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All configurations are documented in the configuration section. A Production Server Config Here is an example production server configuration: # ZooKeeper zookeeper.connect=[list of ZooKeeper servers] # Log configuration num.partitions=8 default.replication.factor=3 log.dir=[List of directories. Kafka should have its own dedicated disk(s) or SSD(s).] # Other configurations broker.id=[An integer. Start with 0 and increment by 1 for each new broker.] listeners=[list of listeners] auto.create.topics.enable=false min.insync.replicas=2 queued.max.requests=[number of concurrent requests] Our client configuration varies a fair amount between different use cases. 6.6 Java Version Java 8 and Java 11 are supported. Java 11 performs significantly better if TLS is enabled, so it is highly recommended (it also includes a number of other performance improvements: G1GC, CRC32C, Compact Strings, Thread-Local Handshakes and more). From a security perspective, we recommend the latest released patch version as older freely available versions have disclosed security vulnerabilities. Typical arguments for running Kafka with OpenJDK-based Java implementations (including Oracle JDK) are: -Xmx6g -Xms6g -XX:MetaspaceSize=96m -XX:+UseG1GC -XX:MaxGCPauseMillis=20 -XX:InitiatingHeapOccupancyPercent=35 -XX:G1HeapRegionSize=16M -XX:MinMetaspaceFreeRatio=50 -XX:MaxMetaspaceFreeRatio=80 -XX:+ExplicitGCInvokesConcurrent For reference, here are the stats for one of LinkedIn's busiest clusters (at peak) that uses said Java arguments: 60 brokers 50k partitions (replication factor 2) 800k messages/sec in 300 MB/sec inbound, 1 GB/sec+ outbound All of the brokers in that cluster have a 90% GC pause time of about 21ms with less than 1 young GC per second. 6.7 Hardware and OS We are using dual quad-core Intel Xeon machines with 24GB of memory. You need sufficient memory to buffer active readers and writers. 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. The disk throughput is important. We have 8x7200 rpm SATA drives. In general disk throughput is the performance bottleneck, and more disks is better. Depending on how you configure flush behavior you may or may not benefit from more expensive disks (if you force flush often then higher RPM SAS drives may be better). OS Kafka should run well on any unix system and has been tested on Linux and Solaris. We have seen a few issues running on Windows and Windows is not currently a well supported platform though we would be happy to change that. It is unlikely to require much OS-level tuning, but there are three potentially important OS-level configurations: 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 100000 allowed file descriptors for the broker processes as a starting point. Note: The mmap() function adds an extra reference to the file associated with the file descriptor fildes which is not removed by a subsequent close() on that file descriptor. This reference is removed when there are no more mappings to the file. Max socket buffer size: can be increased to enable high-performance data transfer between data centers as described here. Maximum number of memory map areas a process may have (aka vm.max_map_count). See the Linux kernel documentation. You should keep an eye at this OS-level property when considering the maximum number of partitions a broker may have. 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. Keep in mind that the number of log segments per partition varies depending on the segment size, load intensity, retention policy and, generally, tends to be more than one. Disks and Filesystem We recommend using multiple drives to get good throughput and not sharing the same drives used for Kafka data with application logs or other OS filesystem activity to ensure good latency. You can either RAID these drives together into a single volume or format and mount each drive as its own directory. Since Kafka has replication the redundancy provided by RAID can also be provided at the application level. This choice has several tradeoffs. If you configure multiple data directories partitions will be assigned round-robin to data directories. Each partition will be entirely in one of the data directories. If data is not well balanced among partitions this can lead to load imbalance between disks. RAID can potentially do better at balancing load between disks (although it doesn't always seem to) because it balances load at a lower level. The primary downside of RAID is that it is usually a big performance hit for write throughput and reduces the available disk space. Another potential benefit of RAID is the ability to tolerate disk failures. However our experience has been that rebuilding the RAID array is so I/O intensive that it effectively disables the server, so this does not provide much real availability improvement. Application vs. OS Flush Management Kafka always immediately writes all data to the filesystem and supports the ability to configure the flush policy that controls when data is forced out of the OS cache and onto disk using the flush. This flush policy can be controlled to force data to disk after a period of time or after a certain number of messages has been written. There are several choices in this configuration. Kafka must eventually call fsync to know that data was flushed. When recovering from a crash for any log segment not known to be fsync'd Kafka will check the integrity of each message by checking its CRC and also rebuild the accompanying offset index file as part of the recovery process executed on startup. Note that durability in Kafka does not require syncing data to disk, as a failed node will always recover from its replicas. We recommend using the default flush settings which disable application fsync entirely. This means relying on the background flush done by the OS and Kafka's own background flush. This provides the best of all worlds for most uses: no knobs to tune, great throughput and latency, and full recovery guarantees. We generally feel that the guarantees provided by replication are stronger than sync to local disk, however the paranoid still may prefer having both and application level fsync policies are still supported. The drawback of using application level flush settings is that it is less efficient in its disk usage pattern (it gives the OS less leeway to re-order writes) and it can introduce latency as fsync in most Linux filesystems blocks writes to the file whereas the background flushing does much more granular page-level locking. In general you don't need to do any low-level tuning of the filesystem, but in the next few sections we will go over some of this in case it is useful. Understanding Linux OS Flush Behavior In Linux, data written to the filesystem is maintained in pagecache until it must be written out to disk (due to an application-level fsync or the OS's own flush policy). The flushing of data is done by a set of background threads called pdflush (or in post 2.6.32 kernels "flusher threads"). Pdflush has a configurable policy that controls how much dirty data can be maintained in cache and for how long before it must be written back to disk. This policy is described here. When Pdflush cannot keep up with the rate of data being written it will eventually cause the writing process to block incurring latency in the writes to slow down the accumulation of data. You can see the current state of OS memory usage by doing > cat /proc/meminfo The meaning of these values are described in the link above. Using pagecache 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 Filesystem Selection Kafka uses regular files on disk, and as such it has no hard dependency on a specific filesystem. The two filesystems which have the most usage, however, are EXT4 and XFS. Historically, EXT4 has had more usage, but recent improvements to the XFS filesystem have shown it to have better performance characteristics for Kafka's workload with no compromise in stability. 2b1af7f3a8