Zero code needed to be modified on the consumer side. Most cloud service providers offer the ability to physically locate instances in a specific location within a data center to increase network performance. By clicking on Subscribe Now, I agree to HPE sending me personalized email communication about HPE and select HPE-Partner products, services, offers and events. Wenn Sie ber einen Consumer verfgen, der zu viel Zeit mit der Verarbeitung von Nachrichten verbringt, knnen Sie dies entweder durch eine Erhhung der Obergrenze fr die Zeit, die ein Consumer im Leerlauf sein kann, bevor er weitere Datenstze abruft, oder max.poll.interval.ms durch eine Verringerung der maximalen Gre der mit dem Konfigurationsparameter zurckgegebenen Batches max.poll.records beheben. They recommend increasing the spark.executor.heartbeatInterval from 10s to 20s. If the number of partitions is not a multiple of the number of consumers, a few of the consumers will have additional load. The MapR Data Platform is the only currently available production-ready implementation of such a platform as of this writing. Niedriger Durchsatz/Geringe Latenz. Written by Im folgenden Abschnitt werden einige der wichtigen allgemeinen Konfigurationen hervorgehoben, um die Leistung Ihrer Kafka-Consumer zu optimieren. Bei der Nutzung von Datenstzen knnen Sie bis zu einen Consumer pro Partition einsetzen, um parallele Verarbeitung der Daten zu erzielen. Most common Google searches don't turn out to be very useful, at least at first. Since some calls will start in one batch and finish in another, the system keeps three hours and only processes the middle one-hour batch, thus the join can succeed on close to 100% of the records. Hoher Durchsatz/Geringe Latenz. Der Codec snappy bietet weniger Komprimierung bei weniger CPU-Aufwand. Die Batchgre bezeichnet die Anzahl der Bytes, die vorhanden sein muss, ehe diese Gruppe bertragen wird. The more consumers you have, the larger risk there is, that one might drop and halt all other consumers. As it turns out, cutting out Hive also sped up the second Spark application that joins the data together, so that it now ran in 35m, which meant that both applications were now well within the project requirements. This is a good practice with a new application, since you dont know how much you will need at first. The streaming app is very straightforward: map, filter, and foreach partition to save to Ignite. You may unsubscribe from receiving HPE and HPE-Partner news and offers at any time by clicking on the Unsubscribe button at the bottom of the newsletter. Finally, HBase is used as the ultimate data store for the final joined data. Die Erhhung der Partitionsdichte (also der Anzahl von Partitionen pro Broker) fhrt zu mehr Verarbeitungsaufwand im Zusammenhang mit Metadatenvorgngen und pro Partitionsanfordung/-antwort zwischen der bergeordneten Partition und ihren untergeordneten Partitionen. Ein Beispiel fr diesen Anwendungstyp ist die Onlineprfung von Rechtschreibung und Grammatik. Eine ausfhrliche Erluterung aller Brokereinstellungen finden Sie in der Apache Kafka-Dokumentation zu Brokerkonfigurationen. The streaming application finally became stable, with an optimized runtime of 30-35s. Wir empfehlen, in Azure HDInsight mindestens die Dreifachreplikation fr Kafka zu verwenden. Die Einstellung compression.type gibt den Komprimierungscodec an, der verwendet werden soll. Tuning this application took about 1 week of full-time work. But the team never could get Kafka and Ignite to play nice with Mesos, and so they were running in standalone mode, leaving only Spark to be managed by Mesos. Diese Dateien sind wiederum auf mehrere Kafka-Clusterknoten verteilt. Tuning a Kafka/Spark Streaming application requires a holistic understanding of the entire system. Most likely, there are 36 partitions because there are 6 nodes with each 6 disks assigned to HDFS, and Kafka documentation seems to recommend having about one partition per physical disk as a guideline. Kafka guarantees that a message is only read by a single consumer in the group. Ein Beispiel fr diesen Anwendungstyp ist die berwachung der Verfgbarkeit von Diensten. Wenn eine Abrufanforderung nicht gengend Nachrichten gem der Gre von fetch.min.bytes enthlt, wartet sie basierend auf dieser Konfiguration fetch.max.wait.ms bis zum Ablauf der Wartezeit. Surely, with more time, there is little doubt all applications could be properly configured to work with Mesos. Weitere Informationen zur Untersttzung von Kafka-Partitionen