KIP-405: Kafka Tiered Storage

Authors Sriharsha Chintalapani, Satish Duggana, Suresh Srinivas, Ying Zheng (alphabetical order by the last names)

Status

Current State: Discussion

Discussion Thread: Discuss Thread

JIRA:  Unable to render Jira issues macro, execution error.

Google doc version of this wiki is located here.

Motivation

Kafka is an important part of data infrastructure and is seeing significant adoption and growth. As the Kafka cluster size grows and more data is stored in Kafka for a longer duration, several issues related to scalability, efficiency, and operations become important to address.

Kafka stores the messages in append-only log segments on local disks on Kafka brokers. The retention period for the log is based on `log.retention` that can be set system-wide or per topic. Retention gives the guarantee to consumers that even if their application failed or was down for maintenance, it can come back within the retention period to read from where it left off without losing any data.

The total storage required on a cluster is proportional to the number of topics/partitions, the rate of messages, and most importantly the retention period. A Kafka broker typically has a large number of disks with the total storage capacity of 10s of TBs. The amount of data locally stored on a Kafka broker presents many operational challenges.

Kafka as a long-term storage service

Kafka has grown in adoption to become the entry point of all of the data. It allows users to not only consume data in real-time but also gives the flexibility to fetch older data based on retention policies. Given the simplicity of Kafka protocol and wide adoption of consumer API, allowing users to store and fetch data with longer retention help make Kafka one true source of data.

Currently, Kafka is configured with a shorter retention period in days (typically 3 days) and data older than the retention period is copied using data pipelines to a more scalable external storage for long-term use, such as HDFS. This results in data consumers having to build different versions of applications to consume the data from different systems depending on the age of the data.

Kafka cluster storage is typically scaled by adding more broker nodes to the cluster. But this also adds needless memory and CPUs to the cluster making overall storage cost less efficient compared to storing the older data in external storage. Larger cluster with more nodes also adds to the complexity of deployment and increases the operational costs.

Kafka local storage and operational complexity

When a broker fails, the failed node is replaced by a new node. The new node must copy all the data that was on the failed broker from other replicas. Similarly, when a new Kafka node is added to scale the cluster storage, cluster rebalancing assigns partitions to the new node which also requires copying a lot of data. The time for recovery and rebalancing is proportional to the amount of data stored locally on a Kafka broker. In setups that have many Kafka clusters running 100s of brokers, a node failure is a common occurrence, with a lot of time spent in recovery making operations difficult and time-consuming.

Reducing the amount of data stored on each broker can reduce the recovery/rebalancing time. But it would also necessitate reducing the log retention period impacting the time available for application maintenance and failure recovery.

Kafka in cloud

On-premise Kafka deployments use hardware SKUs with multiple high capacity disks to maximize the i/o throughput and to store the data for the retention period. Equivalent SKUs with similar local storage options are either unavailable or they are very expensive in the cloud. There are more available options for SKUs with lesser local storage capacity as Kafka broker nodes and they are more suitable in the cloud.

Solution - Tiered storage for Kafka

Kafka data is mostly consumed in a streaming fashion using tail reads. Tail reads leverage OS's page cache to serve the data instead of disk reads. Older data is typically read from the disk for backfill or failure recovery purposes and is infrequent.

In the tiered storage approach, Kafka cluster is configured with two tiers of storage - local and remote. Local tier is the same as the current Kafka that uses the local disks on the Kafka brokers to store the log segments. The new remote tier uses systems, such as HDFS or S3 to store the completed log segments. Two separate retention periods are defined corresponding to each of the tiers. With remote tier enabled, the retention period for the local tier can be significantly reduced from days to few hours. The retention period for remote tier can be much longer, days or even months. When a log segment is rolled on the local tier, it is copied to the remote tier along with the corresponding offset index. Latency sensitive applications perform tail reads and are served from local tier leveraging the existing Kafka mechanism of efficiently using page cache to serve the data. Backfill and other applications recovering from a failure that needs data older than what is in the local tier are served from the remote tier.

This solution allows scaling storage independent of memory and CPUs in a Kafka cluster enabling Kafka to be a long-term storage solution. This also reduces the amount of data stored locally on Kafka brokers and hence the amount of data that needs to be copied during recovery and rebalancing. Log segments that are available in the remote tier need not be restored on the broker or restored lazily and are served from the remote tier. With this, increasing the retention period no longer requires scaling the Kafka cluster storage and the addition of new nodes. At the same time, the overall data retention can still be much longer eliminating the need for separate data pipelines to copy the data from Kafka to external stores, as done currently in many deployments.

Goals

Extend Kafka's storage beyond the local storage available on the Kafka cluster by retaining the older data in an external store, such as HDFS or S3 with minimal impact on the internals of Kafka. Kafka behavior and operational complexity must not change for existing users that do not have tiered storage feature configured.

Non-Goals

Tiered storage does not replace ETL pipelines and jobs. Existing ETL pipelines continue to consume data from Kafka as is, albeit with data in Kafka having a much longer retention period.

It does not support compact topics.   

Public Interfaces

Compacted topics will not have remote storage support. 

Configs

Remote Storage Manager

         `RemoteStorageManager` is an interface to provide the lifecycle of remote log segments and indexes. More details about how we arrived at this interface are discussed in the document. We will provide a simple implementation of RSM to get a better understanding of the APIs. HDFS and S3 implementation are planned to be hosted in external repos and these will not be part of Apache Kafka repo. This is inline with the approach taken for Kafka connectors.

