Best Practices to Use Apache Kafka
Best Practices to Use Apache Kafka With Sprinkle
In this day and age, the collection of records and data is made in abundance irrespective of the business. There's a growing need in the market to stream this large data in real-time. However, streaming large data might seem tedious and time-consuming, but with Apache Kafka, the streaming process is eased up. Let's plunge deep into this.
What is Apache Kafka?
Kafka is an open-source publishing/subscribing messaging system developed by LinkedIn. Streaming large data on a real-time basis is a stern test and it's a time-consuming process but with Kafka, the data is streamed into the server as it comes.
Initially, Kafka was used as an activity-tracking data pipeline but later on, it was used as an operational data monitoring system. Kafka was generally claimed to be an alternative for log aggregation but it gives a cleaner abstraction and as a whole, Kafka is better than most message brokers.
Kafka's data messaging system comes in 4 steps:
The Producer API
It allows an application to publish a stream of records to one or more Kafka topics.
The Consumer API
It allows an application to subscribe to one or more topics and process the stream of records produced by them.
The Streams API
It allows an application to act as a stream processor, consuming an input stream from one or more topics and producing an output stream to one or more output topics, effectively transforming the input streams into output streams. Stream processors can stream data as it comes in, they extract the log data that's required in real-time and processes.
The Connector API
It allows for building and running reusable producers or consumers that connect Kafka topics to existing applications or data systems. For example, a connector to a relational database might capture every change to a table. Connector modules are external plug-in modules for various databases that can publish or receive new messages.
What does Apache Kafka do?
Kafka is the data messaging system that is used to ingest real-time data at a rapid pace. Producers are the ones who publish the data messages in the form of “Topics”, a unique name that's given for a data set. These kinds of unique names are provided for easy identification of the data stream.
As producers publish/stream messages through topics to the Kafka server, the consumers on the other end receive/subscribe to the data as it comes in. These messages fall under the term “Topics.”
Many users of Kafka process data in processing pipelines consisting of multiple stages, where raw input data is consumed from Kafka topics and then aggregated, enriched, or otherwise transformed into new topics for further consumption or follow-up processing.
Kafka has multiple servers running multiple processes all at once and can distribute messages from multiple servers to multiple “Consumer groups” in real time. As these data records are large, they cannot be streamed into a single Kafka server, hence the data records are partitioned.
Producers are the ones who decide the number of partitions big data requires. These partitions are stored on individual systems, with some simple mathematics, the partitions can be calculated in an orderly fashion. However, a large number of partitions can only be handled by dividing the work among the “Consumers” i.e. “Consumer group.”
Kafka clusters, a distributed system depend on a set of computers that act as the servers. These groups of computer systems acting together for a common purpose are termed as “Clusters.”
Sprinkle with Kafka and the steps involved
Sprinkle supports ingesting data from many data sources, one of them being Kafka. The following section describes how you can add Kafka as a Data Source in Sprinkle.
Click through the “Manage Data” tab which routes you to the “Data source” tab in which a new data source could be created. On clicking “Add new data source”, a modal displays the types of data sources through which the data can be ingested, in this case, the data source is “Kafka". The data is given a new name, and thus new data is created.
On creating new data, a new page pops up with three tabs
- Configure
- Add table
- Run and schedule
Configure:
The configure table requires you to select one of the two radio buttons “Zookeeper” and “Bootstrap server.”
Zookeeper:
The configuration, and synchronization services in Apache Kafka are acquired with the hierarchical key value called Zookeeper.
Bootstrap server:
Bootstrapping is uniform authentication of the user tools and servers unknown to each other which exchange a secret session key.
The Zookeeper radio button requires you to fill in the “Zookeeper connection” whereas the Bootstrap radio button requires you to fill in both the “Bootstrap server” tab and also the “Zookeeper connection” tab.
You can connect Kafka by providing a Zookeeper Address or a Bootstrap Server Address. Whichever one you choose, it should be accessible by the Compute Driver.
Add table:
After configuring the data source, you can add tables to it. Here you can configure a topic to be ingested as a table in the warehouse. We assume the records in the topic are in “JSON” format. “JSON” being unstructured data, you need to also provide a “Schema” for the table in the warehouse. Depending on the warehouse, you may be able to use complex types (map/array/struct, etc) as well.
Note that adding tables doesn't immediately import the table. You need to go to the “Run and Schedule Tab” and run the ingestion Job. After the configuration process, the “Topic name” is updated, and the “Hive schema” tab is dropped down where it represents the schema of the record i.e. “string”, “int”, etc based on the client's needs. This brings a wide range of options in which the table can be created.