finden Sie im offiziellen Apache Kafka-Blogbeitrag zur Erhhung der Anzahl untersttzter Partitionen in Version 1.1.0. Der Wert -1 bedeutet, dass von allen Replikaten eine Besttigung empfangen werden muss, bevor der Schreibvorgang abgeschlossen wird. Consumers in the group then divide the topic partitions fairly amongst themselves by establishing that each partition is only consumed by a single consumer from the group, I.e., each consumer in the group is assigned a set of partitions to consume from. Durchsatz ist die maximale Rate, mit der Daten verarbeitet werden knnen. We moved the ZooKeeper services from nodes 1-3 to nodes 10-12. The lessons learned here are fairly general and extend easily to similar systems using MapR Event Store as well as Kafka. Out of memory issues and random crashes of the application were solved by increasing the memory from 20g per executor to 40g per executor as well as 40g for the driver. A consumer group includes the set of consumer processes that are subscribing to a specific topic. For more information on how HPE manages, uses, and protects your personal data please refer to HPE Privacy Statement. Sie kann auch den CPU-Aufwand erhhen, wenn die vom Producer und Broker verwendeten Komprimierungsformate nicht bereinstimmen. They should be applauded for their pioneering spirit. Apache Ignite has a really nice Scala API with a magic IgniteRDD that can allow applications to share in-memory data, a key feature for this system to reduce coding complexity. vm.max_map_count definiert die maximale Anzahl von MMaps, die ein Prozess haben kann. Kafka-Producer schreiben Daten in Themen. Ein Kafka-Producer kann so konfiguriert werden, dass Nachrichten komprimiert werden, ehe sie an Broker gesendet werden. Proposing to remove Mesos was a bit controversial, as Mesos is much more advanced and cool than Spark running in standalone mode. The system had to be ready for production testing within a week, so the code from the architecture and algorithm point of view was assumed to be correct and good enough that we could reach the performance requirement only with tuning. Zwischen Durchsatz und Kosten liegt hier der Kompromiss. The data is pushed into Kafka by the producer as batches every 30s, as it is gathered by FTP batches from the remote systems. It will be queried by other systems outside the scope of this project. Diese Einstellung wirkt sich auf die Datenzuverlssigkeit aus und lsst die Werte 0, 1 oder -1 zu. Learn more in our Cookie Policy. Please enable JavaScript in your browser and refresh the page. With improvements from the next part, the final performance of the Spark Streaming job went down in the low 20s range, for a final speedup of a bit over 12 times. Folglich beansprucht ein hherer Replikationsfaktor mehr Speicherplatz und CPU-Leistung, um zustzliche Anforderungen zu verarbeiten, was die Schreiblatenz erhht und den Durchsatz verringert. Es empfiehlt sich, die Anzahl der Partitionen mit der Anzahl der Consumer zu teilen. Bei den beiden hufig verwendeten Komprimierungscodecs gzip und snappy hat gzip ein hheres Komprimierungsverhltnis, was zu einer geringeren Datentrgerauslastung auf Kosten einer hheren CPU-Last fhrt. In addition, successive batches tended to have much more similar processing time with a delta of 1-3 seconds, whereas it would previously vary by over 5 to 10 seconds. the Kafka cluster unstable, and Kafka brokers do not automatically recover from it. Copyright 2015-2022 CloudKarafka. Indeed, in light of the goal of the system, the worse-case scenario for missing data is that a customer's call quality information cannot be found which is already the case. Consumer lesen mit der gewhlten Taktung Daten aus Kafka-Themen und knnen ihre Position (Offset) im Themenprotokoll whlen. Weitere Informationen zur Replikation finden Sie unter Apache Kafka: Replication (Apache Kafka: Replikation) und Apache Kafka: Increasing replication factor (Apache Kafka: Erhhen des Replikationsfaktors). The raw data is ingested into the system by a single Kafka producer into Kafka running on 6 servers. For AWS, see placement groupsas an example. Wenn Ihre Anwendung mehr Durchsatz erfordert, erstellen Sie einen Cluster mit mehr verwalteten Datentrgern pro Broker. Typically we see production with a three second delay and development with zero delay. Beachten Sie, dass wenn nicht alle Replikate besttigt werden, die Datenzuverlssigkeit abnehmen kann. Weitere Informationen zum Konfigurieren der Anzahl verwalteter Datentrger finden Sie unter