/**
 * RemoteStorageManager provides the lifecycle of remote log segments which includes copy, fetch, and delete operations.
 *
 * {@link RemoteLogMetadataManager} is responsible for storing and fetching metadata about the remote log segments in a
 * strongly consistent manner.
 *
 * Each upload or copy of a segment is given with a {@link RemoteLogSegmentId} which is universally unique even for the
 * same topic partition and offsets. Once the copy or upload is successful, {@link RemoteLogSegmentMetadata} is
 * created with RemoteLogSegmentId and other log segment information and it is stored in {@link RemoteLogMetadataManager}.
 * This allows RemoteStorageManager to store segments even in eventually consistent manner as the metadata is already
 * stored in a consistent store.
 *
 * All these APIs are still experimental.
 */
@InterfaceStability.Unstable
public interface RemoteStorageManager extends Configurable, Closeable {

    /**
     * Copies LogSegmentData provided for the given RemoteLogSegmentId and returns any contextual
     * information about this copy operation. This can include path to the object in the store etc.
     *
     * Invoker of this API should always send a unique id as part of {@link RemoteLogSegmentId#id()} even when it
     * retries to invoke this method for the same log segment data.
     *
     * @param remoteLogSegmentId
     * @param logSegmentData
     * @return
     * @throws IOException
     */
    RemoteLogSegmentContext copyLogSegment(RemoteLogSegmentId remoteLogSegmentId, LogSegmentData logSegmentData)
            throws RemoteStorageException;

    /**
     * Returns the remote log segment data file/object as InputStream for the given RemoteLogSegmentMetadata starting
     * from the given startPosition. The stream will end at the smaller of endPosition and the end of the remote log
     * segment data file/object.
     *
     * @param remoteLogSegmentMetadata
     * @param startPosition
     * @param endPosition
     * @return
     * @throws IOException
     */
    InputStream fetchLogSegmentData(RemoteLogSegmentMetadata remoteLogSegmentMetadata,
                                    Long startPosition, Long endPosition) throws RemoteStorageException;

    /**
     * Returns the offset index for the respective log segment of {@link RemoteLogSegmentMetadata}.
     *
     * @param remoteLogSegmentMetadata
     * @return
     * @throws IOException
     */
    InputStream fetchOffsetIndex(RemoteLogSegmentMetadata remoteLogSegmentMetadata) throws RemoteStorageException;

    /**
     * Returns the timestamp index for the respective log segment of {@link RemoteLogSegmentMetadata}.
     *
     * @param remoteLogSegmentMetadata
     * @return
     * @throws IOException
     */
    InputStream fetchTimestampIndex(RemoteLogSegmentMetadata remoteLogSegmentMetadata) throws RemoteStorageException;

    /**
     *
     * @param remoteLogSegmentMetadata
     * @return
     * @throws RemoteStorageException
     */
    default InputStream fetchTransactionIndex(RemoteLogSegmentMetadata remoteLogSegmentMetadata) throws RemoteStorageException {
        throw new UnsupportedOperationException();
    }

    /**
     *
     * @param remoteLogSegmentMetadata
     * @return
     * @throws RemoteStorageException
     */
    default InputStream fetchProducerSnapshotIndex(RemoteLogSegmentMetadata remoteLogSegmentMetadata) throws RemoteStorageException {
        throw new UnsupportedOperationException();
    }

    /**
     * Deletes the remote log segment for the given remoteLogSegmentMetadata. Deletion is considered as successful if
     * this call returns successfully without any exceptions. It will throw {@link RemoteStorageException} if there are
     * any errors in deleting the file.
     *
     * Broker pushes an event to __delete_failed_remote_log_segments topic for failed segment deletions so that users
     * can do the cleanup later.
     *
     * @param remoteLogSegmentMetadata
     * @throws IOException
     */
    void deleteLogSegment(RemoteLogSegmentMetadata remoteLogSegmentMetadata) throws RemoteStorageException;

}



/**
 * This represents a universally unique id associated to a topic partition's log segment. This will be regenerated for
 * every attempt of copying a specific log segment in {@link RemoteLogStorageManager#copyLogSegment(RemoteLogSegmentId, LogSegmentData)}.
 */
public class RemoteLogSegmentId {
    private TopicPartition topicPartition;
    private UUID id;

    public RemoteLogSegmentId(TopicPartition topicPartition, UUID id) {
        this.topicPartition = requireNonNull(topicPartition);
        this.id = requireNonNull(id);
    }

    public TopicPartition topicPartition() {
        return topicPartition;
    }

    public UUID id() {
        return id;
    }
...
}



public class LogSegmentData {

    private final File logSegment;
    private final File offsetIndex;
    private final File timeIndex;
    private final File txnIndex;
    private final File producerIdSnapshotIndex;
    private final File leaderEpochIndex;

    public LogSegmentData(File logSegment, File offsetIndex, File timeIndex, File txnIndex, File producerIdSnapshotIndex,
                          File leaderEpochIndex) {
        this.logSegment = logSegment;
        this.offsetIndex = offsetIndex;
        this.timeIndex = timeIndex;
        this.txnIndex = txnIndex;
        this.producerIdSnapshotIndex = producerIdSnapshotIndex;
        this.leaderEpochIndex = leaderEpochIndex;
    }
...
}

RemoteLogMetadataManager

`RemoteLogMetadataManager` is an interface to provide the lifecycle of metadata about remote log segments with strongly consistent semantics. There is a default implementation that uses an internal topic. Users can plugin their own implementation if they intend to use another system to store remote log segment metadata.

/**
 * This interface provides storing and fetching remote log segment metadata with strongly consistent semantics.
 *
 * When {@link #configure(Map)} is invoked on this instance, {@link #BROKER_ID}, {@link #CLUSTER_ID} properties are
 * passed which can be used by this instance if needed. These props can be used if there is a single storage used for
 * different clusters. For ex: MySQL storage can be used as metadata store for all the clusters across the org.
 *
 * todo-tier cleanup the abstractions in this interface.
 */
@InterfaceStability.Unstable
public interface RemoteLogMetadataManager extends Configurable, Closeable {

    /**
     *
     */
    String BROKER_ID = "broker.id";

    /**
     *
     */
    String CLUSTER_ID = "cluster.id";

    /**
     * Stores RemoteLogSegmentMetadata with the given RemoteLogSegmentId.
     *
     * @param remoteLogSegmentMetadata
     * @throws IOException
     */
    void putRemoteLogSegmentData(RemoteLogSegmentMetadata remoteLogSegmentMetadata) throws IOException;

