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    <h3 class="anchor-heading"><a id="networklayer" class="anchor-link"></a><a href="#networklayer">5.1 Network Layer</a></h3>
    <p>
    The network layer is a fairly straight-forward NIO server, and will not be described in great detail. The sendfile implementation is done by giving the <code>TransferableRecords</code> interface a <code>writeTo</code> method. This allows the file-backed message set to use the more efficient <code>transferTo</code> implementation instead of an in-process buffered write. The threading model is a single acceptor thread and <i>N</i> processor threads which handle a fixed number of connections each. This design has been pretty thoroughly tested <a href="https://web.archive.org/web/20120619234320/https://sna-projects.com/blog/2009/08/introducing-the-nio-socketserver-implementation/">elsewhere</a> and found to be simple to implement and fast. The protocol is kept quite simple to allow for future implementation of clients in other languages.
    </p>
    <h3 class="anchor-heading"><a id="messages" class="anchor-link"></a><a href="#messages">5.2 Messages</a></h3>
    <p>
    Messages consist of a variable-length header, a variable-length opaque key byte array and a variable-length opaque value byte array. The format of the header is described in the following section.
    Leaving the key and value opaque is the right decision: there is a great deal of progress being made on serialization libraries right now, and any particular choice is unlikely to be right for all uses. Needless to say a particular application using Kafka would likely mandate a particular serialization type as part of its usage. The <code>RecordBatch</code> interface is simply an iterator over messages with specialized methods for bulk reading and writing to an NIO <code>Channel</code>.</p>

    <h3 class="anchor-heading"><a id="messageformat" class="anchor-link"></a><a href="#messageformat">5.3 Message Format</a></h3>
    <p>
    Messages (aka Records) are always written in batches. The technical term for a batch of messages is a record batch, and a record batch contains one or more records. In the degenerate case, we could have a record batch containing a single record.
    Record batches and records have their own headers. The format of each is described below. </p>

    <h4 class="anchor-heading"><a id="recordbatch" class="anchor-link"></a><a href="#recordbatch">5.3.1 Record Batch</a></h4>
	<p> The following is the on-disk format of a RecordBatch. </p>
	<pre><code class="language-text">baseOffset: int64
batchLength: int32
partitionLeaderEpoch: int32
magic: int8 (current magic value is 2)
crc: uint32
attributes: int16
    bit 0~2:
        0: no compression
        1: gzip
        2: snappy
        3: lz4
        4: zstd
    bit 3: timestampType
    bit 4: isTransactional (0 means not transactional)
    bit 5: isControlBatch (0 means not a control batch)
    bit 6: hasDeleteHorizonMs (0 means baseTimestamp is not set as the delete horizon for compaction)
    bit 7~15: unused
lastOffsetDelta: int32
baseTimestamp: int64
maxTimestamp: int64
producerId: int64
producerEpoch: int16
baseSequence: int32
recordsCount: int32
records: [Record]</code></pre>
    <p> Note that when compression is enabled, the compressed record data is serialized directly following the count of the number of records. </p>

    <p>batchLength represents the number of bytes from the current position (immediately after the batchLength field) to the end of the batch. 
    In other words, the total size of a record batch on disk is batchLength + 12 bytes, which includes the 8-byte baseOffset and the 4-byte batchLength field itself.</p>
    
    <p>The CRC covers the data from the attributes to the end of the batch (i.e. all the bytes that follow the CRC). It is located after the magic byte, which
    means that clients must parse the magic byte before deciding how to interpret the bytes between the batch length and the magic byte. The partition leader
    epoch field is not included in the CRC computation to avoid the need to recompute the CRC when this field is assigned for every batch that is received by
    the broker. The CRC-32C (Castagnoli) polynomial is used for the computation.</p>

    <p>On compaction, we preserve the first and last offset/sequence numbers from the original batch when the log is cleaned. This is required in order to be able to restore the
    producer's state when the log is reloaded. If we did not retain the last sequence number, for example, then after a partition leader failure, the producer might see an OutOfSequence error. The base sequence number must
    be preserved for duplicate checking (the broker checks incoming Produce requests for duplicates by verifying that the first and last sequence numbers of the incoming batch match the last from that producer). As a result,
    it is possible to have empty batches in the log when all the records in the batch are cleaned but batch is still retained in order to preserve a producer's last sequence number. One oddity here is that the baseTimestamp
    field is not preserved during compaction, so it will change if the first record in the batch is compacted away.</p>

    <p>Compaction may also modify the baseTimestamp if the record batch contains records with a null payload or aborted transaction markers. The baseTimestamp will be set to the timestamp of when those records should be deleted
    with the delete horizon attribute bit also set.</p>

    <h5 class="anchor-heading"><a id="controlbatch" class="anchor-link"></a><a href="#controlbatch">5.3.1.1 Control Batches</a></h5>
    <p>A control batch contains a single record called the control record. Control records should not be passed on to applications. Instead, they are used by consumers to filter out aborted transactional messages.</p>
    <p> The key of a control record conforms to the following schema: </p>
    <pre><code class="language-text">version: int16 (current version is 0)
type: int16 (0 indicates an abort marker, 1 indicates a commit)</code></pre>
    <p>The schema for the value of a control record is dependent on the type. The value is opaque to clients.</p>