Konfigurieren von Speicher und Skalierbarkeit von Apache Kafka in HDInsight. Kafka partitions are matched 1:1 with the number of partitions in the input RDD, leading to only 36 partitions, meaning we can only keep 36 cores busy on this task. Dieser Browser wird nicht mehr untersttzt. Third, the application was randomly crashing after running for a few hours. on Zookeeper). As there are three logs, there are three Kafka topics. Kafka consumer optimization can help avoid errors and increase performance of your application. We found the correct solution from somebody having the same problem, as seen inSPARK-14140 in JIRA. Wenn also jede Partition ein einzelnes Protokollsegment hostet, sind mindestens 2 mmap erforderlich. Die Optimierungen, die Sie anwenden, hngen von Ihren geschftlichen Anforderungen ab. These problems can be fixed in the code, which now handles exceptions more gracefully, though there is probably work left to increase the robustness of the code. There is little need for dynamic resource allocations, scheduling queues, multi-tenancy, and other buzzwords. The work we did to fix the performance had a direct impact on system stability. Note that old Kafka consumers store consumer offset commits in Zookeeper (deprecated). Its not simply about changing the parameter values of Spark; its a combination of the data flow characteristics, the application goals and value to the customer, the hardware and services, the application code, and then playing with Spark parameters. Lovisa Johansson Hoher Durchsatz/Hohe Latenz. Kafka Consumers reads data from the brokers and can be seen as the executor Latenz ist der Zeitaufwand fr die Speicherung oder den Abruf von Daten. An illustrative, real-world case, built with more time and thought up-front, can end the entire project faster, as opposed to rushing headlong into coding the first working solution that comes to mind. The input data was unbalanced, and most of the application processing time was spent processing Topic 1 (with 85% of the throughput). Sign up for the HPE Developer Newsletter or visit the, https://www.hpe.com/us/en/software/data-fabric.html, https://apacheignite.readme.io/docs/jvm-and-system-tuning, Control over executor size and number was poor, a known issue (. The default processing guarantee is at least once.Kafka balances performance with its best effort to send messages once to each consumer group. The role of the Kafka consumer is to read data from Kafka. Je niedriger der Consumer session.timeout.ms ist, desto schneller knnen wir diese Fehler erkennen. you can avoid common errors and ensure your configuration meets your It is recommended to use new consumers that store offsets in internal Kafka topics (reduces load Die Menge an Arbeitsspeichern, die von JVM auf Arbeitsspeicherzuordnungen verwendet werden darf, wird durch die Einstellung MaxDirectMemory bestimmt. To meet the needs of the telecom company, the goal of the application is to join together the log data from three separate systems. Consumers are allowed to read from any offset point they choose. The initial choice of Mesos to manage resources was forward-looking, but ultimately we decided to drop it from the final production system. Einem Thema ist ein Protokoll zugeordnet, das eine Datenstruktur auf dem Datentrger darstellt. Three hours of data are accumulated into Ignite, because the vast majority of calls last for less than an hour, and we want to run the join on one hours worth of data at a time. Dies fhrt zu einem Neuausgleichs eines Consumers. Because of schedule pressure, the team had given up on trying to get those two services running in Mesos. Consumer werden als aktiv betrachtet, wenn sie einen Heartbeat an einen Broker insession.timeout.ms senden knnen. Speicherdatentrger bieten begrenzte Kapazitt fr IOPS (Ein-/Ausgabevorgnge pro Sekunde) und Lese-/Schreibvorgnge von Bytes pro Sekunde. Before tuning, each stage always had 36 partitions!
Dieser Artikel enthlt Vorschlge zum Optimieren der Leistung Ihrer Apache Kafka-Workloads in HDInsight. Beim Erstellen neuer Partitionen speichert Kafka jede neue Partition auf dem Datentrger mit den wenigsten vorhandenen Partitionen, um sie gleichmig auf die verfgbaren Datentrger zu verteilen. In practice, as this setting is easy to change, we empirically settled on 40g being the smallest memory size for the application to run stably. Cloudera recommends to use a 3-5 machines Zookeeper ensemble solely dedicated to Kafka (co-location of applications Kafka-Consumer lesen Daten aus Themen.