    /**
     * Fetches RemoteLogSegmentId for the given topic partition which contains the given offset.
     *
     * @param topicPartition
     * @param offset
     * @return
     * @throws IOException
     */
    RemoteLogSegmentId getRemoteLogSegmentId(TopicPartition topicPartition, long offset) throws IOException;

    /**
     * Fetches RemoteLogSegmentMetadata for the given RemoteLogSegmentId.
     *
     * @param remoteLogSegmentId
     * @return
     * @throws IOException
     */
    RemoteLogSegmentMetadata getRemoteLogSegmentMetadata(RemoteLogSegmentId remoteLogSegmentId) throws IOException;

    /**
     * Earliest log offset if exists for the given topic partition in the remote storage. Return {@link Optional#empty()}
     * if there are no segments in the remote storage.
     *
     * @param tp
     * @return
     */
    Optional<Long> earliestLogOffset(TopicPartition tp) throws IOException;

    /**
     *
     * @param tp
     * @return
     * @throws IOException
     */
    Optional<Long> highestLogOffset(TopicPartition tp) throws IOException;

    /**
     * Deletes the log segment metadata for the given remoteLogSegmentId.
     *
     * @param remoteLogSegmentId
     * @throws IOException
     */
    void deleteRemoteLogSegmentMetadata(RemoteLogSegmentId remoteLogSegmentId) throws IOException;

    /**
     * List the remote log segment files of the given topicPartition.
     * The RemoteLogManager of a follower uses this method to find out the remote data for the given topic partition.
     *
     * @return List of remote segments, sorted by baseOffset in ascending order.
     */
    default List<RemoteLogSegmentMetadata> listRemoteLogSegments(TopicPartition topicPartition) {
        return listRemoteLogSegments(topicPartition, 0);
    }

    /**
     * @param topicPartition
     * @param minOffset
     * @return List of remote segments, sorted by baseOffset in ascending order.
     */
    List<RemoteLogSegmentMetadata> listRemoteLogSegments(TopicPartition topicPartition, long minOffset);

    /**
     * This method is invoked only when there are changes in leadership of the topic partitions that this broker is
     * responsible for.
     *
     * @param leaderPartitions   partitions that have become leaders on this broker.
     * @param followerPartitions partitions that have become followers on this broker.
     */
    void onPartitionLeadershipChanges(Set<TopicPartition> leaderPartitions, Set<TopicPartition> followerPartitions);

    /**
     * This method is invoked only when the given topic partitions are stopped on this broker. This can happen when a
     * partition is emigrated to other broker or a partition is deleted.
     *
     * @param partitions
     */
    void onStopPartitions(Set<TopicPartition> partitions);

    /**
     * Callback to receive once server is started so that this class can run tasks which should be run only when the
     * server is started.
     */
    void onServerStarted(final String serverEndpoint);
}



/**
 * Metadata about the log segment stored in remote tier storage.
 */
public class RemoteLogSegmentMetadata implements Serializable {

    private static final long serialVersionUID = 1L;

    /**
     * Universally unique remote log segment id.
     */
    private final RemoteLogSegmentId remoteLogSegmentId;

    /**
     * Start offset of this segment.
     */
    private final long startOffset;

    /**
     * End offset of this segment.
     */
    private final long endOffset;

    /**
     * Leader epoch of the broker.
     */
    private final int leaderEpoch;

    /**
     * Maximum timestamp in the segment
     */
    private final long maxTimestamp;

    /**
     * Epoch time at which the remote log segment is copied to the remote tier storage.
     */
    private long createdTimestamp;

    /**
     * Size of the segment in bytes.
     */
    private long segmentSizeInBytes;

    /**
     * It indicates that this is marked for deletion.
     */
    private boolean markedForDeletion;

    /**
     * Any context returned by {@link RemoteStorageManager#copyLogSegment(RemoteLogSegmentId, LogSegmentData)} for
     * the given remoteLogSegmentId
     */
    private final byte[] remoteLogSegmentContext;


    /**
     * @param remoteLogSegmentId      Universally unique remote log segment id.
     * @param startOffset             Start offset of this segment.
     * @param endOffset               End offset of this segment.
     * @param maxTimestamp            maximum timestamp in this segment
     * @param leaderEpoch             Leader epoch of the broker.
     * @param createdTimestamp        Epoch time at which the remote log segment is copied to the remote tier storage.
     * @param markedForDeletion       The respective segment of remoteLogSegmentId is marked fro deletion.
     * @param remoteLogSegmentContext Any context returned by {@link RemoteStorageManager#copyLogSegment(RemoteLogSegmentId, LogSegmentData)}
     * @param segmentSizeInBytes      size of this segment in bytes.
     */
    public RemoteLogSegmentMetadata(RemoteLogSegmentId remoteLogSegmentId, long startOffset, long endOffset,
                                    long maxTimestamp, int leaderEpoch, long createdTimestamp,
                                    boolean markedForDeletion, byte[] remoteLogSegmentContext, long segmentSizeInBytes) {
        this.remoteLogSegmentId = remoteLogSegmentId;
        this.startOffset = startOffset;
        this.endOffset = endOffset;
        this.leaderEpoch = leaderEpoch;
        this.maxTimestamp = maxTimestamp;
        this.createdTimestamp = createdTimestamp;
        this.markedForDeletion = markedForDeletion;
        this.remoteLogSegmentContext = remoteLogSegmentContext;
        this.segmentSizeInBytes = segmentSizeInBytes;
    }

...
}

Proposed Changes

High-level design

The earlier approach consists of pulling the remote log segment metadata from remote log storage APIs as mentioned in the earlier RemoteStorageManager_Old section. This approach worked fine for storages like HDFS. One of the problems of relying on the remote storage to maintain metadata is that tiered-storage needs to be strongly consistent, with an impact not only on the metadata itself (e.g. LIST in S3) but also on the segment data (e.g. GET after a DELETE in S3). Also, the cost (and to a lesser extent performance) of maintaining metadata in remote storage needs to be factored in. In case of S3, frequent LIST APIs incur huge costs. 

