README.md

Kafka Study

Kafka Latest Version - 2.6.0

• Kafka Basics

  • What Why of Kafka

  • Basics

  • Performance

  • Replication

  • Retention

  • Compaction

  • Rebalancing Consumer Groups

  • Delivery Symantics

  • Quota

  • Broker Config

• Kafka Producers

• Kafka Consumers

• Cross Data Center Replication

• Kafka Streams

• Kafka Connect

• Schema Registry

• KSQL

• Kafka CLI

• Miscellaneous

  • Production Deployment

  • Key Metrics

  • ZooKeeper Basics

  • Avro Basics

  • Zookeeper Shell

• References

Kafka Basics

What Why of Kafka

• Kafka originally designed as a message queue has developed into a distributed streaming platform. It is based on a distributed, partitioned, replicated log
• As a messaging system, what makes Kafka different from the traditional messaging systems is its log centric design which makes it suitable for use cases that cannot be so easily and elegantly addressed with other messaging systems
  • Developing applications optimized for both read and write - Kafka allows capturing application data as a time ordered series of immutable events which can then be processed by different consumer processes to produce various read optimized views. Thus, while writes are efficient appends, reads are from read-optimized pre-aggregated denormalized views updated in near-real time. This pattern of developing applications where the read and write models are decoupled is also known as Command Query Responsibility Seggregation (CQRS) and we implement it using another pattern called Event Sourcing which requires the data to be captured as an ordered series of immutable events (as opposed to a state machine) to ensure that the historical information is preserved and the exact state of the application at any point in the past can be deterministically created by replaying the events
  • Replacing dual-writes with log based writes - Applications writing to multiple systems (viz. search index, database, cache, hadoop etc.) can suffer from race condition and partial failure leading to perpetually inconsistent data. Applications can instead write to Kafka and then have the individual systems consume the ordered event stream from Kafka to create their internal state in a deteministic way. In a UI based application, where the user expects a response almost immediately in a synchronous manner, there are couple of options -
    • Change Data Capture - Applications write directly to the database which can then source an event stream from the DB Write Ahead Log (WAL) to Kafka via Kafka Connect. Kafka Stream or Producer API can then be used to prepare the respective system views in separate Kafka topics from the original stream of events and finally export them to the individual systems using Kafka Connect or Kafka Consumer API
    • Request-Reply Enterprise Integration Pattern - Here the application will post the request in a Kafka topic and listen for response on another topic or partition dedicated for response to that specific service. Spring Kafka provides inbuilt support for the pattern, however, it can be little awkward for two reasons - 1. The response for every request will come to every thread or service instance, 2. Every service or request processing thread need to have a different consumer group so that they get all the events. This could be problematic for cloud deployments where multiple instances of the same service are run with the same configuration
  • Materialized View Pattern - In a microservice based architecture, individual microservices can consume events from other services and create it's own pre-aggregated Materialized View and serve data from there instead of querying the respective services during run time. This will lead to reduced latency and loose-coupling between services
  • Recreating views - By sourcing the event stream from Kafka, the individual application specific views can be recreated at anytime after a crash (viz. pre-warming a cache after a restart). This aspect can also be exploited to debug applications by replaing the events to recreate the exact application state in the past
• Some key aspects of Kafka -
  • Kafka is a modern day fault-tolerant distributed system which scales horizontally storing more data than a single machine can hold
  • Kafka provides ordering guarantees within a partition
  • Kafka's durability using distributed replicated partitions allows it to serve as a single source of truth
  • Kafka's compaction cleaning policy allows it to store compacted data for an indefinite period of time
  • Kafka Connect API makes it easy to import/export data from or to other systems
  • Kafka Streamimg API allows processing event stream/s with advanced joining & windowing primitives
  • Kafka's consumer groups allow both queue (load balancing across consumer processes) and publish-subscribe (multiple consumer groups) at the same time
  • Kafka's zero copy, page cache leverage, message batching, smaller heap, and sequential disk access provide good performance
  • Kafka's performance is effectively constant with respect to data size so storing data for a long time is not a problem
  • Kafka's consumers keep track of their progress in separate internal Kafka topics adding very little overhead to the brokers
  • Kafka supports space efficient publish-subscribe as there is only single log no matter how many consumer groups are there

Basics

• Data is organized among topics
• Multiple producers can write to the same topic or multiple topics
• Each topic is partitioned
• Partitions are evenly distributed across the available brokers to distribute the load evenly
• Typically, there will be many more partitions than servers or brokers
• Partitions serve two purposes -
  • Allow storing more records than a single machine can hold
  • Serve as a unit of parallelism
• Producers and Consumers are Kafka clients
• A message in kafka is a key-value pair with a small amount of associated metadata
• A message set (at the internal protocol level) is a group of messages stored (on disk) and transfered (from producer to broker and broker to consumer) together to reduce network roundtrips and many small I/Os. It is the unit of compression
• The broker always decompresses the batch to validate
• Kafka only provides a total order over records within a partition, not between different partitions in a topic
• As Kafka writes its data to local disk, the data may linger in the filesystem cache and may not make its way to the disk. The disk flush parameter are not recommended to set for performance reasons. Therefore Kafka relies solely on replication for reliability and durability
• In Linux, data written to the filesystem is maintained in the pagecache until it must be written out to disk (due to an application-level fsync or the OS's own flush policy)
• Kafka Guarantees
  • Kafka provides order guarantee of messages in a partition
  • Produced messages are considered "committed" when they are written to the partition on all its in-sync replicas (ISR)
  • Messages that are committed will not be lost as long as at least one replica remains alive
  • Consumers can only read messages that are committed
• ZooKeeper holds
  • The upcoming Apache Kafka 3.0 will remove the dependency of Zookeeper. The proposed architecture will use a quorum of controller brokers to elect an active cotroller. The active controller will be elected using a Raft based algorithm. The design looks similar to Elasticsearch master eligible nodes. The metadata is going to be stored in Kafka topic and all other brokers will pull the metadata from the active controller
  • Dynamic per-broker or cluster-wide default config (includes encrypted passwords where secrets used for encoding password is kept in server.properties)
  • Client quota configuration
  • Brokers register themselves in ZooKeeper using ephemeral znodes
  • Leaders of partitions of topics
  • Delegation tokens
  • ACLs
  • List of consumer groups owned by a Co-ordinator and their membership information Not any more with new clients
• Internal topics
  • __transaction_state
  • __consumer_offsets
  • __consumer_timestamps
• CRC32 is used to check if the messages are corrupted
• Shutdown - When a server is stopped gracefully it has two optimizations it will take advantage of:
  • It will sync all its logs to disk to avoid needing to do any log recovery when it restarts (i.e. validating the checksum for all messages in the tail of the log). Log recovery takes time so this speeds up intentional restarts
  • It will migrate any partitions the server is the leader for to other replicas prior to shutting down. This will make the leadership transfer faster and minimize the time each partition is unavailable to a few milliseconds
• Syncing the logs will happen automatically whenever the server is stopped other than by a hard kill, but the controlled leadership migration requires using a special setting: controlled.shutdown.enable=true
• Note that controlled shutdown will only succeed if all the partitions hosted on the broker have replicas (i.e. the replication factor is greater than 1 and at least one of these replicas is alive) - Replication factor 1 (minimum possible value) indicates there is only one copy of data in the cluster
• Default ports
  • Zookeeper: 2181
  • Zookeeper Election Port (Peer port) 2888
  • Zookeeper Leader Port 3888
  • Broker: 9092
  • Schema Registry: 8081
  • REST Proxy: 8082
  • KSQL: 8088
• Prior to release 2.4.0, the records with null keys will be distrubuted across partitions in a round-robin manner. The DefaultPartitioner now uses a sticky partitioning strategy. This means that records for specific topic with null keys and no assigned partition will be sent to the same partition until the batch is ready to be sent. When a new batch is created, a new partition is chosen
• Rack Awareness -
  • broker.rack - If set for a broker, the replicas will be placed to ensure all replicas are not on the same rack. Useful when the brokers are spread across datacenters or AZ in AWS within the same region
  • client.rack - Release 2.4.0 onwards, it allows the consumer to fetch from a follower replica on the same rack, if the partion leader is not available on the same rack. There may be, however, some latency if the follower cannot keep up with the partition leader

