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Apache Spark vs Splunk: What are the differences?
Apache Spark vs Splunk
Apache Spark and Splunk are two popular big data processing platforms used for analyzing and processing large volumes of data. While both platforms have similar capabilities in terms of data processing and analytics, there are key differences that set them apart.
Data Processing Model: Apache Spark is a distributed computing platform that operates on an in-memory data processing model. It allows users to process large datasets in parallel across a cluster of machines, resulting in faster and more efficient data processing. On the other hand, Splunk follows a log-based data processing model, which means it ingests data logs generated by various sources and indexes them for search and analysis.
Data Source Compatibility: Apache Spark supports a wide range of data sources, including structured, semi-structured, and unstructured data from various file formats and databases. It can handle data in real-time streaming and batch mode. Splunk, on the other hand, specializes in ingesting and analyzing log data from applications, systems, and network devices. It provides out-of-the-box support for a wide range of log formats and protocols.
Query Language: Apache Spark provides a unified programming model and supports multiple programming languages, including Scala, Java, Python, and R. It also offers a rich set of high-level APIs for data manipulation and analysis. Splunk, on the other hand, uses its proprietary search language called SPL (Splunk Processing Language) for querying and analyzing data. SPL provides powerful search capabilities and allows users to extract valuable insights from log data.
Scalability and Performance: Apache Spark offers excellent scalability and performance by performing data processing operations in parallel across a cluster of machines. It can handle large-scale data processing tasks efficiently and provides fault tolerance mechanisms for handling failures. Splunk, on the other hand, is designed to handle high volumes of log data and provides scalability through distributed indexing and search capabilities. It is optimized for analyzing log data in real-time.
Data Visualization and Reporting: Apache Spark provides various libraries and tools for data visualization, including integration with popular visualization libraries like Matplotlib and D3.js. It also supports interactive data exploration and provides visualization capabilities within its notebooks. Splunk, on the other hand, offers powerful data visualization and reporting features out-of-the-box. It provides customizable dashboards, charts, and graphs to visualize and analyze log data effectively.
Deployment and Management: Apache Spark can be deployed in various environments, including on-premises data centers and cloud platforms. It provides a flexible cluster manager that allows users to deploy and manage Spark clusters efficiently. Splunk, on the other hand, provides a centralized management platform for deploying and configuring Splunk instances across an organization. It offers granular control over user access and permissions and provides extensive monitoring and reporting capabilities.
In summary, Apache Spark and Splunk differ in their data processing models, data source compatibility, query languages, scalability and performance characteristics, data visualization and reporting capabilities, and deployment and management options.
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 Spark
- Open-source61
- Fast and Flexible48
- One platform for every big data problem8
- Great for distributed SQL like applications8
- Easy to install and to use6
- Works well for most Datascience usecases3
- Interactive Query2
- Machine learning libratimery, Streaming in real2
- In memory Computation2
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 Spark
- Speed4
Cons of Splunk
- Splunk query language rich so lots to learn1
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