So, remote storage is separated from the remote log metadata store and introduced RemoteStorageManager and RemoteLogMetadataManager respectively. You can see the discussion details in the doc located here.

RemoteLogManager (RLM) is a new component which

• receives callback events for leadership changes and stop/delete events of topic partitions on a broker.
• delegates copy, read, and delete of topic partition segments to a pluggable storage manager(viz RemoteStorageManager) implementation and maintains respective remote log segment metadata through RemoteLogMetadataManager.

RLM creates tasks for each leader or follower topic partition, which are explained in detail here.

• RLM Leader Task
  • It checks for rolled over LogSegments (which have the last message offset less than last stable offset of that topic partition) and copies them along with their offset/time/transaction indexes and leader epoch cache to the remote tier. It also serves the fetch requests for older data from the remote tier. Local logs are not cleaned up till those segments are copied successfully to remote even though their retention time/size is reached.

[We proposed an approach to creating a RemoteLogSegmentIndex, per topic-partition to track remote LogSegments. These indexes are described in more detail here. This allows having a larger index interval of remote log segments instead of a large number of small index files. It also supports encrypted segments by encrypting individual record batch and build the respective indexes. We may want to explore this approach by enhancing RemoteStorageManager in later versions ]

• RLM Follower Task 
  • It keeps track of the segments and index files on remote tier by looking into RemoteLogMetdataManager. RLM follower can also serve reading old data from the remote tier.

RLM maintains a bounded cache(possibly LRU) of the index files of remote log segments to avoid multiple index fetches from the remote storage. These indexes can be used in the same way as local segment indexes are used. 

Local and Remote log offset constraints

Below are the leader topic partition's log offsets

Lx  = Local log start offset           L_z  = Local log end offset            L_y  = Last stable offset(LSO)

R_y  = Remote log end offset       R_x  = Remote log start offset

Lz >= Ly >= Lx and Ly >= Ry >= Rx

Manage Remote Log Segments

The leader may fail to ship segment data to remote storage on time. In such a situation, the follower has to keep its local segment files, even if the configured retention time is reached. The local segment files (and the corresponding index files) can only be deleted in the following 2 cases:

1. the follower received the corresponding segment data info from a remote storage and updated its index files and
   
2. the local files are already older than the configured remote retention time

Replica Manager

If RLM is configured, ReplicaManager will call RLM to assign or remove topic-partitions.

If the broker changes its state from Leader to Follower for a topic-partition and RLM is in the process of copying the segment, it will finish the copy before it relinquishes the copy for topic-partition. This might leave duplicated segments but these will be cleanedup when these segments are ready for deletion based on remote retention configs.

Follower Replication

Overview

Currently, followers replicate the data from the leader, and try to catch up till the log-end-offset of the leader to become in-sync replicas. Followers maintain the same log segments lineage as the leader by doing the truncation if required.

With tiered storage, followers need to maintain the same log segments lineage as the leader. Followers replicate the data that is only available on the leader's local storage. But they need to build the state like leader epoch cache and producer id snapshots for the remote segments and they also need to do truncation if required. 

Below diagram gives a brief overview of the interaction between leader, follower and remote log  and metadata storages. It will be described more in detail in the next section.

1. Leader copies log segments with auxiliary state(includes leader epoch cache and producer-id snapshots) to remote storage.
2. Leader publishes remote log segment metadata about the copied remote log segment, 
3. Follower tries to fetch the messages from the leader and follows the protocol mentioned in detail in the next section. 
4. Follower waits till it catches up consuming the required remote log segment metadata.
5. Follower fetches the respective remote log segment metadata to build auxiliary state.

Follower fetch protocol in detail

Leader epoch was introduced for handling possible log divergence among replicas in a few leadership change scenarios mentioned in KIP-101 and KIP-279. This is a monotonically increasing number for partition in a single leadership phase and it is stored in each message batch.

Leader epoch sequence file is maintained for each partition by each broker, and all in-sync replicas are guaranteed to have the same leader epoch history and the same log data.

Leader epoch is used to

• decide log truncation (KIP-101),
• keep consistency across replicas (KIP-279), and 
• reset consumer offsets after truncation (KIP-320). 

Incase of remote storage also, we should maintain log lineage and leader epochs like it is done with local storage.

Currently, followers build the auxiliary state (i.e. leader epoch sequence, producer snapshot state) when they fetch the messages from the leader by reading the message batches. Incase of tiered storage, follower finds the offset and leader epoch upto which the auxiliary state needs to be built from the leader. After which,  followers start fetching the data from the leader starting from that offset. That offset can be local-log-start-offset or next-local-offset. Local-log-start-offset is the log start offset of the local storage. Next local offset is the offset upto which the segments are copied to remote storage. We will describe pros and cons of choosing these segments.

next-local-offset

• Advantage with this option is that followers can catchup quickly with the leader as the segments that are required to be fetched by followers are the segments which are not yet moved to remote storage.  
• One disadvantage with this approach is that followers may have a few local segments than the leader. When that follower becomes a leader then the existing followers will truncate their logs to the leader's local log-start-offset. 

local-log-start-offset

• This will honor local log retention in case of leader switches.
• It will take longer for a lagging follower to become an insync replica by catching up with the leader. One of those cases can be a new follower replica added for a partition need to start fetching from local log start offset to become an insync follower. So, this may take longer based on the local log segments available on the leader. 

We prefer to go with local log start offset as the offset from which follower starts to replicate the local log segments for the  above mentioned reasons.

With tiered storage, the leader only returns the data that is still in the leader's local storage. Log segments that exist only on remote storage are not replicated to followers as those are already present in remote storage. Followers fetch offsets and truncate their local logs if needed with the  current mechanism based on the leader's local-log-start-offset. This is described with several cases in detail in the next section.