Performance

• Modern operating systems are designed to aggressively use the available memory as page cache. Thus if the server hosting Kafka broker is not used for other applications, more page cache will be available for Kafka
• When consumers are lagging only a little from the producer, the broker doesn't need to reread the messages from the disk. The requests can be served directly from the page cache
• Using page cache has several advantages over an in-process cache for storing data that will be written out to disk:
  • The I/O scheduler will batch together consecutive small writes into bigger physical writes which improves throughput
  • The I/O scheduler will attempt to re-sequence writes to minimize movement of the disk head which improves throughput
  • It automatically uses all the free memory on the machine
• Compact byte structure rather than Java objects reduces Java Object ovehead
• Not having an in-process cache for messages makes more memory available for page cache & reduces garbage collection issues with increased in-heap data
• Simple reads and appends to file result in sequential disk access
• Transfer batches of messages over the network to
  • amortize the network roundtrip
  • do larger sequential disk access
  • allocate contiguous memory blocks
  • provide good compression ratio
• Zero-copy - sendfile system call of Linux reduces byte copying (across kernel and user spaces) and context switches
• Standardized binary format shared by producer, broker & consumer reduces recopying & transformation
• Compression of message batch saves network bandwidth
• No intervening routing tier. Messages of a given partition are sent directly to the partition leader
• Consumers use "long poll" to avoid busy waiting and ensure larger transfer sizes

Note: Application level flushing (fsync) gives less leeway to the OS to optimize the writes. The Linux fsync implementation blocks all writes to the file, whereas the OS level flushing makes more granular level locks

Replication

• Each partition can have multiple replicas. For most production system 3 replicas are good enough
• Each broker hosts hundreds or thousands of replicas belonging to different topics
• Replication factor must be less than or equal to the number of brokers up and running
• Kafka recommends using replication for durability and not set the disk flush parameters
• Each partition has a leader replica
• All producer & consumer requests go through the leader replica to guarantee consistency (Starting from 2.4.0, there may be an exception for consumers when client.rack is set)
• Leaders keep track of the last offsets fetched by each replica
• For each partition, Kafka stores in Zookeeper the current leader and the current In-sync replicas (ISR)
• Preferred leader - The replica that was the leader when the topic was originally created
• When the preferred leader is indeed the leader, the load is expected to be more evenly distributed among the brokers
• By default, Kafka is configured with auto.leader.rebalance.enable=true, which will check if the preferred leader replica the current leader. If not and if it is ISR, a leader election will be triggered to make the preferred leader the current leader
• All other replicas (other than the leader replica) are followers
• In the event of leader replica failure, one of the follower replicas will be promoted as leader
• If the leader replica detects that a follower has fallen behind significantly or is not available, it removes the follower from the ISR. Thus it is possible to have only one ISR (the leader itself) and the producer writing to it effectively without durability (unless the min.insync.replicas is set to a value > 1)
• Only in-sync replicas are eligible to be elected as leader in the event of leader failure unless unclean.leader.election.enable is set to true
• Every replica maintains some important offset positions
  • HW (High Water Mark) - Offset of the last committed message
  • LEO (Log End Offset) - Tail of the log
• Conditions for ISR
  • It has sent a heartbeat to Zookeeper in last 18 seconds
  • It has requested message from the leader within last 30 seconds (replica.lag.time.max.ms)
  • It has fetched the LEO from the leader in the last 30 seconds (replica.lag.time.max.ms)
• Followers don't serve client request (until recently when consumers can fetch from followers if the leader is not available in the same rack). Their only job is to replicate messages from the leader and stay in sync with the leader
• Each follower constantly pulls new messages from the leader using a single socket channel. That way, the follower receives all messages in the same order as written in the leader
• A message is considered committed when all the ISR have been updated
• Consumers don't see uncommitted messages regardless of the acks setting which affects only the acknowledgement to the producer
• Replication flow
  • The client fetches the metadata from a bootstrap broker and caches it during initialization
  • If the client gets NotLeaderForPartition, it fetches the latest metadata info from a broker and caches it
  • The client sends the message to the leader
  • The leader writes the message to its local log
  • The follower receives all messages in the same order as written in the leader (Followers constantly pull messages from the leader)
  • The follower writes each received message to its own log
  • The follower Sends an acknowledgment back to the leader
  • Once the leader finds that the the replicas in ISR have LEO >= the given offset within a timeout, the message is committed
  • The leader advances the HW
  • the leader sends an acknowledgment to the client (depending on producer acks parameter)
• Leader replica shares its HW to the followers by piggybacking the value with the return value of the fetch requests from the followers
• From time to time, followers checkpoint their HW to its local disk (replica.high.watermark.checkpoint.interval.ms)
• When a follower comes back after failure, it truncates all the logs after the last check pointed HW and then reads all the logs from the leader after the given HW
• When a partition leader fails, the new leader chooses its LEO as HW (Follower LEO is usually behind leader HW)
• When a new partition leader is elected, the followers truncate their logs after the last check pointed HW and then read all the logs from the new leader after the given HW
• The new partition leader (before making it available for reads and writes by clients) waits until all the surviving ISR have caught up or a configured period has passed
• Controller is a broker that is responsible for electing partition leader (in addition to regular broker responsibilities)
  • The first broker that starts in the cluster will become the controller
  • If the controller goes down, another broker will become the controller with a higher epoch number and thus preventing "split brain" from occuring
• Client
  • Must send the fetch requests to the leader replica of a given partition (otherwise "Not a leader" error is returned) (except the client.rack scenario mentioned above)
  • Knows about the leader replica and broker details for a given topic using metadata requests
  • Caches the metadata information
  • Fetches metadata information when metadata.max.age.ms expires or "Not a leader" error is returned (partition leader moved to a different broker due to failure)
  • Metadata requests can be sent to any broker because all brokers cache this information
• If all the replicas crash, the data that is committed but not written to the disk are lost
• Kafka MirrorMaker provides geo-replication support for your clusters. With MirrorMaker, messages are replicated across multiple datacenters or cloud regions. You can use this in active/passive scenarios for backup and recovery; or in active/active scenarios to place data closer to your users, or support data locality requirements
• To support fetching from the follower replica (because the partition leader is not available on the same rack) based on HW, Kafka leader doesn't delay responding to replica fetch requests if the follower has obsolete HW
• If a replica constantly drops out of and rejoins ISR, you may need to increase replica.lag.max.messages Removed in release 0.9.0
• If a replica stays out of ISR for a long time, it may indicate that the follower is not able to fetch data as fast as data is accumulated at the leader. You can increase the follower’s fetch throughput by setting a larger value for num.replica.fetchers
• replica.lag.time.max.ms - This is typically set to a value that reliably detects the failure of a broker for the purpose of moving it out of ISR. If the metric MinFetchRate is n, set the value for this config to larger than 1/n * 1000

Retention

• Segments
  • Each partition is split into multiple segments
  • By default, each segment contains 1 GB of data (log.segment.bytes) or 1 week worth of data (log.roll.ms or log.roll.hours) whichever is smaller
  • If either of the segment limits is reached, the segment file is closed and a new segment file is created
  • Kafka brokers always keeps 3 open file handles for all the segments of all the partition - even inactive segments
    • File handle to the segment file
    • File handle to the offset index of the segment file
    • File handle to the time index of the segment file (introduced in release 0.10.1.0)
  • Offset index contains a mapping between a relative message offset (within the segment) and the corresponding physical location in the segment file. This allows Kafka to quickly lookup a given offset in a segment file
  • Time index contains a mapping between a relative message offset (within the segment) and the corresponding message time (LogAppendTime or CreateTime depending on log.message.timestamp.type - broker/topic configuration). The following functionalities will refer to the time index
    • Search based on timestamp
    • Time based retention
    • Time based rotation
  • Note that the indexes do not contain all offsets. Each entry represents a range of offsets
  • The segment being currently written to for a given partition is called active segment
  • Active segments are never deleted even if the retention criteria is met
  • If the retention policy is set as "delete", the old segments are deleted depending on retention criteria
  • Smaller log segments mean frequent closing and allocation of files reducing overall disk performance
• Deciding Number of Partitions
  • Avoid underestimating as one partition will always be read by only one consumer
  • Avoid overestimating as each partition uses memory and other resources on a broker and increases the leader election time
• Log Retention
  • Retention by time is done based on the last modification time of the segment file (which is usually the last time a message is added to the segment unless some admistrative activities moved the partitions across brokers resulting is excess retention) (Prior to release 0.10.1.0)
  • Default log retention size is 1 GB (log.retention.bytes). This configuration is applicable per partition and NOT per topic
  • log.retention.ms, log.retention.minutes & log.retention.hours - If more than one of these parameters are set, the smallest unit takes precedence
  • log.retention.check.interval.ms = The frequency at which the log cleaner checks if there is any log for deletion
• offsets.retention.minutes (for consumer offset log) & log.retention.hours (for regular logs) - the default retention is set to 7 days equivalent