In a Spark Streaming application, the stream is said to be stable if the processing time of each microbatch is equal to or less than the batch time. I think this problem may be caused by nodes getting busy from disk or CPU spikes because of Kafka, Ignite, or garbage collector pauses. Es ist mglich, dass dieser erreicht wird. in the Apache Kafka three-stage rocket. Keep your broker cluster running at peak performance by ensuring proper network connectivity. The Spark applications are both coded in Scala 2.10 and Kafkas direct approach (no receivers). MapR Data Platform would have cut the development time, complexity, and cost for this project. Producer senden Datenstze an Kafka-Broker, die die Daten anschlieend speichern. The configuration change fixed this issue completely. If one consumer in a group has a Trotz Speicherungsstrategie kann Kafka bei der Verarbeitung von Hunderten von Partitionsreplikaten auf jedem Datentrger den verfgbaren Datentrgerdurchsatz leicht komplett ausschpfen. Dieser Parameter definiert die minimalen Bytes, die von einer Abrufantwort eines Consumers erwartet werden. Da jeder Consumerthread Nachrichten aus einer Partition liest, wird auch die Nutzung von Daten aus mehreren Partitionen parallel verarbeitet. In HDInsight Apache Kafka Linux-Cluster-VM ist der Wert standardmig 65535. In fact, the requirements for this project show the real-world business need for a state-of-the-art converged platform with a fast distributed files system, high-performance key-value store for persistence, and real-time streaming capabilities. Es gibt verschiedene Mglichkeiten zum Messen der Leistung. Die richtige Balance zwischen Durchsatz, Latenz und den Kosten der Anwendungsinfrastruktur zu finden, kann eine echte Herausforderung sein. This means that consumers in the group are not able to A distribution of partitions Bei geringer Last kann eine erhhte Batchgre die Sendelatenz von Kafka erhhen, da der Producer darauf wartet, dass ein Batch fertig wird. The technology stack selected for this project is centered around Kafka 0.8 for streaming the data into the system, Apache Spark 1.6 for the ETL operations (essentially a bit of filter and transformation of the input, then a join), and the use of Apache Ignite 1.6 as an in-memory shared cache to make it easy to connect the streaming input part of the application with joining the data. Ein hherer Replikationsfaktor fhrt zu zustzlichen Anforderungen zwischen der bergeordneten und den untergeordneten Partitionen. Debugging a real-life distributed application can be a pretty daunting task. We offer fully managed Apache Kafka clusters with epic performance & superior support, Get a managed Apache Kafka server for FREE, Andernfalls wird der Consumer als tot oder gescheitert betrachtet. When the data is joined, it becomes possible to correlate the network conditions to a particular call for any particular customer, thus allowing customer support to provide accurate and useful information to customers who are unsatisfied with their phone service. Selbst wenn keine Daten durchflieen, rufen Partitionsreplikate dennoch Daten aus der bergeordneten Partition ab, was zu einer zustzlichen Verarbeitung von Sende- und Empfangsanforderungen ber das Netzwerk fhrt. Happily, the machines in the production cluster were heavily provisioned with memory. The second part involves no more than 100GB worth of data, and the cluster hardware is properly sized to handle that amount of data. Apache Kafka-Producer stellen Gruppen von Nachrichten (sog. This allows consumers to join the cluster at any point in time. The original developer was never given access to the production cluster or the real data. The network is 10GB Ethernet. In other words, the risk of data loss is not a deal-breaker, and the upside to gaining data is additional insights. A consumer can read from many partitions. We configured the application to run with 80 cores at a maximum of 10 cores per executor, for a total of 8 executors. Replikation kommt zum Einsatz, um Partitionen auf Knoten zu duplizieren. Wenn session.timeout.ms zu niedrig ist, kann es bei einem Consumer zu wiederholten unntigen Neuausgleichen kommen, z. For information about current offerings, which are now part of HPE Ezmeral Data Fabric, please visit https://www.hpe.com/us/en/software/data-fabric.html, Real-world case study in the telecom industry. In addition, its a single-purpose cluster, so we can live with customizing the sizing of the resources for each application with a global view of the systems resources. Achten Sie darauf, wie viel Arbeitsspeicher auf dem Knoten verwendet wird und ob gengend RAM zur Verfgung steht, um dies zu untersttzen. Hinsichtlich Leistung von Apache Kafka gibt es zwei Hauptaspekte: Durchsatz und Latenz. A MapR solution could probably skip the requirement for a still speculative open-source project like Ignite, since the full software stack required by the architecture is already built-in and fully supported. Jede Kafka-Partition ist eine Protokolldatei im System. Die Erhhung des Parameters batch.size kann den Durchsatz steigern, da der Verarbeitungsaufwand fr Netzwerk- und E/A-Anforderungen reduziert wird. Kafka fgt Datenstze von Producern am Ende eines Themenprotokolls an. The main issues for these applications were caused by trying to run a development system's code, tested on AWS instances on a physical, on-premise cluster running on real data. To view or add a comment, sign in Basically, this is a fairly straight-up ETL job that would normally be done as a batch job for a data warehouse but now has to be done in real time as a streaming distributed architecture. A consumer can join a group, called a consumer group. Batches) zusammen, die als Einheit gesendet und in einer einzigen Speicherpartition gespeichert werden. A second "regular" Spark application runs on the data stored in-memory by Ignite to join the records from the three separate logs into a single table in batches of 1 hour. Der Standardwert ist 1. With this change, the run time for each batch dutifully came down by about five seconds, from 30 seconds down to about 25 seconds. Die Datenkomprimierung erhht die Anzahl der Datenstze, die auf einem Datentrger gespeichert werden knnen. The application has great additional value if it can do this work in real time rather than as a batch job, since call quality information that is 6 hours old has no real value for customer service or network operations.
