Solr vs elasticsearch LEARNOVITA

Elasticsearch vs Solr | Difference You Should Know

Last updated on 31st Oct 2022, Artciles, Blog

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Smita Jhingran (Big Data Engineer )

Smita Jhingran provides in-depth presentations on various big data technologies. She specializes in Docker, Hadoop, Microservices, MiNiFi, Cloudera, Commvault, and BI tools with 5+ years of experience.

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    • 1.What is Apache Solr?
    • 2.What is Elasticsearch?
    • 3.Elasticsearch vs Solr: Key Differences
    • 4.Installation and configuration
    • 5.Features and Implementation
    • 6.Elasticsearch vs Solr – Which has a better learning curve and community support
    • 7.Conclusion

What is the Apache Solr?

As open source search engine, Apache Solr is built on a top of Apache Lucene software library. With the HTTP requests, Apache Solr offers each of the advanced search capabilities of an Apache Lucene.

Initially released in year 2004, Apache Solr has a large and growing user community. Some of its best features include a distributed full text search, faceting, and real-time indexing. The latest release of an Apache Solr is version 8.6 – that was released in a July 2020. As standalone search server, Solr uses REST-like API – using which can index documents in a JSON, XML, and CSV formats.

What is the Elasticsearch?

Similar to the Apache Solr, Elasticsearch is built on a Apache Lucene library. With REST APIs, Elasticsearch leverages on a search and indexing functions of an Apache Lucene. This tool also offers a distributed full text search engine along with a HTTP web interface.

Released initially in year 2010, Elasticsearch is famous for its REST APIs usage, distributed architecture, along with its speed and scalability. Elasticsearch is integral component of ELK Stack tools (comprising Elasticsearch, Logstash, and Kibana) – that are used for a data ingestion, storage, analysis, and visualization.

Elasticsearch vs Solr – Which is a Better Elasticsearch or Solr?

Elasticsearch vs Solr

1. Performance and scalability

Going by an industry tests, both the Elasticsearch and Solr perform at same level for 95% of the use cases. Apache Solr is better choice if working with a static data and require accurate precision for the data analysis. On other hand, Elasticsearch has been designed for cloud platform. This tool is also simpler to work with – as it only has single process. As cloud-based distributed model, Solr uses a Solr Cloud that depends on the Apache ZooKeeper for implementing self-contained cluster and automatic node discovery.

If need application monitoring and work with the metrics, then Elasticsearch is better option. Alternatively, more Hadoop developers like Cloudera and MapReduce prefer to work with a Solr over Elasticsearch.

What about the scalability? Both these tools have a built-in support for sharding. However, with the horizontal scaling features, Elasticsearch offers better support for the cluster scaling and management. Even for a cloud deployments, Elasticsearch offers the better scalability – while Apache Solr requires support from the Apache Zookeeper and Solr Cloud for managing its clusters.

2. Indexing and searching

For indexing and searches, both the Apache Solr and Elasticsearch write their indexes using a Apache Lucene. While an Elasticsearch supports native DSL, Solr uses standard query parser tool to align the Lucene syntax. For structured query DSL, Elasticsearch has a built-in support while for Solr, need to program queries that go beyond Lucene query syntax.

When it comes to including the multiple document types in single index, Elasticsearch performs better in identifying every document type during indexing and querying. To achieve same, Apache Solr needs to develop the customized search component – or simulate feature within the application.

3. Data sources

Both Apache Solr and Elasticsearch uses a variety of a data sources. Apache Solr can import a data from sources are including JDBC, XML, CSV, Microsoft Word documents, and even PDF files. With its native support for an Apache Tika, it can extract and index thousands of the file types. Other data tools like an Apache Zeppelin and Flume also use Apache Solr as a data source.

Being based on the JSON, Elasticsearch supports a data imports from sources including Beats (available with Elastic Stack) and Logstash. Additionally, there are other data tools like a Kibana and Grafana that use Elasticsearch as a data source.

4. Node discovery and cluster management

Apache Solr and Elasticsearch differ majorly when it comes to the node discovery and also cluster management. Node discovery is a crucial for monitoring cluster node states and choosing a master node.

Elasticsearch uses its own automatic node discovery tool, Zen that assures a complete fault tolerance with at least 3 dedicated master nodes. On other hand, Apache Solr uses a Apache Zookeeper – with an external ensemble that needs at least 3 Zookeeper instances – for discovering a nodes on Solr Cloud.