    <h4 class="anchor-heading"><a id="record" class="anchor-link"></a><a href="#record">5.3.2 Record</a></h4>
	<p>The on-disk format of each record is delineated below. </p>
	<pre><code class="language-text">length: varint
attributes: int8
    bit 0~7: unused
timestampDelta: varlong
offsetDelta: varint
keyLength: varint
key: byte[]
valueLength: varint
value: byte[]
headersCount: varint
Headers => [Header]</code></pre>
	<h5 class="anchor-heading"><a id="recordheader" class="anchor-link"></a><a href="#recordheader">5.3.2.1 Record Header</a></h5>
	<pre><code class="language-text">headerKeyLength: varint
headerKey: String
headerValueLength: varint
Value: byte[]</code></pre>
    <p>The key of a record header is guaranteed to be non-null, while the value of a record header may be null. The order of headers in a record is preserved when producing and consuming.</p>
    <p>We use the same varint encoding as Protobuf. More information on the latter can be found <a href="https://developers.google.com/protocol-buffers/docs/encoding#varints">here</a>. The count of headers in a record
    is also encoded as a varint.</p>

    <h4 class="anchor-heading"><a id="messageset" class="anchor-link"></a><a href="#messageset">5.3.3 Old Message Format</a></h4>
    <p>
        Prior to Kafka 0.11, messages were transferred and stored in <i>message sets</i>. See <a href="https://kafka.apache.org/39/documentation/#messageset">Old Message Format</a> for more details.
    </p>
    <h3 class="anchor-heading"><a id="log" class="anchor-link"></a><a href="#log">5.4 Log</a></h3>
    <p>
    A log for a topic named "my-topic" with two partitions consists of two directories (namely <code>my-topic-0</code> and <code>my-topic-1</code>) populated with data files containing the messages for that topic. The format of the log files is a sequence of "log entries"; each log entry is a 4 byte integer <i>N</i> storing the message length which is followed by the <i>N</i> message bytes. Each message is uniquely identified by a 64-bit integer <i>offset</i> giving the byte position of the start of this message in the stream of all messages ever sent to that topic on that partition. The on-disk format of each message is given below. Each log file is named with the offset of the first message it contains. So the first file created will be 00000000000000000000.log, and each additional file will have an integer name roughly <i>S</i> bytes from the previous file where <i>S</i> is the max log file size given in the configuration.
    </p>
    <p>
    The exact binary format for records is versioned and maintained as a standard interface so record batches can be transferred between producer, broker, and client without recopying or conversion when desirable. The previous section included details about the on-disk format of records.
    </p>
   <p>
    The use of the message offset as the message id is unusual. Our original idea was to use a GUID generated by the producer, and maintain a mapping from GUID to offset on each broker. But since a consumer must maintain an ID for each server, the global uniqueness of the GUID provides no value. Furthermore, the complexity of maintaining the mapping from a random id to an offset requires a heavy weight index structure which must be synchronized with disk, essentially requiring a full persistent random-access data structure. Thus to simplify the lookup structure we decided to use a simple per-partition atomic counter which could be coupled with the partition id and node id to uniquely identify a message; this makes the lookup structure simpler, though multiple seeks per consumer request are still likely. However once we settled on a counter, the jump to directly using the offset seemed natural&mdash;both after all are monotonically increasing integers unique to a partition. Since the offset is hidden from the consumer API this decision is ultimately an implementation detail and we went with the more efficient approach.
    </p>
    <img class="centered" src="/{{version}}/images/kafka_log.png">
    <h4 class="anchor-heading"><a id="impl_writes" class="anchor-link"></a><a href="#impl_writes">Writes</a></h4>
    <p>
    The log allows serial appends which always go to the last file. This file is rolled over to a fresh file when it reaches a configurable size (say 1GB). The log takes two configuration parameters: <i>M</i>, which gives the number of messages to write before forcing the OS to flush the file to disk, and <i>S</i>, which gives a number of seconds after which a flush is forced. This gives a durability guarantee of losing at most <i>M</i> messages or <i>S</i> seconds of data in the event of a system crash.
    </p>
    <h4 class="anchor-heading"><a id="impl_reads" class="anchor-link"></a><a href="#impl_reads">Reads</a></h4>
    <p>
    Reads are done by giving the 64-bit logical offset of a message and an <i>S</i>-byte max chunk size. This will return an iterator over the messages contained in the <i>S</i>-byte buffer. <i>S</i> is intended to be larger than any single message, but in the event of an abnormally large message, the read can be retried multiple times, each time doubling the buffer size, until the message is read successfully. A maximum message and buffer size can be specified to make the server reject messages larger than some size, and to give a bound to the client on the maximum it needs to ever read to get a complete message. It is likely that the read buffer ends with a partial message, this is easily detected by the size delimiting.
    </p>
    <p>
    The actual process of reading from an offset requires first locating the log segment file in which the data is stored, calculating the file-specific offset from the global offset value, and then reading from that file offset. The search is done as a simple binary search variation against an in-memory range maintained for each file.
    </p>
    <p>
    The log provides the capability of getting the most recently written message to allow clients to start subscribing as of "right now". This is also useful in the case the consumer fails to consume its data within its SLA-specified number of days. In this case when the client attempts to consume a non-existent offset it is given an OutOfRangeException and can either reset itself or fail as appropriate to the use case.
    </p>

    <p> The following is the format of the results sent to the consumer.