Compaction

• log.cleanup.policy = compact
• Kafka will always retain at least the last known value for each message key within the log of data for a single topic partition
• It serves use cases to restore a system or pre-warm a cache after a crash. Essentially it serves the use cases that needs the latest values for the complete set of keys rather than the most recent changes
• Compaction is useful to implement event sourcing or materialized view pattern
• The original offset of the messages are not changed
• Compaction also allows for deletes. A message with a key and a null payload will be treated as a delete from the log
• Delete markers will themselves be cleaned out of the log after a period of time to free up space
• It is possible for a consumer to miss delete markers if it lags by more than delete.retention.ms (default 24 hrs)
• The compaction is done in the background by periodically recopying log segments
• Cleaning does not block reads and can be throttled to use no more than a configurable amount of I/O throughput to avoid impacting producers and consumers
• The topic's min.compaction.lag.ms can be used to guarantee the minimum length of time must pass after a message is written before it could be compacted
• Compaction will never re-order messages, just remove some
• Cleaning is done by a separate pool of threads log.cleaner.threads at an interval

Rebalancing Consumer Groups

• Rebalancing is the process where a group of consumer instances (belonging to the same group) co-ordinate to own a mutually exclusive set of partitions of topics that the group has subscribed to
• Every partition for all subscribed topics will be owned by a single consumer instance
• One of the brokers is elected as the coordinator for a subset of the consumer groups
• Co-ordinator role
  • Triggering rebalancing whenever required (new members connect with co-ordinator, failed consumers are identified by failure detection run by the co-ordinator)
  • Communicating the resulting partition-consumer ownership configuration to all consumers of the group undergoing a rebalance operation
• Essentially, a rebalance kicks in when following conditions are met:
  • Group membership changes, such as a new member joining
  • Member subscription changes, such as one consumer changing the subscribed topics
  • Resource changes, such as adding more partitions to the subscribed topic
• Stages
  • Rebalance -
    • Kill the socket connection to any consumers in the group
    • The consumers re-register with the co-ordinator
  • Sync
    • The group coordinator replies to all members with the latest generation information to fence off any previous generation consumers
    • Nominates one of the members as the leader and replies to the leader with encoded membership and subscription metadata
    • The leader shall complete the assignment based on membership and topic metadata information, and reply to the coordinator with the assignment information
    • All the followers are required to send a sync group request to get their actual assignments
    • Co-ordinator responds back with the assignment information
  • Stable
• Static membership with group.instance.id so that the same set of partitions are assigned back to the member after temporary network partitioning (within session.timeout) without a fresh rebalancing
• Release 2.4.0 onwards, incremental rebalance protocol tries to minimize the partition migration and let the consumers retain their partitions during rebalancing
• Consumers should provide an implementation of ConsumerRebalanceListener to commit the offsets in Kafka and / or external store before the partitions are revoked

Delivery Symantics

• Exactly once semantic was introduced in version 0.11 with support from the following features
  • idempotence - enable.idempotence (producer config)
  • transaction - transactional.id (producer config)
• Kafka supports
  • At most once — Messages may be lost but are never redelivered
  • At least once — Messages are never lost but may be redelivered
  • Exactly once — this is what people actually want, each message is delivered once and only once
• Producer Perspective
  • At most once - No retry if successful response from broker is not received (retries = 0)
  • At least once - If no success response received from broker, retry again (retries = Integer.MAX_VALUE, delivery.timeout.ms = 120000)
  • Exactly once -
    • retries = Integer.MAX_VALUE, delivery.timeout.ms = 120000, enable.idempotence = true
    • Broker assigns each new producer a unique id (PID) and keeps a sequence number per partition that starts from 0 and increments with each message sent (If transactional.id is specified, an epoch is also maintained against the PID to fence off multiple producers with the same PID and transaction id)
    • Relying on the in-order property of Kafka (and TCP), the broker will only keep track of the highest sequence number seen and reject any sequence number which is not exacly 1 higher than the last seen number
    • Messages with a lower sequence number result in a duplicate error, which can be ignored by the producer
    • Messages with a higher number result in an out-of-sequence error, which indicates that some messages have been lost, and is fatal
    • The PID, producer epoch and the first sequence number within the set will be stored at the message set level to reduce the overhead at message level
    • Since each new instance of a producer is assigned a new unique PID, we can only guarantee idempotent production within a single producer session
    • Producers lease PIDs for a fixed duration of time from the transaction co-ordinator broker to ensure that the brokers don't go out-of-memory to keep track of all PIDs
• Consumer Perspective
  • At most once - enable.auto.commit commits the offset even before the messages were processed
  • At least once - The writes to the down stream system when retried without the offsets
  • Exactly once - The offsets can be saved in the downstream system along with the messages. In a key value DB, the message will be simply overwritten and in a relational DB, both can be saved in a transaction
• Transactions across partitions are useful for streaming - consume-process-produce - as consume is also about producing offset messages to keep track of progress, consuming and producing in streaming need to be done in a transaction
• transactional.id is a user provided unique id per producer. It serves two purposes
  • Allows only one transaction to proceed for a given producer
  • Allows recovering a transaction after restart
• Transaction expiry is configured using - transactional.id.expiration.ms
• Kafka default isolation.level is read_uncommitted
• read_uncommitted - consume both committed and uncommitted messages in offset ordering.
• read_committed - Only consume non-transactional messages or committed transactional messages in offset order. The end offset of a partition for a read_committed consumer would be the offset of the first message in the partition belonging to an open transaction. This offset is known as the Last Stable Offset (LSO)
• Kafka Streams API sets the internal embedded producer client with a transaction id to enable the idempotence and transactional messaging features, and also sets its consumer client with the read_committed mode to only fetch messages from committed transactions from the upstream producers
• The components introduced with the transactions API in Kafka 0.11.0 are the Transaction Coordinator and the Transaction Log
• Every broker is a leader for certain number of partitions of the transaction log
• Every transactional.id is mapped to a specific partition of the transaction log through a simple hashing function. Therefore, the leader of this partition becomes the transaction co-ordinator of the given transaction id
• A transactional id is mapped one-to-one with a producer id or PID, but the difference is producer id is auto generated by the broker while the transactional id is provided by the application owner allowing the recovery of a transaction across application restart
• An epoch is maintained along with the producer id. If an application is restarted with a transaction id, the epoch is incremented to fence off any other producer instances running with the save producer id
• The broker only allows a producer with a recognized producer ID and the current epoch for that producer ID to write or commit data
• The transaction co-ordinator is the 2-phase commit arbitrator. However, due to the replication and partition leader election functionalities of Kafka, this 2-phase commit does not suffer from the shortcomings of the traditional 2-phase commit
• A transaction could be in various states like "Ongoing", "Prepare commit", and "Completed". It is this state and associated metadata that is stored in the transaction log

Quota

• Broker config

  quota.window.num
  quota.window.size.seconds
  replication.quota.window.num
  replication.quota.window.size.seconds
  

• quota.window.num or replication.quota.window.num specifies the number of samples to be retained in memory for the calculation
• Throttle Time = (overall produced in window - quotabound)/Quota limit per second
• If the client exceeds the quota, the broker responds with the throttle time X asking the client to refrain from sending further requests for time X
• Two types of client quotas can be enforced by Kafka brokers for each group of clients sharing a quota:
  • Network bandwidth quotas define byte-rate thresholds (since 0.9)
  • Request rate quotas define CPU utilization thresholds as a percentage of network and I/O threads (since 0.11)
• Quotas can be applied to (user, client-id), user or client-id groups
• For a given connection, the most specific quota matching the connection is applied
• Quota overrides are written to ZooKeeper
• By default, each unique client group receives a fixed quota as configured by the cluster. This quota is defined on a per-broker basis

