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Apache Flink vs Splunk: What are the differences?
Apache Flink vs Splunk
Apache Flink and Splunk are both powerful tools used for processing and analyzing data, but they have key differences that set them apart. Here are the main differences between Apache Flink and Splunk:
Architecture: Apache Flink is a distributed stream processing framework that focuses on real-time data processing and event-driven applications. It is designed to handle large streams of data in a fault-tolerant and highly available manner. On the other hand, Splunk is a software platform that specializes in collecting, indexing, and analyzing machine-generated big data. It provides a centralized and scalable solution for search, monitoring, and data visualization.
Data Processing Model: Apache Flink follows a stream processing model, where data is processed as it arrives, enabling low-latency, continuous, and real-time analytics. It supports stateful processing, allowing users to maintain and update state while processing the data streams. Splunk, on the other hand, uses a batch processing model, where data is indexed and stored before being queried and analyzed. It is well-suited for log management and retrospective analysis of historical data.
Programming Languages: Apache Flink provides support for multiple programming languages, including Java, Scala, and Python. It offers a rich set of APIs and libraries that enable developers to build complex streaming applications with ease. Splunk, on the other hand, uses its own proprietary search processing language called SPL, which is specifically designed for handling machine-generated data. It also provides integration with other programming languages through its SDKs.
Scalability and Flexibility: Apache Flink is built to scale horizontally, allowing users to leverage the power of distributed computing by running Flink jobs on multiple machines. It provides automatic fault tolerance and efficient resource management, which makes it suitable for large-scale data processing. Splunk, on the other hand, is known for its scalability and flexibility in handling big data. It can handle large volumes of data from various sources and supports distributed search to improve performance.
Use Cases: Apache Flink is commonly used in scenarios where real-time analytics and event-driven processing are required, such as fraud detection, clickstream analysis, and IoT data processing. It excels in handling continuous streams of data and provides low-latency processing capabilities. Splunk, on the other hand, is often used for log management, security information and event management (SIEM), and IT operations analytics. It helps organizations gain insights from machine-generated data and enables proactive monitoring and troubleshooting.
Ecosystem and Community: Apache Flink has a thriving open-source community and a rich ecosystem of connectors, libraries, and tools that support various use cases. It integrates well with other Apache projects like Kafka, Hadoop, and Spark, allowing users to build end-to-end data processing pipelines. Splunk, on the other hand, has a proprietary ecosystem with its own marketplace for apps and add-ons. It provides a wide range of integrations with popular enterprise systems and offers a comprehensive set of features specifically designed for log analysis and monitoring.
In summary, Apache Flink is a distributed stream processing framework that focuses on real-time data processing, while Splunk is a software platform for collecting, indexing, and analyzing machine-generated big data. Flink is known for its low-latency, continuous processing capabilities, while Splunk excels in log management and retrospective analysis.
We have a Kafka topic having events of type A and type B. We need to perform an inner join on both type of events using some common field (primary-key). The joined events to be inserted in Elasticsearch.
In usual cases, type A and type B events (with same key) observed to be close upto 15 minutes. But in some cases they may be far from each other, lets say 6 hours. Sometimes event of either of the types never come.
In all cases, we should be able to find joined events instantly after they are joined and not-joined events within 15 minutes.
The first solution that came to me is to use upsert to update ElasticSearch:
- Use the primary-key as ES document id
- Upsert the records to ES as soon as you receive them. As you are using upsert, the 2nd record of the same primary-key will not overwrite the 1st one, but will be merged with it.
Cons: The load on ES will be higher, due to upsert.
To use Flink:
- Create a KeyedDataStream by the primary-key
- In the ProcessFunction, save the first record in a State. At the same time, create a Timer for 15 minutes in the future
- When the 2nd record comes, read the 1st record from the State, merge those two, and send out the result, and clear the State and the Timer if it has not fired
- When the Timer fires, read the 1st record from the State and send out as the output record.
- Have a 2nd Timer of 6 hours (or more) if you are not using Windowing to clean up the State
Pro: if you have already having Flink ingesting this stream. Otherwise, I would just go with the 1st solution.
Please refer "Structured Streaming" feature of Spark. Refer "Stream - Stream Join" at https://spark.apache.org/docs/latest/structured-streaming-programming-guide.html#stream-stream-joins . In short you need to specify "Define watermark delays on both inputs" and "Define a constraint on time across the two inputs"
Pros of Apache Flink
- Unified batch and stream processing16
- Easy to use streaming apis8
- Out-of-the box connector to kinesis,s3,hdfs8
- Open Source4
- Low latency2
Pros of Splunk
- API for searching logs, running reports3
- Alert system based on custom query results3
- Splunk language supports string, date manip, math, etc2
- Dashboarding on any log contents2
- Custom log parsing as well as automatic parsing2
- Query engine supports joining, aggregation, stats, etc2
- Rich GUI for searching live logs2
- Ability to style search results into reports2
- Granular scheduling and time window support1
- Query any log as key-value pairs1
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Cons of Apache Flink
Cons of Splunk
- Splunk query language rich so lots to learn1