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Apache Spark vs Solr: What are the differences?
Introduction:
Apache Spark and Solr are two popular technologies used in big data processing and analytics. While both technologies serve different purposes, they have some key differences that set them apart.
Data Processing Paradigm: Apache Spark is an open-source cluster computing framework that focuses on providing a unified analytics engine for big data processing. It offers a distributed processing model and supports both batch and stream processing. On the other hand, Solr is an open-source search platform that is built on top of Apache Lucene. Solr is primarily designed for full-text search and does not support general-purpose data processing like Spark.
Data Storage: Apache Spark utilizes a distributed file system (HDFS) or distributed data warehouses (e.g., Apache HBase, Apache Cassandra) to store its data. It can process and analyze data from various sources, including Hadoop Distributed File System (HDFS), Apache Cassandra, Apache HBase, etc. On the contrary, Solr uses its own built-in NoSQL database called Apache Lucene to store and retrieve data. It is optimized for search operations and provides advanced indexing and querying capabilities.
Querying Capabilities: Apache Spark provides a rich set of APIs and libraries for data analysis, machine learning, and graph processing. It supports complex SQL queries, data frames, and graph algorithms. Additionally, Spark's machine learning library (MLlib) offers a wide range of algorithms and tools for building and training models. Solr, on the other hand, offers powerful full-text search capabilities with advanced querying features like faceted search, filtering, highlighting, and spell suggestions. It excels in text search and retrieval tasks.
Real-time Processing vs Batch Processing: Apache Spark is designed to process and analyze data in real-time or near real-time. It supports stream processing frameworks like Apache Kafka, allowing developers to process data as it arrives. This makes Spark suitable for applications requiring real-time analytics or continuous data processing. On the contrary, Solr is more focused on batch processing and indexing. Although it has some support for near-real-time indexing, it is not as efficient as Spark in processing data streams.
Scalability and Fault-Tolerance: Apache Spark is designed to handle large-scale data processing and offers built-in mechanisms for fault tolerance and data parallelism. It can distribute computing tasks across a cluster of machines, providing horizontal scalability. Spark's Resilient Distributed Datasets (RDDs) allow fault-tolerant distributed processing. In contrast, Solr also supports distributed indexing and searching, but it is primarily optimized for single-node deployments. Scaling Solr requires setting up multiple instances and configuring load balancing.
Ecosystem and Integration: Apache Spark has a vibrant and active community that has developed a rich ecosystem of libraries and tools. It integrates well with other big data technologies like Apache Hadoop, Apache HBase, Apache Kafka, etc. It also provides support for various programming languages like Python, Java, Scala, and R. On the other hand, Solr integrates well with the Apache Lucene ecosystem and can be easily used with other components like Apache ZooKeeper. However, its ecosystem is not as extensive as Spark's.
In Summary, Apache Spark is a powerful and versatile analytics engine that supports both batch and real-time processing, while Solr is a search platform optimized for full-text search and indexing.
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 Solr
- Powerful35
- Indexing and searching22
- Scalable20
- Customizable19
- Enterprise Ready13
- Restful5
- Apache Software Foundation5
- Great Search engine4
- Security built-in2
- Easy Operating1
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
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Cons of Solr
Cons of Apache Spark
- Speed4
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