Elasticsearch vs Solr: Installation and Configuration

  • Before installing either of these search engine tools, need to first install Java as prerequisite. Elasticsearch is more easier to install and configure as compared to the Apache Solr.
  • On a flip side, Elasticsearch requires a 1GB of HEAP memory for configuration – while Solr requires at least 512MB of configured a HEAP memory for an instance allocation. However, can change these default settings for an Elasticsearch (in the /config/jvm.options file) and for a Solr (in Solr script file or file).
  • While Elasticsearch supports the configuration files in a YML format, Apache Solr supports the XML-based configuration files.
  • The Elasticsearch installation package is more heavier than that of Solr. For instance, an Elasticsearch version 7.7.1 – released in a June 2020 – has a installer file of 314.5MB, while Solr version 8.5.2- released in a May 2020 – is much lighter at be 191.7MB.
  • Next, how does Solr perform against an Elasticsearch with regards to configuration?
  • For Solr can explain index structure and configuration in a managed schema file – along with a schema.xml file for matching a data structure.
  • On other hand, Elasticsearch is schema-less – where can launch the tool and send documents for the indexing without any indexing schema. Adn can choose to explain index structure (or mappings) and then create an index using the mappings.
  • For Apache Solr can configure all its components, caches, and search handlers in a solrconfig.xml file – where need to restart or reload a Solr node after every change.
  • For Elasticsearch, can write all configurations in an elasticsearch.yml config file. Additionally, for live cluster, can change settings about placement of the shards and replicas – without restarting an Elasticsearch node.
  • When it comes to the rebalancing shards, Elasticsearch can automatically load balance when add a new machines – and move its shards to the new cluster nodes. Solr does not have an automatic shard rebalancing feature.

Elasticsearch vs Solr: Features and Implementation

Solr Integration with Applications

Search engines typically have to process the large volumes of data and queries on a datasets containing millions of data records. Both Apache Solr and Elasticsearch have a list of the powerful features – but which is better? some of their features:

1. Sharding

  • Both these search engines are support sharding. Elasticsearch is more dynamic in a shard placement. For instance, it can simply move around shards within node cluster whenever a new node is added, or an existing node is be removed. However, an Elasticsearch has an inherent disadvantage that it cannot increase number of shards – once index has been created.
  • On other hand, Apache Solr is much static and does not take any action whenever a node is added or removed from a cluster. With Solr version 7, adn can use the AutoScaling API to explain rules for shard placement. With implicit routing, shards can also be added or a split – but cannot be reduced.

2. API support

  • Both Solr and Elasticsearch support the HTTP REST APIs. For binary APIs, Solr has a SolrJ Java-based client while an Elasticsearch uses tools like a TransportClient and Thrift though the plugin.
  • To get search results in a Solr, need to query any of the explained request handlers and pass necessary parameters. These parameters can differ based on a query parser use – but the method “HTTP GET request” is same.
  • On other hand, Elasticsearch supports a REST APIs that can be accessed through the multiple methods including Get, Delete, Post, and Put. With Elasticsearch, y can use a APIs for query documents, creating and managing indices, and obtaining the metrics showing the current Elasticsearch configuration.

3. Caching

  • Both Elasticsearch and Solr architecture varies when it comes to the caching mechanisms. For a start, both these search engines work on Lucene segments that are created whenever index the data. A segment is built on the multiple files containing immutable data.
  • Apache Solr uses a global caching – a form of caching that contains single caching instance of a specific type for a shard – across all its segments. Whenever a segment is modified, entire cache needs to be refreshed, which takes a time and consumes server resources.
  • Elasticsearch uses a caching for every segment – meaning even if a single segment is changed, only a portion of cached data needs to be refreshed.

4. Data Analytics

  • Both the Apache Solr and Elasticsearch have powerful data analytics and aggregation capabilities. Apache Solr uses faceting mechanism to slice and make a sense of large datasets. It also uses the advanced faceting with JSON APIs that are more faster and consume less memory. Finally, with its streaming an expression feature, Solr can analyze data from the multiple sources including the SQL and Solr.
  • Elasticsearch uses a data aggregation that can perform a one level of data analysis – much like faceting – and also use a nested data analysis. With its a pipeline aggregation, it can be used to calculate aggregations like a derivatives and moving averages.

5. Machine learning

  • Both Solr and Elasticsearch have a built-in support for the Machine learning (ML). With its contrib module libraries, can develop a ML ranking models and features on top of Solr tool.
  • On other hand, Elasticsearch is bundled with the Kibana plugin that supports a ML algorithms that can perform anomaly detection on a time series data. Compared to the Solr, this package can be quite expensive.
  • Elasticsearch vs Solr: Which has better learning curve and community support?
  • On whole, Elasticsearch is simpler to learn – as it just needs a single command to get started. Apache Solr require more technical expertise and knowledge to be implemented – though it has become a more user-friendly in recent versions.
  • As open source tool, any Solr developer can access its source code and make contribution. Elasticsearch is an open source – but not fully. While developers can make a contributions, the changes need to finally approved by a development team at Elastic (the company that owns Elasticsearch).
  • Going back to start of 2010, Apache Solr had broader base of online community users and developers – that contributed regularly towards product’s development and engineering. However, in last five years, Elasticsearch has grown its user base considerably – and has crossed a Solr in terms of popularity and support.
  • When it comes to the user documentation, Elasticsearch scores over Apache Solr – thanks to its official website documentation along with rhe other guides and books written by users.


Which search engine is a better – Elasticsearch or Solr? That is complex to decide and depends completely on use cases for which need a search engine – along with functionalities that they offer. While Solr scores higher in the information retrieval, Elasticsearch is better at a production and scalability. On positive note, both these tools are simple to work with and offer a great set of functionalities that have discussed in this guide.

Through this guide, have tried to list all the major differences between the Apache Solr and Elasticsearch – so that can make a right decision in selecting right tool. Additionally, need to consider own business requirements and use cases a before making the right selection.

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