Broker Config

• auto.create.topics.enable - Default value is true. It should be set to false in production as there is no way to validate the topic names
• Tuning for Throughput
  • compression.type = producer (default value) - The broker will decompress the batch to validate and then send the compressed message from producer directly to the consumer
• Tuning for latency
  • num.replica.fetchers - Number of fetcher threads per broker. The threads are used to replicate the messages. Many partitions increase the latency as by default there is only one thread per broker to do fetching from the leader broker for replication
• Tuning for durability -
  • default.replication.factor = 3
• Message Size
  • message.max.bytes - limits the size of a single message batch
  • max.partition.fetch.bytes (consumer) & replica.fetch.max.bytes must be as large as message.max.bytes
  • compression.type - Accepts the standard compression codecs ('gzip', 'snappy', 'lz4', 'zstd'). It additionally accepts 'uncompressed' which is equivalent to no compression; and 'producer' which means retain the original compression codec set by the producer
• zstd should provide the maximum compression ratio with reasonable performance

Kafka Producers

• KafkaProducer<K,V> is thread safe and sharing a single producer instance across threads will generally be faster than having multiple instances as it will improve the batching
• If transactions are used, every producer must have a unique producer id and transactional id and it can have only one transaction at a time. Therefore, when transaction is enabled, each thread should have a separate producer instance
• The send() method is asynchronous. When called, it adds the record to a buffer of pending record sends and immediately returns
• The producer maintains buffers of unsent records for each partition. These buffers are of a size specified by the batch.size config
• If the buffer is full or metadata is not available, the send() method blocks for max.block.ms and throws a TimeoutException after that
• Message Reliability (Typical Scenario)
  • Producer Config

    # Producers won't get acknowledgement until all the in-sync replicas are updated
    acks = all
    
    # Default value. With idempotence enabled, there is no risk of duplicates
    retries = Integer.MAX_VALUE 
    
    # To get exacly once delivery symantic
    enable.idempotence = true
    
    # Default value. With idempotence enabled, there won't be any duplicate and it cannot be more than 5
    max.in.flight.requests.per.connection = 5
    
    # Default value. Should be >= request.timeout.ms and linger.ms. An upper bound on the time to report success or failure after a call to send()
    # Retries will stop after this duration
    delivery.timeout.ms = 120000
    

    Broker / Topic Config

    default.replication.factor = 3
    
    # Consumers won't get message until the message is committed i.e. all in-sync replicas are updated
    # If 2 ISR are not available, the producer will throw an exception (either NotEnoughReplicas or NotEnoughReplicasAfterAppend)
    min.insync.replicas = 2
    
    # Graceful shutdown will do the leadership transfer of the partitions hosted in the broker before shutdown
    controlled.shutdown.enable = true
    
    # In the absence of ISR, no partition leader will be elected
    # Prefering data consistency over availability
    unclean.leader.election.enable = false
    

• Mandatory Parameters
  • key.serializer
  • value.serializer
  • bootstrap.servers - List host:port of brokers. All brokers need not be present and the producer will discover about other brokers from metadata. However, more than one broker should be specified so that the producer can connect even in the event of a broker failure
• acks=0
  • The producer will not wait for reply from the broker
  • Message loss is possible
  • Throughput is high as the producer doesn't have to wait for response from the broker
• acks=1
  • The producer will receive a response the moment the leader replica receives the message
  • If the message can't be written to the leader, the producer will receive an error and will retry automatically
  • Message loss is possible in the event of unclean leader election (by default disabled)
  • If the message is sent synchronously, the latency will be high
  • If the message is sent asynchronously, the latency will be hidden but the throughput depends on the number of allowed inflight messages before receiving a response from the server)
• acks=all
  • The producer will receive a success response after all the in-sync replicas received the message
  • Message is durable
  • Latency is high
• buffer.memory - The memory buffer that will store the messages before being sent out to a broker. If the buffer runs out of space, the thread will remain blocked for max.block.ms before throwing an exception. This setting should correspond roughly to the total memory the producer will use, but is not a hard bound since not all memory the producer uses is used for buffering. Some additional memory will be used for compression (if compression is enabled) as well as for maintaining in-flight requests
• Requests sent to brokers will contain multiple batches, one for each partition with data available to be sent
• max.request.size - The maximum size of a request in bytes. This setting will limit the number of record batches (batch size is specified by batch.size) the producer will send in a single request to avoid sending huge requests. This is also effectively a cap on the maximum record batch size. It should not be larger than the broker setting message.max.bytes or topic setting max.message.bytes
• compression.type - By default messages are uncompressed. Supported compression algorithms - gzip, snappy, lz4 and zstd
• client.id - This can be any string, and will be used by the brokers to identify messages sent from the client. It is used in logging and metrics, and for quotas
• In 2.4.0 release, the DefaultPartitioner now uses a sticky partitioning strategy. This means that records for specific topic with null keys and no assigned partition will be sent to the same partition until the batch is ready to be sent. When a new batch is created, a new partition is chosen. This decreases latency to produce, but it may result in uneven distribution of records across partitions in edge cases
• Tuning for throughput
  • acks = 1 (default value) - The leader broker will send the acknowledge as soon as it has writtent the record in int local log. The acknowledgement will not wait for the replicas to write the record in their logs. The tradeoff is less durability.
  • compression.type = lz4
  • batch.size - The records for a given partition are usually sent in batches to amortize the network roundtrip, improve compression ratio and reduce the load of processing messages in producer and broker. The batch will not be sent to the broker until the batch.size is full or linger.ms is expired. The tradeoff is higher latency
  • linger.ms - The producer will wait for linger.ms duration before sending the batch to the broker unless the batch.size is full
• Tuning for latency
  • linger.ms = 0 (default) - The producer will send the message as soon as it has some data to send. Batching will still happen if more messages are available at the time of sending
• With enable.idempotence = true, Kafka can keep the message ordring in a partitioning even with max.in.flight.requests.per.connection = 5
• In the absence of enable.idempotence = true, if max.in.flight.requests.per.connection is more than 1 and retries is also enabled, the message order can change if the first message fails and the second message succeeds

Kafka Consumers

• Transparently handles the failure of Kafka brokers
• Transparently adapts as topic partitions it fetches migrate within the cluster
• Consumer is NOT thread-safe
• If all the consumer instances have the same consumer group, then the records will effectively be load balanced over the consumer instances
• Each record will be broadcast to all the consumer groups
• Each consumer instance in a consumer group is the exclusive consumer of a "fair share" of partitions at any point in time
• session.timeout.ms defines how long the coordinator waits after the member’s last heartbeat before it assuming the member failed
  • With a low value, a network jitter or a long garbage collection (GC) might fail the liveness check, causing the group coordinator to remove this member and begin rebalancing
  • With a longer value, there will be a longer partial unavailability when a consumer actually fails
• 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
• 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
• Mandatory Parameters -
  • key.deserializer
  • value.deserializer
  • bootstrap.servers
  • group.id - A unique string that identifies the consumer group this consumer belongs to. This property is required if the consumer uses either the group management functionality by using subscribe(topic) or the Kafka-based offset management strategy
  • heartbeat.interval.ms - The expected time between heartbeats to the consumer coordinator when using Kafka's group management facilities. Heartbeats are used to ensure that the consumer's session stays active and to facilitate rebalancing when new consumers join or leave the group. The value must be set lower than session.timeout.ms, but typically should be set no higher than 1/3 of that value. It can be adjusted even lower to control the expected time for normal rebalances
  • max.partition.fetch.bytes - The maximum amount of data per-partition the server will return. Records are fetched in batches by the consumer. The value should be as large as message.max.bytes (broker config) or max.message.bytes (topic config)
  • fetch.max.bytes - The maximum amount of data the server should return for a fetch request
  • session.timeout.ms - The timeout used to detect client failures when using Kafka's group management facility. The client sends periodic heartbeats to indicate its liveness to the broker. If no heartbeats are received by the broker before the expiration of this session timeout, then the broker will remove this client from the group and initiate a rebalance. Note that the value must be in the allowable range as configured in the broker configuration by group.min.session.timeout.ms and group.max.session.timeout.ms
  • allow.auto.create.topics - Should be set to false in production as there is no way to validate topic name
  • auto.offset.reset - What to do when there is no initial offset in Kafka or if the current offset does not exist any more on the server (e.g. because that data has been deleted). Default value is latest
  • max.poll.interval.ms - The maximum delay between invocations of poll() when using consumer group management. This places an upper bound on the amount of time that the consumer can be idle before fetching more records. If poll() is not called before expiration of this timeout, then the consumer is considered failed and the group will rebalance in order to reassign the partitions to another member. For consumers using a non-null group.instance.id which reach this timeout, partitions will not be immediately reassigned. Instead, the consumer will stop sending heartbeats and partitions will be reassigned after expiration of session.timeout.ms
  • enable.auto.commit - If true the consumer's offset will be periodically committed in the background. Default value is true and must be set to false in production
• Handling polls for unpredictable message processing time
  • Move message processing to another thread
  • Allow the consumer to continue calling poll while the processor is still working
  • Some care must be taken to ensure that committed offsets do not get ahead of the actual position
  • Typically, you must disable automatic commits and manually commit processed offsets for records only after the thread has finished handling them
  • Note also that you will need to pause the partition so that no new records are received from poll until after thread has finished handling those previously returned
• Setting enable.auto.commit means that offsets are committed automatically with a frequency controlled by the config auto.commit.interval.ms giving us "at most once" i.e. commiting before the message processing is complete
• Users can also control when records should be considered as consumed and hence commit their offsets. We will manually commit the offsets only after the corresponding records have been inserted into the database. This gives us "at least once" delivery semantic where the message will be delivered once, but in failure cases, it could be possibly delivered twice
• The consumer application need not use Kafka's built-in offset storage, it can store offsets in a store of its own choosing. The primary use case for this is allowing the application to store both the offset and the results of the consumption in the same system in a way that both the results and offsets are stored atomically. This will give us "exactly-once" semantics
• Kafka consumer stores the last offset read in a special Kafka topic
• Tuning for throughput
  • fetch.min.bytes - The broker won't send messages to the consumer, until this much volume of data is available or fetch.max.wait.ms duration is over. It reduces the consumer load for processing a message. The tradeoff is latency
  • fetch.max.wait.ms
• Tuning for latency
  • fetch.min.bytes = 1 (default value) - The broker will send message as soon as there is at least 1 byte data available
• Tuning for durability
  • enable.auto.commit = false