Run and schedule:
This tab shows settings for ingestion jobs. You can run an ingestion job by clicking on the Run button. The section below shows a (limited) list of past jobs. An ingestion job consists of a combination of “Tasks”. Generally, one table translates to one task. You can control the parallelism of these tasks on the left. You can also control how many times a task should be retried if it fails.
Once you click on “Run” " one ingestion job starts and it spears up to the top of the list of jobs. You can expand that to see the current and upcoming tasks. Sprinkle Provides you with real-time updates on what exactly is happening in your Ingestion job. In the “Jobs List”, you can also see statistics about ingestion like the number of records, number of bytes fetched, etc.
If the job fails, you will be able to see the error in the message column. The job can fail due to the following reasons:
- Incorrect/Unreachable endpoints are provided in the “Configure Tab”. (Double-check the values you have provided and also check the connectivity from your side.)
- Incorrect configuration in a table. (Delete the table and add again with correct values.)
The next step is to schedule the ingestion to run at various frequencies (the default being nightly).
The table is allowed to run and check if it is a successful process or a failure, the number of jobs that are supposed to run in parallel is scheduled with the help of the “Concurrency” tab. The data is ingested at this point. There is no assurance that a job to succeed after this step, so the number of times the failed jobs need to be re-run could also be scheduled.
The next step is where the “Explores” tab is accessed to carry on with the process of scripting which allows Sprinkle to provide actionable insights as per the needs of the users.
Apache Kafka's distributed streaming system helps in generating real-time data at very short time intervals, this helps sprinkle build a better visualization. However, Sprinkle's ETL allows businesses to understand the gathered data and simplifies the analytics part lets businesses understand the best way to make use of data.
Frequently Asked Questions FAQs- Best Practices For Apache Kafka
What is Apache Kafka?
Apache Kafka is an open-source distributed event streaming platform for building real-time data pipelines and streaming applications.
Why is Apache Kafka important?
Apache Kafka is important because it allows businesses to process and analyze data in real-time, enabling them to make faster and more informed decisions.
What are some best practices for using Apache Kafka?
Some best practices for using Apache Kafka include:
- Set up automated data retention limits to the desired data retention period based on your specific use case and configure it in Kafka clusters. This helps in automatically deleting old or expired data, preventing unnecessary storage consumption.
- Keep an eye on disk space utilization to ensure that you have enough capacity to handle incoming data.
- Guarantee that you have sufficient replication of Kafka topics by setting an appropriate replication factor. The advised value is typically three, providing fault tolerance and high availability in case of node failures.
- Adjust TCP/IP settings like buffer sizes, socket options, etc., at both the producer and consumer sides to optimize network throughput for better performance.
How can businesses optimize their use of Apache Kafka?
Businesses can optimize their use of Apache Kafka by properly configuring their Kafka cluster for their specific use case, maintaining cluster performance, and regularly reviewing their Kafka infrastructure to ensure it meets their needs.
What are some common mistakes to avoid when using Apache Kafka?
Some common mistakes to avoid when using Apache Kafka include not properly configuring your Kafka cluster for your specific use case, not monitoring the performance and memory usage of your Kafka cluster, and not properly sizing your Kafka cluster to handle your data streams.
How can businesses get started with Apache Kafka?
Businesses can start with Apache Kafka by downloading and installing Kafka, exploring the documentation and resources available, experimenting with building real-time data pipelines and streaming applications, and correctly monitoring the Kafka cluster's memory and CPU resources.
What are the basic principles of Kafka?
The basic principles of Kafka revolve around its design as a distributed streaming platform. One of the key principles is its ability to handle high-throughput, fault-tolerant, and scalable real-time data streams. Kafka follows a publish-subscribe model, where producers write data to topics and consumers subscribe to those topics to receive the data.
How many types of Kafka are there?
There are two main types of Kafka: Apache Kafka and Confluent Kafka. Apache Kafka is the open-source version developed by the Apache Software Foundation, while Confluent Kafka is the enterprise-ready distribution provided by Confluent Inc., the company founded by the creators of Apache Kafka.
How many partitions for Kafka?
The number of partitions for Kafka depends on various factors such as throughput requirements, fault tolerance needs, and scalability goals. Partitioning allows for parallel processing since each partition can be consumed independently by consumer instances. Generally, it is recommended to have at least as many partitions as there are consumer instances to achieve maximum parallelism.
Is Kafka a framework or tool?
Kafka can be considered both a framework and a tool. As an open-source distributed streaming platform, it provides a foundation for building various data infrastructure solutions. It also offers a set of tools and APIs that enable developers to interact with Kafka and build applications on top of it.