    <pre><code class="language-text">MessageSetSend (fetch result)

total length     : 4 bytes
error code       : 2 bytes
message 1        : x bytes
...
message n        : x bytes</code></pre>

    <pre><code class="language-text">MultiMessageSetSend (multiFetch result)

total length       : 4 bytes
error code         : 2 bytes
messageSetSend 1
...
messageSetSend n</code></pre>
    <h4 class="anchor-heading"><a id="impl_deletes" class="anchor-link"></a><a href="#impl_deletes">Deletes</a></h4>
    <p>
    Data is deleted one log segment at a time. The log manager applies two metrics to identify segments which are
        eligible for deletion: time and size. For time-based policies, the record timestamps are considered, with the
        largest timestamp in a segment file (order of records is not relevant) defining the retention time for the
        entire segment. Size-based retention is disabled by default. When enabled the log manager keeps deleting the
        oldest segment file until the overall size of the partition is within the configured limit again. If both
        policies are enabled at the same time, a segment that is eligible for deletion due to either policy will be
        deleted. To avoid locking reads while still allowing deletes that modify the segment list we use a copy-on-write
        style segment list implementation that provides consistent views to allow a binary search to proceed on an
        immutable static snapshot view of the log segments while deletes are progressing.
    </p>
    <h4 class="anchor-heading"><a id="impl_guarantees" class="anchor-link"></a><a href="#impl_guarantees">Guarantees</a></h4>
    <p>
    The log provides a configuration parameter <i>M</i> which controls the maximum number of messages that are written before forcing a flush to disk. On startup a log recovery process is run that iterates over all messages in the newest log segment and verifies that each message entry is valid. A message entry is valid if the sum of its size and offset are less than the length of the file AND the CRC32 of the message payload matches the CRC stored with the message. In the event corruption is detected the log is truncated to the last valid offset.
    </p>
    <p>
    Note that two kinds of corruption must be handled: truncation in which an unwritten block is lost due to a crash, and corruption in which a nonsense block is ADDED to the file. The reason for this is that in general the OS makes no guarantee of the write order between the file inode and the actual block data so in addition to losing written data the file can gain nonsense data if the inode is updated with a new size but a crash occurs before the block containing that data is written. The CRC detects this corner case, and prevents it from corrupting the log (though the unwritten messages are, of course, lost).
    </p>

    <h3 class="anchor-heading"><a id="distributionimpl" class="anchor-link"></a><a href="#distributionimpl">5.5 Distribution</a></h3>
    <h4 class="anchor-heading"><a id="impl_offsettracking" class="anchor-link"></a><a href="#impl_offsettracking">Consumer Offset Tracking</a></h4>
    <p>
    Kafka consumer tracks the maximum offset it has consumed in each partition and has the capability to commit offsets so
        that it can resume from those offsets in the event of a restart. Kafka provides the option to store all the offsets for
        a given consumer group in a designated broker (for that group) called the group coordinator.  i.e., any consumer instance
        in that consumer group should send its offset commits and fetches to that group coordinator (broker). Consumer groups are
        assigned to coordinators based on their group names. A consumer can look up its coordinator by issuing a FindCoordinatorRequest
        to any Kafka broker and reading the FindCoordinatorResponse which will contain the coordinator details. The consumer
        can then proceed to commit or fetch offsets from the coordinator broker. In case the coordinator moves, the consumer will
        need to rediscover the coordinator. Offset commits can be done automatically or manually by consumer instance.
    </p>

    <p>
    When the group coordinator receives an OffsetCommitRequest, it appends the request to a special <a href="#compaction">compacted</a> Kafka topic named <i>__consumer_offsets</i>.
        The broker sends a successful offset commit response to the consumer only after all the replicas of the offsets topic receive the offsets.
        In case the offsets fail to replicate within a configurable timeout, the offset commit will fail and the consumer may retry the commit after backing off.
        The brokers periodically compact the offsets topic since it only needs to maintain the most recent offset commit per partition.
        The coordinator also caches the offsets in an in-memory table in order to serve offset fetches quickly.
    </p>

    <p>
    When the coordinator receives an offset fetch request, it simply returns the last committed offset vector from the offsets cache.
        In case coordinator was just started or if it just became the coordinator for a new set of consumer groups (by becoming a leader for a partition of the offsets topic),
        it may need to load the offsets topic partition into the cache. In this case, the offset fetch will fail with an
        CoordinatorLoadInProgressException and the consumer may retry the OffsetFetchRequest after backing off.
    </p>

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