When a follower fetches data for an offset which is no longer available in the leader's local storage, the leader will send a new error code `OFFSET_MOVED_TO_TIERED_STORAGE`. After that, follower finds the local-log-start-offset and respective leader epoch from the leader. Followers need to build the auxiliary state of the remote log segments till that offset, which are leader epochs and producer-snapshot-ids. This can be done in two ways.

• introduce a new protocol (or API) to fetch this state from the leader partition.
• fetch this state from the remote storage.

Latter is preferred here as remote storage can have this state and it is simpler without introducing a new protocol with the leader.

This involves two steps in getting the required state of the respective log segment for the requested fetch offset.

• it should fetch the respective remote log segment metadata and
• it should fetch respective state like leader epochs from remote storage for the respective remote log segment metadata. 

When shipping a log segment to remote storage, the leader broker will store the leader epoch sequence and producer id snapshot up to the end of the segment into the same remote directory (or the same remote object key prefix). These data can be used by the followers to rebuild the leader epoch sequences and producer id snapshots when needed.

So, we need to add a respective ReplicaState for building auxiliary state which can be called `BuildingRemoteLogAux`. Fetcher thread processes this state also in every run like it does for Fetching and Truncating states.

When a follower tries to fetch an offset that is no longer in the leader's local storage, the leader returns OffsetMovedToRemoteStorage error. Upon receiving this error, the follower will

1) Retrieve the Earliest Local Offset (ELO) and the corresponding leader epoch (ELO-LE) from the leader with a ListOffset request (timestamp = -3)

2) Truncate local log and local auxiliary state

3) Transfer from Fetching state to BuildingRemoteLogAux state

In BuildingRemoteLogAux state, the follower will

Option 1:

Repeatedly call FetchEarliestOffsetFromLeader from ELO-LE to the earliest leader epoch that the leader knows, and build local leader epoch cache accordingly.

Option 2:

1) Wait for RLMM to receive remote segment information, until there is a remote segment that contains the ELO-LE.

2) Fetch the leader epoch sequence from remote storage (using remote storage fetcher thread pool)

3) Build the local leader epoch cache by cutting the leader epoch sequence received from remote storage to [LSO, ELO]. (LSO = log start offset)

After building the local leader epoch cache, the follower transfers back to Fetching state, and continues fetching from ELO.

Let  us discuss a few cases that followers can encounter while it tries to replicate from the leader and build the auxiliary state from remote storage.

OMRS : OffsetMovedToRemoteStorage

ELO : Earliest-Local-Offset

LE-x : Leader Epoch x, 

HW : High Watermark

seg-a-b: a remote segment with first-offset = a and last-offset = b

LE-x, y : A leader epoch sequence entry indicates leader-epoch x starts from offset y

Follower fetch scenarios

Scenario 1: new empty follower

Broker is added to the cluster and assigned as a replica for a partition. This broker will not have any local data as it  has just become a follower for the first time. It will try to fetch the offset 0 from the leader. If that offset does not exist on the leader, the follower will receive the OFFSET_MOVED_TO_TIERED_STORAGE error. The follower will then send a ListOffset request with timestamp = EARLIEST_LOCAL_TIMESTAMP, and will receive the offset of the leader's earliest local message.

The follower will need to build the state till that offset before it starts to fetch from the leader's local storage.

step 1:

Fetch remote segment info, and rebuild leader epoch sequence.

step 2:

continue fetching from the leader

Scenario - 2: out-of-sync follower catching up

A follower is trying to catch up, and the segment has moved to tiered storage. It involves two cases like whether the local segment exists or not. 

2.1 Local segment exists and the latest local offset is larger than the earliest-local-offset of the leader

In this case, followers fetch like earlier as the local segments exist. There will not be any changes for this case.

2.2 Local segment does not exist, or the latest local offset is smaller than ELO of the leader

In this case, local segments might have already been deleted because of the local retention settings, or the follower has been offline for a very long time. The follower receives OFFSET_MOVED_TO_TIERED_STORAGE error while trying to fetch the desired offset. The follower has to truncate all the local log segments, because we know the data already expired on the leader.

step 1:

An out-of-sync follower (broker B) has local data up to offset 3

step 2:

Local segments on the leader are deleted because of retention, and then the follower starts trying to catch up with the leader.

step 3:

After deleting the local data, this case becomes the same as scenario 1.

Scenario 3: Multiple hard failures (Senario 2 of KIP-101)

Step 1:

Broker A has shipped its 1st log segment to remote storage.

Step 2:

Both broker A and broker B crashed at the same time. Some messages (msg 1 and msg 2) on broker B were not synced to the hard disk, and were lost.

In this case, it is acceptable to lose data, but we have to keep the same behaviour as described in KIP-101.

After restart, B losses message 1 and 2. B becomes the new leader, and receives a new message 3 (LE1, offset 1).

(Note: This may not be technically an unclean-leader-election, because B may have not been removed from ISR because both of the 2 brokers crashed at the same time.)

Step 3:

After restart, broker A truncates offset 1 and 2 (LE-0), and receives the new message (LE-1, offset 1).

Step 4:

A new message (message 4) is received. The 2nd segment on broker B (seg-1-1) is shipped to remote storage.

The local segments upto offset 2 are deleted on both brokers.

A consumer fetches offset 0, LE-0. According to the local leader epoch cache, offset 0 LE-0 is valid. So, the broker returns message 0 from remote segment 0-1.

A pre-KIP-320 consumer fetches offset 1, without leader epoch info. According to the local leader epoch cache, offset 1 belongs to LE-1. So, the broker returns message 3 from remote segment 1-1, rather than the LE-0 offset 1 message (message 1) in seg-0-1.

A consumer fetches offset 2 LE0 is fenced (KIP-320).

A consumer fetches offset 1 LE1 receives message 3 from remote segment 1-1.

Scenario 4: (unclean leader election)

Step 1:

Step 2:

Broker A stopped, an out-of-sync replica (broker B) became the new leader. With unclean-leader-election, it's acceptable to lose data, but we have to make sure the existing Kafka behaviour is not changed.