Cross Data Center Replication

• The proprietary Confluent Replicator and the open source MirrorMaker are the two options available
• Mirror maker doesn't support all the advanced options like preventing infinite loop for active-active replication, replication of configuration changes, partition changes etc.
• Replication to the In-sync replicas in the local data center are done synchrnously, whereas the replication to the remote data center is done asynchronously
• In either option, offset topic (__consumer_offsets) is never replicated because the offset of the same message may differ across clusters due to various reasons
• However, the message creation timestamp is copied across clusters and therefore the timestamps can be used to find the offset in a given cluster
• Replication of topics and logs from one cluster to another can fall into an infinite loop unless one of the following broad strategies are taken
  • Same topic name in both clusters - With provenance.header.enable=true, replicator puts provenance information in the message header. If the cluster has the same details as the provenance information, the message is not copied breaking the loop
  • Different topic names in each cluster - A cluster name is usually appended or prepended with the topic name. E.g. topic1_C1 in C! is copied to C2 as is. Also, topic1_C2 is created in C2 and copied to C1
• Same topic name in both clusters
  • Pros
    • The consumer offsets are translated automatically using the __consumer_timestamps provided the consumer interceptor is enabled a consumer is not already running in the cluster
  • Cons
    • If producers produce in both clusters, there is no "global ordering" for messages
    • If consumers consume from both clusters, the same message will be re-processed
• Different topic names in each cluster
  • Pros
    • Producer and consumers can run in both clusters at the same time without reprocessing any message
  • Cons
    • An extra topic needs to be created in each cluster and the original topic name needs to prefixed or suffixed with cluster name to get the actual topic name
    • When falling back to the remote cluster in the event of a local cluster failure, the consumer offset need to be manually set by searching the offset in the remote cluster based on timestamp

Kafka Streams

• Kafka Streams applications do not run inside the Kafka brokers (servers) or the Kafka cluster – they are client-side applications. Kafka Streams is a library that is embedded and run within the user client application
• The unit of parallelism in Kafka Streams is task. But, the number of tasks is dependent on threads and input topic partitions
• One or more partitions get exclusively assigned to a task, and one or more tasks get exclusively assigned to a thread
• The maximum limit of tasks across all the application instances is the maximum number of input topic partitions
• Similarly the maximum limit of threads across all application instances is the maximum number of tasks possible
• Assigning more threads of application instances will keep the extra threads and instances idle, but they can take over tasks if one or more instances die
• Some stream processing applications don’t require state – they are stateless – which means the processing of a message is independent from the processing of other messages. Examples are when you only need to transform one message at a time, or filter out messages based on some condition
• In practice, however, most applications require state – they are stateful – in order to work correctly, and this state must be managed in a fault-tolerant manner. Your application is stateful whenever, for example, it needs to join, aggregate, or window its input data
• Kafka Streams uses RocksDB (other DBs are also pluggable) to store local states
• Local state in an application instance as well as remote states in other instances of the application can be queried from within the application. However, the data will be read-only
• For fault-tolerance of the state-store, an internal compacted changelog topic is used
• The changelog topic is also partitioned so that each task can exclusively be assigned a fair share of partitions
• The state store sends changes to the changelog topic in a batch, either when a default batch size has been reached or when the commit interval is reached
• If a machine, which runs tasks, fails and the tasks are restarted on a different machine, this internal changelog topic is replayed on the state store of the restarted task to restore its state
• To minimize this restoration time, users can configure their applications to have standby replicas of local states (i.e. fully replicated copies of the state) in other application instances
• When a task migration happens, Kafka Streams attempts to assign a task to an application instance where such a standby replica already exists
• Kafka Streams provides two APIs
  • Kafka Streams DSL
    • A high level API
    • Provides the most common data transformation operations such as map, filter, join, and aggregations out of the box
    • Built on top of the Streams Processor API
    • Provides built-in abstractions for streams and tables in the form of KStream, KTable, and GlobalKTable. A topic can be processed in the form of KStream, KTable, and GlobalKTable
  • Processor API
    • A low level API
    • Provides more flexibility than the DSL but at the expense of requiring more manual work
• KTable & GlobalKTable contain the latest value of each key (similar to a database table) and each record represents an UPDATE
• KStream provides all values for a given key and exh record represents an INSERT
• Unlike KTables, GlobalKTables load all partitions of the input topic within each application instance. Thus
  • GlobalKTables does not need to be co-partitioned with the input data
  • Input data can be joined with the GlobalKTable on any field (not necessarily the key)
  • GlobalKTable has more storage requirement that KTable as it loads all partitions in each app instance
  • GlobalKTables are good for broadcasting lookup data to all instances (a.k.a. star joins)
  • You must provide a name for the table (more precisely, for the internal state store that backs the table). This is required for supporting interactive queries against the table
• KTables
  • the local KTable instance of every application instance will be populated with data from only a subset of the partitions of the input topic. Collectively, across all application instances, all input topic partitions are read and processed
  • You must provide a name for the table (more precisely, for the internal state store that backs the table). This is required for supporting interactive queries against the table
• The computational logic of a Kafka Streams application is defined as a processor topology, which is a graph of stream processors (nodes) and streams (edges)
• The steps of writing a stream processing application:
  • Specify one or more input streams that are read from Kafka topics
  • Compose transformations on these streams
  • Write the resulting output streams back to Kafka topics, or expose the processing results of your application directly to other applications through interactive queries (e.g., via a REST API)
• The KStream and KTable interfaces support a variety of transformation operations. Each of these operations can be translated into one or more connected processors into the underlying processor topology
• Some KStream transformations may generate
  • one or more KStream objects, for example: - filter and map on a KStream will generate another KStream - branch on KStream can generate multiple KStreams
  • a KTable object, for example an aggregation of a KStream also yields a KTable
• All KTable transformation operations can only generate another KTable. However, the Kafka Streams DSL does provide a special function that converts a KTable representation into a KStream
• All of these KStream and KTable transformation methods can be chained together to compose a complex processor topology
• Interactive Queries - Allows an external application query the state of Kafka stream application instances. For e.g. a KTable local store in a given application instance does not contain the complete data - it contains the data of a subset of all partitions. If an external application queries the info of a given key and the query is routed through a load balancer, it can land in any of the app instances. Now, interactive queries will allow the application instance serve that request even if the data is present in the data store of some other app instance
• Interactive queries work in the following way:
  • The app developer needs to provide an endpoint in the Kafka stream application instance to query the locat data store
  • The app developer needs to configure the host:port pair of the endpoint in the property application.server. Each instance will have its own endpoint details in thi property
  • Kafka Streams keep track of this information by piggybacking on the consumer group membership protocol. Thus every app instance will discover the endpoint host:port details of every other app instance
  • Kafka streams provide APIs to query this endpoint metadata which allows an app instance determine the details of the app instance that holds the value of a given key
  • The app instance serving the request of the external app can now use the host:port details obtained from the metadata to query the appropriate app instance for the data and return the same to the external app
• There are two main differences between non-windowed and windowed aggregation with regard to key-design
  • For window aggregation the key is <K,W>, i.e., for each window a new key is used
  • Instead of using a single instance, Streams uses multiple instances (called “segments”) for different time periods
• After the window retention time has passed old segments can be dropped. Thus, RocksDB memory requirement does not grow infinitely
• Stateless transformations – Branch, Filter, Inverse Filter, FlatMap, ForEach, GroupByKey, GroupBy, Map, Peek, Print, SelectKey, Table To Stream
• Join operands - KStream-to-Kstream, KTable-to-KTable, Kstream-to-Ktable, KStream-to-GlobalKTable
• KStream-KStream join is always windowed join
• Outer join is supported only for KStream-to-KStream & KTable-to-KTable
• No join is supported for KTable-to-GlobalKTable
• Windowed joins are not supported for KStream-to-KTable, KTable-to-KTable, Kstream-to-GlobalKTable
• Input data must be co-partitioned when joining. This ensures that input records with the same key, from both sides of the join, are delivered to the same stream task during processing. It is the responsibility of the user to ensure data co-partitioning when joining
• The requirements for data co-partitioning are:
  • The input topics of the join (left side and right side) must have the same number of partitions.
  • All applications that write to the input topics must have the same partitioning strategy so that records with the same key are delivered to same partition number
• GlobalKTables do not require co-partitioning
• Kafka streams cannot verify the co-partitioning requirement for partition strategy
• Kafka streams throws TopologyBuilderException if the number of partitions on both sides of the join are not same
• Stream joining windows
  • Tumbling time window - Fixed-size, non-overlapping, gap-less windows
  • Hopping time window - Hopping time windows are windows based on time intervals. They model fixed-sized, (possibly) overlapping windows. A hopping window is defined by two properties: the window’s size and its advance interval (aka “hop”). The advance interval specifies by how much a window moves forward relative to the previous one
  • Sliding time window - A sliding window models a fixed-size window that slides continuously over the time axis; here, two data records are said to be included in the same window if (in the case of symmetric windows) the difference of their timestamps is within the window size. Thus, sliding windows are not aligned to the epoch, but to the data record timestamps
  • Session window
• It is generally preferable to use mapValues() and flatMapValues() as they ensure the key has not been modified and thus, the repartitioning step can be omitted
• Kafka Streams inserts a repartitioning step if a key-based operation like aggregation or join is preceded by a key changing operation like selectKey(), map(), or flatMap()
• Joining with a KStream will always yield a KStream
• Any streams and tables may be (continuously) written back to a Kafka topic with the method to()
• You may also leverage the Processor API from the DSL
• StreamsConfig.APPLICATION_ID_CONFIG (application.id) - It is a mandatory parameter and is used to derive
  • Consumer group id
  • Internal topic name prefix
  • Client id prefix
• ConsumerConfig.AUTO_OFFSET_RESET_CONFIG (auto.offset.reset) -
  • It's applicable when the consumer group doesn't have any offset associated in Kafka
  • The possible values are - earliest (read the messages from the begining), latest (read the new messages)
• num.stream.threads - The number of threads per streams app instance