If there are four consumers and eight partitions, each consumer will read from two partitions. Um diese Ausnahme zu vermeiden, verwenden Sie die folgenden Befehle, um die Gre fr mmap in vm zu berprfen und die Gre bei Bedarf auf jedem Workerknoten zu erhhen. Ein hoher Durchsatz ist in der Regel besser. Select Accept to consent or Reject to decline non-essential cookies for this use. takes around 2-3 seconds or longer. But a better choice of platform and application architecture would have made their lives a lot easier. Wenn die Anzahl der Consumer kleiner als die Anzahl der Partitionen ist, lesen einige der Consumer aus mehreren Partitionen, wodurch sich die Wartezeit der Consumer erhht. bad connection, the whole group is affected and will be gzip bietet eine fnfmal hhere Komprimierungsrate als snappy. Make the Spark Streaming application stable. and following blog post focuses on the. I understand that my email address will be used in accordance with HPE Privacy Statement. Some parts of the code assumed reliability, like queries to Ignite, when in fact there was a possibility of the operations failing. A partition can only send data to a single consumer. Ein Beispiel fr diese Art von Anwendung ist die Erfassung von Telemetriedaten fr echtzeitnahe Prozesse wie in Anwendungen fr Sicherheit und Angriffserkennung. The team was also able to switch easily between test data and the real production data stream as well as a throttle on the producers to configure how much data to let in to the system. In einigen Szenarien scheinen Consumer langsam zu sein, wenn die Nachricht nicht verarbeitet werden kann. The main information we used was Spark UI and Spark logs, easily accessible from the Spark UI. Aim to have the number of partitions either be exactly the number of consumers or a multiple thereof. The view of Jobs and Stages as well as the streaming UI are really very useful. Ihre Leistungsanforderungen entsprechen mit hoher Wahrscheinlichkeit einer der folgenden drei gngigen Situationen, je nachdem, ob Sie hohen Durchsatz, geringe Latenz oder beides bentigen: In den folgenden Abschnitten werden einige der wichtigsten allgemeinen Konfigurationseigenschaften zur Optimierung der Leistung Ihrer Kafka-Producer vorgestellt. Given this system is heading into production for a telecom operator with 24/7 reliability expectation, such an advantage is considerable. Bei hoher Last wird empfohlen, die Batchgre zu erhhen, um Durchsatz und Latenz zu verbessern. For example, if there are four consumers and nine partitions, three consumers will read from two partitions and one consumer will read from three partitions. consume messages during this time.
consumer connects or drop out of the consumer group. << Kafka Performance: System-Level Broker Tuning, There are cases where Zookeeper can require more connections. Der Schwerpunkt liegt auf dem Anpassen der Producer-, Broker- und Consumerkonfiguration. In the case of this system, Kafka and Ignite are running outside of Mesos knowledge, meaning its going to assign resources to the Spark applications incorrectly. LinkedIn and 3rd parties use essential and non-essential cookies to provide, secure, analyze and improve our Services, and to show you relevant ads (including professional and job ads) on and off LinkedIn. We could indeed see this problem when the join application runs: the stages with 25GB shuffle had some rows with spikes in GC time, ranging from 10 seconds up to more than a minute. Several strategies were required, as we will explain below.
kafka consumer performance tuning