We assume min.in_sync = 1 in this example.

Broker B ships its local segment (seg-0-1) to remote storage, after the highwater mark is moved to 2 (message 5).

Step 3:

The 1st local segment on broker B expired.

A consumer fetches offset 0 LE-0 receives message 0 (LE-0, offset 0). This message can be served from either remote segment seg-0-2 or seg-0-1.

A pre-KIP-320 consumer fetches offset 1. The broker finds offset 1 belongs to leader epoch 1. So, it returns message 4 (LE-1, offset 1) to the consumer, rather than message 1 (LE-0, offset 1).

A post-KIP-320 consumer fetches offset 1 LE-1 receives message 4 (LE-1, offset 1) from remote segment 0-1.

A consumer fetches offset 2 LE-0 is fenced (KIP-320).

Scenario 5: (log divergence in remote storage - unclean leader election)

step 1

step 2

An out-of-sync broker B becomes the new leader, after broker A is down. (unclean leader election)

step 3

Broker B is down. Broker A restarted without knowing LE-1. (another unclean leader election)

step 4

Broker B reimaged and lost all the local data

A consumer fetches offset 3, LE-1 from broker B will be fenced.

A pre-KIP-320 consumer fetches offset 2 from broker B will get msg 2 (offset 2, LE-0).

Transactional support

RemoteLogManager copies transaction index and producer-id-snapshot along with the respective log segment earlier to last-stable-offset. This is used by the followers to return aborted transactions in fetch requests with isolation level as READ_COMMITTED. 

Consumer Fetch Requests

For any fetch requests, ReplicaManager will proceed with making a call to readFromLocalLog, if this method returns OffsetOutOfRange exception it will delegate the read call to RemoteLogManager. More details are explained in the RLM/RSM tasks section.

Fetch from follower

There are no changes required for this to work in the case of tiered storage. 

Other APIs

DeleteRecords

There is no change in the semantics of this API. It deletes records until the given offset if possible. This is equivalent to updating logStartOffset of the partition log with the given offset if it is greater than the current log-start-offset and it is less than or equal to high-watermark. If needed, it will clean remote logs asynchronously after updating the log-start-offset of the log.

ListOffsets

ListOffsets API gives the offset(s) for the given timestamp either by looking into the local log or remote log time indexes. 

If the target timestamp is

ListOffsetRequest.EARLIEST_TIMESTAMP (value as -2) returns logStartOffset of the log.

ListOffsetRequest.LATEST_TIMESTAMP(value as-1) returns log-stable-offset or log-end-offset based on the isolation level in the request.

This API is enhanced with supporting new target timestamp value as -3 which is called EARLIEST_LOCAL_TIMESTAMP. There will not be any new fields added in request and response schemes but there will be a version bump to indicate the version update. This request is about the offset that the followers should start fetching to replicate the local logs. It represents the log-start-offset available in the local log storage which is also called as local-log-start-offset. All the records earlier to this offset can be considered as copied to the remote storage. This is used by follower replicas to avoid fetching records that are already copied to remote tier storage.

When a follower replica needs to fetch the earliest messages that are to be replicated then it sends a request with target timestamp as EARLIEST_LOCAL_TIMESTAMP. 

For timestamps >= 0, it returns the first message offset whose timestamp is >= to the given timestamp in the request. That means it checks in remote log time indexes first, after which local log time indexes are checked.

LeaderAndIsr

This is received by RLM to register for new leaders so that the data can be copied to the remote storage. RLMM will also register the respective metadata partitions for the leader/follower partitions if they are not yet subscribed.  

Stopreplica

RLM receives a callback and unassigns the partition for leader/follower task, If the delete option is enabled then remote log segment metadata is updated in RLMM by enabling the delete marker. RLMM will eventually delete these segments by using RemoteStorageManager. As the segments are deleted asynchronously, creation of a topic with the same name later may cause issues because of the existing metadata about the earlier generation of the topic. It was discussed in the community earlier for adding UUID to represent a topic along with the name as part of KIP-516. That enhancement will be useful to address the issue mentioned here. 

OffsetForLeaderEpoch

Look into leader epoch checkpoint cache. This is stored in tiered storage and it may be fetched by followers from tiered storage as part of the fetch protocol. 

LogStartOffset

LogStartOffset of a topic can be either in local or in remote storage. This is already maintained in `Log` class while loading the logs and it can also be fetched from RemoteLogMetadataManager.  

JBOD related changes

Currently, JBOD is supported by altering log dirs in two ways.

• Altering to a different dir on the local broker

  • This is not supported in this KIP but we may plan this in future releases.

• Altering to a dir on a remote broker

  • This is equivalent to reassigning partitions to a different broker, which is already supported in this KIP as part of how followers behave with respect to remote tiered storage.

There are no changes with other protocol APIs because of tiered storage. 

RLM/RSM tasks and thread pools

Remote storage (e.g. HDFS/S3/GCP) is likely to have higher I/O latency and lower availability than local storage.

When the remote storage becoming temporarily unavailable (up to several hours) or having high latency (up to minutes), Kafka should still be able to operate normally. All the Kafka operations (produce, consume local data, create/expand topics, etc.) that do not rely on remote storage should not be impacted. The consumers that try to consume the remote data should get reasonable errors, when remote storage is unavailable or the remote storage requests timeout.

To achieve this, we have to handle remote storage operations in dedicated threads pools, instead of Kafka I/O threads and fetcher threads.

1. Remote Log Manager (RLM) Thread Pool

RLM maintains a list of the topic-partitions it manages. The list is updated in Kafka I/O threads, when topic-partitions are added to / removed from RLM. Each topic-partition in the list is assigned a scheduled processing time. The RLM thread pool processes the topic-partitions that the "scheduled processing time" is less than or equal to the current time.