Kafka Connect

• Kafka Connect is a tool for scalably and reliably streaming data between Apache Kafka and other systems
• Connectors can be configured with transformations to make lightweight message-at-a-time modifications
• To copy data between Kafka and another system, users create a Connector for the system they want to pull data from or push data to
• Connectors come in two flavors: SourceConnectors import data from another system (e.g. JDBCSourceConnector would import a relational database into Kafka) and SinkConnectors export data (e.g. HDFSSinkConnector would export the contents of a Kafka topic to an HDFS file)
• Connectors do not perform any data copying themselves: their configuration describes the data to be copied, and the Connector is responsible for breaking that job into a set of Tasks that can be distributed to workers
• Tasks also come in two corresponding flavors: SourceTask and SinkTask
• With an assignment in hand, each Task must copy its subset of the data to or from Kafka

Schema Registry

• Schema Registry defines a scope in which schemas can evolve, and that scope is the subject. The name of the subject depends on the configured subject name strategy, which by default is set to derive subject name from topic name
• KafkaAvroSerializer and KafkaAvroDeserializer default to using -Key and -value as the corresponding subject name while registering or retrieving the schema
• The default behavior can be modified using the following properties:
  • key.subject.name.strategy - Determines how to construct the subject name under which the key schema is registered with the Schema Registry
  • value.subject.name.strategy - Determines how to construct the subject name under which the value schema is registered with Schema Registry
• Integration with Schema Registry means that Kafka messages do not need to be written with the entire Avro schema. Instead, Kafka messages are written with the schema id. The producers writing the messages and the consumers reading the messages must be using the same Schema Registry to get the same mapping between a schema and schema id
• A producer sends the new schema for Payments to Schema Registry. Schema Registry registers this schema Payments to the subject transactions-value, and returns the schema id of 1 to the producer. The producer caches this mapping between the schema and schema id for subsequent message writes, so it only contacts Schema Registry on the first schema write
• When a consumer reads this data, it sees the Avro schema id of 1 and sends a schema request to Schema Registry. Schema Registry retrieves the schema associated to schema id 1, and returns the schema to the consumer. The consumer caches this mapping between the schema and schema id for subsequent message reads, so it only contacts Schema Registry the on first schema id read
• Best practice is to register schemas outside of the client application to control when schemas are registered with Schema Registry and how they evolve.
• Disable automatic schema registration by setting the configuration parameter auto.register.schemas=false, as shown in the example below

  props.put(AbstractKafkaAvroSerDeConfig.AUTO_REGISTER_SCHEMAS, false);
  

• Kafka is used as Schema Registry storage backend

KSQL

• The DDL statements (Imperative verbs that define metadata on the KSQL server by adding, changing, or deleting streams and tables) include:
  • CREATE STREAM
  • CREATE TABLE
  • DROP STREAM
  • DROP TABLE
  • CREATE STREAM AS SELECT (CSAS)
  • CREATE TABLE AS SELECT (CTAS)
• The DML statements (Declarative verbs that read and modify data in KSQL streams and tables. Data Manipulation Language statements modify data only and don’t change metadata) include:
  • SELECT
  • INSERT INTO
  • CREATE STREAM AS SELECT (CSAS)
  • CREATE TABLE AS SELECT (CTAS)
• The CSAS and CTAS statements occupy both categories, because they perform both a metadata change, like adding a stream, and they manipulate data, by creating a derivative of existing records
• In headless mode, KSQL stores metadata in the config topic
• In interactive mode, KSQL stores metatada in and builds metadata from the KSQL command topic. To secure the metadata, you must secure the command topic
• SHOW STREAMS and EXPLAIN statements run against the KSQL server that the KSQL client is connected to. They don’t communicate directly with Kafka
• CREATE STREAM WITH and CREATE TABLE WITH write metadata to the KSQL command topic
• Non-persistent queries based on SELECT that are stateless only read from Kafka topics, for example SELECT … FROM foo WHERE ….
• Non-persistent queries that are stateful read and write to Kafka, for example, COUNT and JOIN. The data in Kafka is deleted automatically when you terminate the query with CTRL-C

Kafka CLI

Start Zookeeper with default configuration

bin\windows\zookeeper-server-start.bat config\zookeeper.properties

Start Kafka broker with default configuration

bin\windows\kafka-server-start.bat config\server.properties

Create a new topic

bin\windows\kafka-topics.bat ^
    --create ^
    --bootstrap-server localhost:9092 ^
    --replication-factor 1 ^
    --partitions 2 ^
    --topic word-count-input

