean
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*Default:* `false`
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=== iceberg_disable_snapshot_tagging
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Whether to disable tagging of Iceberg snapshots. These tags are used to ensure that the snapshots that Redpanda writes are retained during snapshot removal, which in turn, helps Redpanda ensure exactly-once delivery of records. Disabling tags is therefore not recommended, but may be useful if the Iceberg catalog does not support tags.
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