When a new topic-partition is assigned to the broker, the topic-partition is added to the list, with scheduled processing time = 0, which means the topic-partition has to be processed immediately, to retrieve information from remote storage.

After a topic-partition is successfully processed by the thread pool, it's scheduled processing time is set to ( now() + rlm_process_interval_ms ). rlm_process_interval_ms can be configured in broker config file.

If the process of a topic-partition is failed due to remote storage error, its scheduled processing time is set to ( now() + rlm_retry_interval_ms ). rlm_retry_interval_ms can be configured in broker config file.

When a topic-partition is unassigned from the broker, the topic-partition is not currently processed by the thread pool, the topic-partition is directly removed from the list; otherwise, the topic-partition is marked as "deleted", and will be removed after the current process is done.

Each thread in the thread pool processes one topic-partition at a time in the following steps:

Copy log segments to remote storage (leader)

Copy the log segment files that are

       - inactive and

       - the offset range is not covered completely by the segments on the remote storage and

      - those segments have the last offset < last-stable-offset of the partition.

If multiple log segment files are ready, they are copied to remote storage one by one, from the earliest to the latest. It generates a universally unique RemoteLogSegmentId for each segment, it calls RLMM.putRemoteLogSegmentData(RemoteLogSegmentId remoteLogSegmentId, RemoteLogSegmentMetadata remoteLogSegmentMetadata) and it invokes copyLogSegment(RemoteLogSegmentId remoteLogSegmentId, LogSegmentData logSegmentData) on RSM. If it is successful then  it calls RLMM.putRemoteLogSegmentData with the updated RemoteLogSegmentMetadata instance else it removes the entry. Any dangling entries will be removed while removing expired log segments based on remote retention. 

Handle expired remote segments (leader and follower)

RLM leader computes the log segments to be deleted based on the remote retention config. It updates the earliest offset for the given topic partition in RLMM. It gets all the remote log segment ids and removes them from remote storage using RemoteStorageManager. It also removes respective metadata using RemoteLogMetadataManager. If there are any failures in removing remote log segments then those are stored in a specific topic (default as __remote_segments_to_be_deleted) and user can consume the events(which contain remote-log-segment-id) from that topic and clean them up from remote storage.  This can be improved upon in later versions. 

RLM follower fetches the earliest offset by calling RLMM.earliestLogOffset(tp: TopicPartition).Both leader and follower cleanup the existing indexes till that offset and updates start offset with the received value.

2. Remote Storage Fetcher Thread Pool

When handling consumer fetch request, if the required offset is in remote storage, the request is added into "RemoteFetchPurgatory", to handle timeout. RemoteFetchPurgatory is an instance of kafka.server.DelayedOperationPurgatory, and is similar to the existing produce/fetch purgatories. At the same time, the request is put into the task queue of "remote storage fetcher thread pool".

Each thread in the thread pool processes one remote fetch request at a time. The remote storage fetch thread will

1. find out the corresponding RemoteLogSegmentId from RLMM and startPosition and endPosition from the offset index.
2. try to build Records instance data fetched from RSM.fetchLogSegmentData(RemoteLogSegmentMetadata remoteLogSegmentMetadata, Long startPosition, Optional<Long> endPosition)
   1. if success, RemoteFetchPurgatory will be notified to return the data to the client
   2. if the remote segment file is already deleted, RemoteFetchPurgatory will be notified to return an error to the client.
   3. if the remote storage operation failed (remote storage is temporarily unavailable), the operation will be retried with Exponential Back-Off, until the original consumer fetch request timeout.

RemoteLogMetadataManager implemented with an internal topic

Metadata of remote log segments are stored in an internal topic called `__remote_log_metadata`. This topic can be created with default partitions count as 50. Users can configure the topic name, partitions count and replication factor etc. 

In this design, RemoteLogMetadataManager(RLMM) is responsible for storing and fetching remote log metadata. It provides

• Storing remote log metadata for a partition based on offsets
• Fetching remote log segment metadata for an offset
• Register a topic partition to build cache for remote log metadata by reading from remote log metadata topic

RemoteLogMetadataManager(RLMM) mainly has the below components

• Cache
• Producer
• Consumer

Remote log metadata topic partition for a given user topic-partition is:

user-topic-partition.toString().hashCode() % no_of_remote_log_metadata_topic_partitions

RLMM registers the topic partitions that the broker is either a leader or a follower. 

For leader replicas,  RemoteLogManager(RLM) copies the log segment and indexes to the remote storage with the given RemoteLogsegmentId (RemoteStorageManager#copyLogSegment API). After this, RLM calls RLMM to store remote log metadata. This is stored in the remote log metadata topic and updates the cache. 

For follower replicas, it maintains metadata cache by subscribing to the respective remote log metadata topic partitions. Whenever a topic partition is reassigned to a new broker and RLMM on that broker is not subscribed to the respective remote log metadata topic partition then it will subscribe to the respective remote log metadata topic partition and adds all the entries to the cache. So, in the worst case, RLMM on a broker may be consuming from most of the remote log metadata topic partitions. This requires the cache to be based on disk storage like RocksDB to avoid a high memory footprint on a broker. This will allow us to commit offsets of the partitions that are already read. Committed offsets can be stored in a local file to avoid reading the messages again when a broker is restarted.

[We will add more details later about how the resultant state for each topic partition is computed ]

Truncation of remote segments under unclean leader election

It may be possible that log segments to be truncated are in tiered storage when unclean leader election is enabled for a partition. It is possible that segments offloaded to the tiered storage (which were eligible as their end offset < LSO) need to be truncated when leadership of a topic-partition moved to a previously lagging follower. 

Local log segments are truncated in such a situation. We may not want to fetch and update the remote segment which may add a lot of complexity in handling the followers which started reading that segment. One way to address this is to update the respective remote log segment metadata with truncation offsets.  

todo: We will update with the scenarios in handling log truncation. 

New metrics

The following new metrics will be added:

kafka.server:type=KafkaRequestHandlerPool,name=

Performance Test Results

We have tested the performance of the initial implementation of this proposal.