Delete a topic

• If the flag delete.topic.enable is not enabled, the following command will be ignored
• The process is asynchronous and hence the topic won't be deleted immediately

bin\windows\kafka-topics.bat ^
    --delete ^
    --bootstrap-server localhost:9092 ^
    --topic word-count-input

Adding partitions

• Key to partition mapping will change if the number of partitions is changed
• It is not advisable to add partitions to topics that have keys
• Deletion of partition is not possible

bin\windows\kafka-topics.bat ^
    --alter ^
    --bootstrap-server localhost:9092 ^
    --topic word-count-input ^
    --partitions 16 ^

List topics

bin\windows\kafka-topics.bat ^
    --list ^
    --bootstrap-server localhost:9092

Describe a topic

The output includes - partition count, topic configuration overrides, linting of each partition and its replica assignments

bin\windows\kafka-topics.bat ^
    --describe ^
    --bootstrap-server localhost:9092 ^
    --topics-with-overrides ^       REM Display topics having configurtion overrides
    --under-replicated-partitions ^ REM Display partitions with one or more out-of-sync replicas
    --unavailable-partitions ^      REM Display partitions without a leader
    --topic mytopic

List consumer groups

bin\windows\kafka-consumer-groups.bat ^
    --bootstrap-server localhost:9092 ^
    --list

Describe consumer group

Among other things following fields are displayed

• CURRENT-OFFSET - The next offset to be consumed by the consumer group
• LOG-END-OFFSET - The current high-water mark offset from the broker for the topic partition. The offset of the next message to be produced to this partition
• LAG - The difference between the consumer Current-Offset and the broker Log-End-Offset for the topic partition

bin\windows\kafka-consumer-groups.bat ^
    --bootstrap-server localhost:9092 ^
    --describe ^
    --group testgroup ^
    --members ^ REM Lists active members
    --verbose   REM Gives partition assignments

Delete consumer group

bin\windows\kafka-consumer-groups.bat ^
    --bootstrap-server localhost:9092 ^
    --delete ^
    --group my-group ^
    --group my-other-group

Reset Offset

bin\windows\kafka-consumer-groups.bat ^
    --bootstrap-server localhost:9092 ^
    --reset-offsets ^
    --group consumergroup1 ^
    --topic topic1 ^
    --to-latest

Adding or decommissioning brokers

• The following command only generates the plan. However, it is not aware of partitions size, and neither can provide a plan to reduce the number of partitions to migrate from brokers to brokers. Therefore, edit the plan on your own
• Once the plan is ready, execute it with the option --execute
• Finally verify the status using --verify
• Throttling is important to ensure producers and consumers in the cluster are not impacted
• The throttle can be changed even when the rebalance is ongoing by rerunning the same command with the new throttle value
• --verify option removes the throttle

bin\windows\kafka-reassign-partitions.bat ^
    --zookeeper localhost:2181 ^
    --topics-to-move-json-file topics-to-move.json ^
    --broker-list "5,6" ^
    --generate

bin\windows\kafka-reassign-partitions.bat ^
    --zookeeper localhost:2181 ^
    --reassignment-json-file expand-cluster-reassignment.json ^ REM JSON file content is the output of --generate command
    --throttle 50000000 ^ REM 50MB/sec
    --execute

Update dynamic config

• The following command updated sets the parameter log.cleaner.thread for broker id 0

bin\windows\kafka-configs.bat ^
    --bootstrap-server localhost:9092 ^
    --entity-type brokers ^
    --entity-name 0 ^
    --alter ^
    --add-config log.cleaner.threads=2

Describe dynamic config

bin\windows\kafka-configs.bat ^
    --bootstrap-server localhost:9092 ^
    --entity-type brokers ^
    --entity-name 0 ^
    --describe

Delete dynamic config

bin\windows\kafka-configs.bat ^
    --bootstrap-server localhost:9092 ^
    --entity-type brokers ^
    --entity-name 0 ^
    --alter ^
    --delete-config log.cleaner.threads

Update cluster-wide default dynamic config

bin\windows\kafka-configs.bat ^
    --bootstrap-server localhost:9092 ^
    --entity-type brokers ^
    --entity-default ^
    --alter ^
    --add-config log.cleaner.threads=2

Console producer

bin\windows\kafka-console-producer.bat ^
    --broker-list localhost:9092 ^
    --topic word-count-input

Console consumer

bin\windows\kafka-console-consumer.bat ^
    --bootstrap-server localhost:9092 ^
    --topic word-count-output ^
    --from-beginning ^
    --formatter kafka.tools.DefaultMessageFormatter ^
    --property print.key=true ^
    --property print.value=true ^
    --property key.deserializer=org.apache.kafka.common.serialization.StringDeserializer ^
    --property value.deserializer=org.apache.kafka.common.serialization.LongDeserializer

Miscellaneous

Production Deployment

• Java 8 with G1 Collector
• On AWS, for lower latency I/O optimized instances will be good
• Extents File System (XFS) perform well for Kafka Workload
• The mount points should have noatime option set to eliminate the overhead of updating access time
• export KAFKA_JVM_PERFORMANCE_OPTS="-server -XX:+UseG1GC -XX:MaxGCPauseMillis=20 -XX:InitiatingHeapOccupancyPercent=35 -XX:+DisableExplicitGC-Djava.awt.headless=true"
• vm.swappiness = 1 - A low value means the kernel will try to avoid swapping as much as possible making less memory available for page cache
• vm.dirty_background_ratio = 5 (default - 10) - The percentage of system memory which when dirty, system will start writing to disk
• vm.dirty_ratio = 60 (default - 15) - The percentage of memory which when dirty, the process doing writes would block and write out dirty pages to the disks
• Tuning vm.dirty_background_ratio and vm.dirty_ratio as above relies on:
  • Good disk I/O performance (SSD or RAID)
  • Replication is used in the cluster to guard against system failure
• net.core.wmem_default = 131072 (128 KiB)
• net.core.rmem_default = 131072 (128 KiB)
• net.core.wmem_max = 131072 (128 KiB)
• net.core.rmem_max = 2097152 (2 MiB)
• net.ipv4.tcp_wmem = 4096 65536 2048000 (4 KiB 64 KiB 2 MiB)
• net.ipv4.tcp_rmem = 4096 65536 2048000 (4 KiB 64 KiB 2 MiB)
• vm.max_map_count = 65535 - Maximum number of memory map areas a process may have (aka vm.max_map_count). By default, on a number of Linux systems, the value of vm.max_map_count is somewhere around 65535. Each log segment, allocated per partition, requires a pair of index/timeindex files, and each of these files consumes 1 map area. In other words, each log segment uses 2 map areas. Thus, each partition requires minimum 2 map areas, as long as it hosts a single log segment. That is to say, creating 50000 partitions on a broker will result allocation of 100000 map areas and likely cause broker crash with OutOfMemoryError (Map failed) on a system with default vm.max_map_count
• File descriptor limits: Kafka uses file descriptors for log segments and open connections. If a broker hosts many partitions, consider that the broker needs at least (number_of_partitions)*(partition_size/segment_size) to track all log segments in addition to the number of connections the broker makes. We recommend at least 100,000 allowed file descriptors for the broker processes as a starting point
• Kafka uses heap space very carefully and does not require setting heap sizes more than 6 GB
• You can do a back-of-the-envelope estimate of memory needs by assuming you want to be able to buffer for 30 seconds and compute your memory need as write_throughput * 30
• A machine with 64 GB of RAM is a decent choice, but 32 GB machines are not uncommon. Less than 32 GB tends to be counterproductive
• Do not share the same drives used for Kafka data with application logs or other OS filesystem activity to ensure good latency
• You should use RAID 10 if the additional cost is acceptable. Otherwise, configure your Kafka server with multiple log directories, each directory mounted on a separate drive
• You should avoid network-attached storage (NAS). NAS is often slower, displays larger latencies with a wider deviation in average latency, and is a single point of failure
• Modern data-center networking (1 GbE, 10 GbE) is sufficient for the vast majority of clusters.
• You should avoid clusters that span multiple data centers, even if the data centers are colocated in close proximity; and avoid clusters that span large geographic distances
• num.partitions - The default number of log partitions for auto-created topics. You should increase this since it is better to over-partition a topic. Over-partitioning a topic leads to better data balancing and aids consumer parallelism. For keyed data, you should avoid changing the number of partitions in a topic
• Key Service Goals
  • Throughput
  • Latency
  • Durability
  • Availability
• Partitions are the unit of parallelism. On both the producer and the broker side, writes to different partitions can be done fully in parallel. Therefore, in general, more partitions lead to higher throughput. However, following tradeoffs need to be considered
  • More partitions need more open file handles (3 per segment - the segment itself, offset index, time index)
  • More partitions need more memory map areas (2 per segment - the offset index, time index)
  • More partitions may increase unavailability - In the event of broker failure, the partitions for which the broker is a leader may remain unavailable for sometime as the partition leader need to be elected by modifying the Zookeeper metadata sequentially for each partition. If the failed broker happens to be the controller, it would mean a further downtime for the partitions as a new broker elected as the new controller needs to read the metadata from Zookeeper for each partition in the cluster. A total downtime of a few seconds for the impacted partitions are expected Post release 1.1.0 with the use of Zookeeper async APIs and batching request to other brokers during failover, things have improved here
  • More partitions may increase latency as a limited number of threads in a given broker will do replication in the broker for all the partitions in the cluster. Unless the in-sync replicas are all updated (committed), the message will not be available to the consumers
  • More partitions means more memory requirements for producer as each producer thread will have some buffer for each partiton
• As a rule of thumb, we recommend each broker to have up to 4,000 partitions and each cluster to have up to 200,000 partitions

