Apache Spark vs Solr

Overview

Solr

Stacks805

Followers644

Votes126

Apache Spark

Stacks3.1K

Followers3.5K

Votes140

GitHub Stars42.2K

Forks28.9K

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.

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Advice on Solr, Apache Spark

Nilesh

Technical Architect at Self Employed

Jul 8, 2020

Needs adviceon

Elasticsearch

Kafka

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.

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Detailed Comparison

Solr	Apache Spark
Solr is the popular, blazing fast open source enterprise search platform from the Apache Lucene project. Its major features include powerful full-text search, hit highlighting, faceted search, near real-time indexing, dynamic clustering, database integration, rich document (e.g., Word, PDF) handling, and geospatial search. Solr is highly reliable, scalable and fault tolerant, providing distributed indexing, replication and load-balanced querying, automated failover and recovery, centralized configuration and more. Solr powers the search and navigation features of many of the world's largest internet sites.	Spark is a fast and general processing engine compatible with Hadoop data. It can run in Hadoop clusters through YARN or Spark's standalone mode, and it can process data in HDFS, HBase, Cassandra, Hive, and any Hadoop InputFormat. It is designed to perform both batch processing (similar to MapReduce) and new workloads like streaming, interactive queries, and machine learning.
Advanced full-text search capabilities; Optimized for high volume web traffic; Standards-based open interfaces - XML, JSON and HTTP; Comprehensive HTML administration interfaces; Server statistics exposed over JMX for monitoring; Linearly scalable, auto index replication, auto-failover and recovery; Near real-time indexing; Flexible and adaptable with XML configuration; Extensible plugin architecture	Run programs up to 100x faster than Hadoop MapReduce in memory, or 10x faster on disk;Write applications quickly in Java, Scala or Python;Combine SQL, streaming, and complex analytics;Spark runs on Hadoop, Mesos, standalone, or in the cloud. It can access diverse data sources including HDFS, Cassandra, HBase, S3
Statistics
GitHub Stars -	GitHub Stars 42.2K
GitHub Forks -	GitHub Forks 28.9K
Stacks 805	Stacks 3.1K
Followers 644	Followers 3.5K
Votes 126	Votes 140
Pros & Cons
Pros 35 Powerful 22 Indexing and searching 20 Scalable 19 Customizable 13 Enterprise Ready	Pros 61 Open-source 48 Fast and Flexible 8 One platform for every big data problem 8 Great for distributed SQL like applications 6 Easy to install and to use Cons 4 Speed
Integrations
Lucene	No integrations available

What are some alternatives to Solr, Apache Spark?

Algolia

Our mission is to make you a search expert. Push data to our API to make it searchable in real time. Build your dream front end with one of our web or mobile UI libraries. Tune relevance and get analytics right from your dashboard.

Presto

Distributed SQL Query Engine for Big Data

Amazon Athena

Amazon Athena is an interactive query service that makes it easy to analyze data in Amazon S3 using standard SQL. Athena is serverless, so there is no infrastructure to manage, and you pay only for the queries that you run.

Apache Flink

Apache Flink is an open source system for fast and versatile data analytics in clusters. Flink supports batch and streaming analytics, in one system. Analytical programs can be written in concise and elegant APIs in Java and Scala.

lakeFS

It is an open-source data version control system for data lakes. It provides a “Git for data” platform enabling you to implement best practices from software engineering on your data lake, including branching and merging, CI/CD, and production-like dev/test environments.

Druid

Druid is a distributed, column-oriented, real-time analytics data store that is commonly used to power exploratory dashboards in multi-tenant environments. Druid excels as a data warehousing solution for fast aggregate queries on petabyte sized data sets. Druid supports a variety of flexible filters, exact calculations, approximate algorithms, and other useful calculations.

Apache Kylin

Apache Kylin™ is an open source Distributed Analytics Engine designed to provide SQL interface and multi-dimensional analysis (OLAP) on Hadoop/Spark supporting extremely large datasets, originally contributed from eBay Inc.

Splunk

It provides the leading platform for Operational Intelligence. Customers use it to search, monitor, analyze and visualize machine data.

Apache Impala

Impala is a modern, open source, MPP SQL query engine for Apache Hadoop. Impala is shipped by Cloudera, MapR, and Amazon. With Impala, you can query data, whether stored in HDFS or Apache HBase – including SELECT, JOIN, and aggregate functions – in real time.

Vertica

It provides a best-in-class, unified analytics platform that will forever be independent from underlying infrastructure.

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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.