The cluster configuration:

1. 5 brokers
2. 20 CPU cores, 256GB RAM (each broker)
3. 2TB * 22 hard disks in RAID0 (each broker)
4. Hardware RAID card with NV-memory write cache
5. 20Gbps network
6. snappy compression
7. 6300 topic-partitions with 3 replicas
8. remote storage uses HDFS

Each test case is tested under 2 types of workload (acks=all and acks=1)

Test case 1 (Normal case):

Normal traffic as described above.

We can see there is a little overhead when tiered storage is turned on. This is expected, as the brokers have to ship segments to remote storage, and sync the remote segment metadata between brokers. With at-least-once (acks=all) produce, the produce latency is slightly increased when tiered storage is turned on. With acks=1 produce, the produce latency is almost not changed when tiered storage is turned on.

Test case 2 (out-of-sync consumers catching up):

In addition to the normal traffic, 9 out-of-sync consumers consume 180MB/s per broker (or 900MB/s in total) old data.

With tiered storage, the old data is read from HDFS. Without tiered storage, the old data is read from local disk.

Consuming old data has a significant performance impact to acks=all producers. Without tiered storage, the P99 produce latency is almost tripled. With tiered storage, the performance impact is relatively lower, because remote storage reading does not compete the local hard disk bandwidth with produce requests.

Consuming old data has little impact to acks=1 producers.

Test case 3 (rebuild broker):

Under the normal traffic, stop a broker, remove all the local data, and rebuild it without replication throttling. This case simulates replacing a broken broker server.

With tiered storage, the rebuilding broker only needs to fetch the latest data that has not been shipped to remote storage. Without tiered storage, the rebuilt broker has to fetch all the data that has not expired from the other brokers. With the same log retention time, tiered storage reduced the rebuilding time by more than 100 times.

Without tiered storage, the rebuilding broker has to read a large amount of data from the local hard disks of the leaders. This competes page cache and local disk bandwidth with the normal traffic, and dramatically increases the acks=all produce latency.

Future work

• Enhance RLMM implementation based on topic based storage pointing to a target Kafka cluster instead of using as system level topic with in the cluster.  
• Improve default RLMM implementation with less chatty protocol.

Alternatives considered

Following alternatives were considered:

1. Replace all local storage with remote storage - Instead of using local storage on Kafka brokers, only remote storage is used for storing log segments and offset index files. While this has the benefits related to reducing the local storage, it has the problem of not leveraging the local disk for efficient latest reads as done in Kafka today.
   
   
2. Implement Kafka API on another store - This is an approach that is taken by some vendors where Kafka API is implemented on a different distributed, scalable storage (example HDFS). Such an option does not leverage Kafka other than API compliance and requires the much riskier option of replacing the entire Kafka cluster with another system.
   
   
3. Client directly reads remote log segments from the remote storage - The log segments on the remote storage can be directly read by the client instead of serving it from Kafka broker. This reduces Kafka broker changes and has benefits of removing an extra hop. However, this bypasses Kafka security completely, increases Kafka client library complexity and footprint and hence is not considered.

4. Remote Log Indexes

   For each topic partition that has RLM configured, RLM leader for a topic partition copies log segments which have last message offset less than last stable offset of that topic partition to remote storage. The active segment file (the last segment file of each partition, to which the new records are appending) is never shipped to remote storage.

   After a segment file is copied to remote storage, RLM will append a set of index entries to 3 local index files: remoteLogIndex, remoteOffsetIndex, remoteTimeIndex. These index files are rotated by RLM at a configurable time interval (or a configurable size).

   (active segment) {log.dirs}/{topic-partition}/0000002400013.index {log.dirs}/{topic-partition}/0000002400013.timeindex {log.dirs}/{topic-partition}/0000002400013.log (inactive segments) {log.dirs}/{topic-partition}/0000002000238.index {log.dirs}/{topic-partition}/0000002000238.timeindex {log.dirs}/{topic-partition}/0000002000238.log {log.dirs}/{topic-partition}/0000001600100.index {log.dirs}/{topic-partition}/0000001600100.timeindex {log.dirs}/{topic-partition}/0000001600100.log ( remote segment) {log.dirs}/{topic-partition}/0000001000121.remoteOffsetIndex {log.dirs}/{topic-partition}/0000001000121.remoteTimeIndex {log.dirs}/{topic-partition}/0000001000121.remoteLogIndex ( remote segments) {log.dirs}/{topic-partition}/0000000512002.remoteOffsetIndex {log.dirs}/{topic-partition}/0000000512002.remoteTimeIndex {log.dirs}/{topic-partition}/0000000512002.remoteLogIndex

   Each index entry of the remoteLogIndex file contains the information of a sequence of records in the remote log segment file. The format of a remoteLogIndex entry:

   magic: int16 (current magic value is 0) length: int16 (length of this entry) crc: int32 (checksum from firstOffset to the end of this entry) firstOffset: int64 (the Kafka offset of the 1st record) lastOffset: int64 (the Kafka offset of the last record) firstTimestamp: int64 lastTimestamp: int64 dataLength: int32 (length of the remote data) rdiLength: int16 rdi: byte[] (Remote data identifier)

   
   

   RDI (Remote data identifier) is the "pointer" or "URI" of the remote data. The format of RDI depends on the implementation. For example, RDI can be HDFS file path and offset, or S3 key and offset. When reading the remote records, RLM will use RDI to retrieve the remote data.

   Depends on the implementation, RLM may append 1 or more entries to the remoteLogIndex file for each remote segment file. More entries will provide fine-grained indexing of the remote data with the cost of local disk space.

   The RemoteLogIndex entries are shipped to remote storage along with the segment data. The followers will retrieve those index entries from remote storage to build their own indices.

   Remoteoffsetindex file and remoteTimestampIndex file are similar with the existing .index file (offset index) and .timeindex file (timestamp index). The only difference is that they point to the index in the corresponding remoteLogIndex file instead of a log segment file.