Key Metrics

• Consumer Metrics
  • MaxLag - The number of messages the consumer lags behind the producer
  • MinFetchRate - If the MinFetchRate of the consumer drops to almost 0, the consumer is likely to have stopped. If the MinFetchRate is non-zero and relatively constant, but the consumer lag is increasing, it indicates that the consumer is slower than the producer. If so, the typical solution is to increase the degree of parallelism in the consumer. This may require increasing the number of partitions of a topic
• Broker Metrics
  • UnderReplicatedPartitions - Number of under-replicated partitions (| ISR | < | all replicas |). Alert if value is greater than 0
  • OfflinePartitionsCount - Number of partitions that don’t have an active leader and are hence not writable or readable. Alert if value is greater than 0
  • ActiveControllerCount - Number of active controllers in the cluster. Alert if the aggregated sum across all brokers in the cluster is anything other than 1 because there should be exactly one controller per cluster
  • MinFetchRate - Rate of fetching messages from the leader by the replicas
  • MaxLag - The number of messages the replica lags behind the leader

Deploying on AWS

• EBS st1 for log.dirs
• d2.xlarge if you’re using instance storage, or r4.xlarge if you’re using EBS
• Kafka was designed to run within a single data center. As such, we discourage distributing brokers in a single cluster across multiple regions. However, we recommend “stretching” brokers in a single cluster across availability zones within the same region
• Kafka 0.10 supports rack awareness, which makes spreading replicas across availability zones much easier to configure

ZooKeeper Basics

• A Zookeeper cluster is called an ensemble
• Due to the algorithm used, it is recommended that ensembles contain an odd number of servers (e.g., 3, 5, etc.)
• A three-node ensemble can run with one node missing
• A five-node ensemble can run with two nodes missing
• It's a good idea to run Zookeeper in a five-node ensemble so that the ensemble can run even when one node goes down while another node is taken down due to configuration change etc.
• ZooKeeper parameters
  • initLimit - the amount of time to allow the followers to connect with a leader
  • syncLimit - Limits how out-of-sync followers can be with the leader
  • tickTime - Both values are a number of tickTime units
• In the following example, the initLimit is 20 * 2000 ms or 40 seconds

  tickTime=2000
  initLimit=20
  syncLimit=5
  

Avro Basics

• BACKWARD: consumer using schema X can process data produced with schema X or X-1
• BACKWARD_TRANSITIVE: consumer using schema X can process data produced with schema X, X-1, or X-2
• FORWARD: data produced using schema X can be ready by consumers with schema X or X-1
• FORWARD_TRANSITIVE: data produced using schema X can be ready by consumers with schema X, X-1, or X-2
• FULL: backward and forward compatible between schemas X and X-1
• FULL_TRANSITIVE: backward and forward compatible between schemas X, X-1, and X-2
• Allowed operations for FULL & FULL_TRANSITIVE
  • Add optional fields
  • Delete optional fields
• Allowed operations for BACKWARD & BACKWARD_TRANSITIVE
  • Add optional fields
  • Delete fields
• Allowed operations for FORWARD & FORWARD_TRANSITIVE
  • Add fields
  • Delete optional fields
• Order of upgrading clients
  • BACKWARD or BACKWARD_TRANSITIVE: there is no assurance that consumers using older schemas can read data produced using the new schema. Therefore, upgrade all consumers before you start producing new events.
  • FORWARD or FORWARD_TRANSITIVE: there is no assurance that consumers using the new schema can read data produced using older schemas. Therefore, first upgrade all producers to using the new schema and make sure the data already produced using the older schemas are not available to consumers, then upgrade the consumers.
  • FULL or FULL_TRANSITIVE: there are assurances that consumers using older schemas can read data produced using the new schema and that consumers using the new schema can read data produced using older schemas. Therefore, you can upgrade the producers and consumers independently.
• Primitive data types –
  • null, boolean, int, long, float, double, bytes, string
• Complex data types –
  • record, enum, array, map, union, fixed
• Record attributes –
  • name, namespace, doc, aliases, type, fields
• Enum attributes
  • name, namespace, aliases, doc, symbols
• Enums once defined cannot be changed. Otherwise, the compatibility will break
• Arrays are a way to define an unbounded list of items

{"type": "array", "items": "string"}

• Unions can allow a field value to take different types. One common use case is to define an optional value:

{"name": "middle_name", "type": {"null", "string"}, "default": null}

• Maps are a way to define a list of keys and values, where the keys are strings

{"type": "map", "values": "string"}

Zookeeper Shell

bin\windows\zookeeper-shell.bat localhost:2181

REM List brokers
ls /brokers/topics

REM List topics
ls /brokers/topics

REM List partitions
ls /brokers/topics/<topic_name>/partitions

REM List dynamic config
ls /config/topics/<topic_name>

REM Find the controller broker
get /controller

REM Find the partition assignments to brokers
get /brokers/topics/<topic_name>

REM Find broker listener & port details
get /brokers/ids/<broker_id>

REM Find partition leader and ISR
get /brokers/topics/<topic_name>/partitions/<partition_id>/state

REM Find all replica locations
get /brokers/topics/<topic_name>

Operations & Troubleshooting

• Logging -
  • server.log
  • controller.log - ERROR, FATAL & WARN messages need to be looked at
  • state-change.log
• Rolling Restart
  • Run the brokers with controlled.shutdown.enable=true
  • Ensure the server is healthy with no under replicated partition
  • Identify the Controller broker. It should be the last one to shutdown
  • Login to one broker server and run bin/kafka-server-stop
  • Restart the server using bin/kafka-server-start etc/kafka/server.properties and wait until it catches up before stopping the next broker

References

Books

• Narkhede, Neha. Kafka: The Definitive Guide . O'Reilly Media. Kindle Edition
• Kleppmann, Martin. Making Sense of Stream Processing: The Philosophy Behind Apache Kafka and Scalable Stream Data Platforms . O'Reilly

White Paper

• Byzek, Yeva. Optimizing Your Apache Kafka Deployment: Levers for Throughput, Latency, Durability, and Availability

Useful Blogs on Operational Stuff

• https://engineering.linkedin.com/kafka/benchmarking-apache-kafka-2-million-writes-second-three-cheap-machines
• https://linux-kernel-labs.github.io/master/labs/memory_mapping.html
• https://www.confluent.io/blog/how-choose-number-topics-partitions-kafka-cluster/
• https://www.confluent.io/blog/apache-kafka-supports-200k-partitions-per-cluster/
• https://www.confluent.io/blog/design-and-deployment-considerations-for-deploying-apache-kafka-on-aws/
• https://labs.tabmo.io/rebalancing-kafkas-partitions-803918d8d244
• https://www.confluent.io/blog/transactions-apache-kafka/
• https://www.confluent.io/white-paper/disaster-recovery-for-multi-datacenter-apache-kafka-deployments/

Udemy Courses

• Udemy courses by https://www.udemy.com/user/stephane-maarek/

Others

• https://jira.apache.org/jira/projects/KAFKA/issues
• https://cwiki.apache.org/confluence/collector/pages.action?key=KAFKA
• https://kafka.apache.org/
• https://www.confluent.io/blog/