Solr vs Elasticsearch
The main difference between Solr and Elasticsearch is that Solr is a completely open source search engine. Whereas Elasticsearch though open source is still managed by Elastic’s employees. Solr supports text search while Elasticsearch is mainly used for analytical querying, filtering, and grouping.
In this article, we are going to discuss in detail about Solr and Elasticsearch
Which one is better or faster?
Which one scales better?
Solr vs Elasticserach, which one is easier to manage?
Which one should we go for?
Should you migrate from Solr to Elasticsearch?
Let’s get started.
Solr is an open-source search platform built in a java library called Lucene and provides Apache Lucene search function in an easy to use way.
It has been in the search engine industry for almost a decade; it is a proven product with a strong and broad user community. Solr offers automatic load balancing, distributed reindexing, failover, and recovery queries.
If implemented correctly and managed well, it can become a highly reliable, scalable, fault-tolerant search engine. Many Internet giants like Netflix, eBay, Instagram, and Amazon (Cloud Search) use Solr because it can index and search multiple websites.
The list of key features includes:-
- Full-text search
- Multi-array Search
- Real-time indexing
- Dynamic Clustering
- Database integration
- NoSQL functionality and productive document handling (e.g. words and PDF files)
Elasticsearch is an open source (Apache 2 license), distributed, a RESTful search engine built on top of the Apache Lucene library.
Distributed search engines contain indexes that can be divided into fragments, and each fragment can have multiple copies. Each Elasticsearch node can have one or more fragments. Its engine also acts as a coordinator, delegates operations on the correct fragments.
Elasticsearch has near-real-time search scalability. One of its key features is multi-tenant.
The list of key features includes:
- Distributed Search
- Multi-lease period
- A string of Analyzers
- Scan Search
- Group Aggregation
Age and Maturity
Solr has a more extended history since it was created by yonik on the CNET network in 2004. It then contributed to Apache in 2006. On the other hand, the elastic search was started in 2001 by its founder Shaw Bannon with the name compass.
Since then, the creators of Kibana, Logstash, and Beat have joined the Elastic Stack family of products. It is an influential participant in the field of search and analysis of records. Therefore, Solr has advantages in terms of being in the market first and having a deeper reach.
Solr vs Elasticsearch: Community and Open Source
All have very active communities. If you check Github, you can see that they are prevalent open-source projects with many versions.
It is crucial to note that although both are released under the Apache license, and both are open source, they work a little different. Solr is a open source: anyone can help and contribute. One can add to elastic search as well, but it is up to flexible Elasticsearch employees or company to accept the same.
Is this good or bad? This means that if you need a function, and you contribute it to the community, with sufficient quality, it can be accepted. With Elasticsearch, it depends on whether the elastic decision will be accepted or not.
On the other hand, contributions to elastic search, through more quality controls, can provide greater consistency and quality.
Installation & Configuration
Flexible search is easy to install and very light. The current version of the Solr distribution package size (6.2.0) is approximately 150 MB. On the other hand, the current version (2.4.0) of elastic search has distribution package size of only 26.1 MB. Plus, you can install and run Elasticsearch minutes in a few minutes.
However, this ease of deployment and use can become a problem if Elasticsearch is mismanaged. JSON based configuration is easy, but if you want to specify comments for each configuration in the file, then it doesn’t work for you.
The latest version of Solr provides a good set of rest APIs that eliminate the complexities in earlier versions, such as recording clustering algorithms and creating custom snippets.
In general Elasticsearch is a better choice if your app uses JSON. Otherwise, use Solr because schema.xml and solrconfig.xml are well documented.
Solr vs Elasticsearch: Node Discovery
Another important difference between these two major products is node discovery. When a cluster is initially formed, when a new node is joined, or when something bad happens to a node in a cluster. You must decide what to do according to the given criteria. This is one of the so-called responsibility nodes found.
Elasticsearch uses its own discovery implementation, called Zen, which requires three dedicated master nodes to be completely fault-tolerant (i.e. unaffected by network divisions).
Solr uses Apache zookeeper for discovery and choice of leaders. This requires an external collection of animator-admin that requires at least three animator-admin instances for fault-tolerant and fully available Solr cloud clusters.
In general, Elasticsearch is very dynamic in terms of the location of the index and shard it is being built. When an operation occurs, you can move shards around the cluster, for example, when a new node is connected or a node is removed from the cluster.
We can decide where shard should or should not be placed by consciousness tags, we can tell Elasticsearch to move shards on request using api calls. Solr, on the other hand, is more static.
When a Solr node joins or exits the cluster Solr without doing anything on its own, we need to rebalance the data. Of course, we can move the shards, but it involves several steps. We need to create a copy, wait for it to Synchronize the data, and then delete the one we no longer need.
One thing that allows us to automate some things. Delete or replace nodes in Solr cloud using the collection API, which is a quick way to delete all fragments or quickly copy to another node. Although this still requires manual API calls, not auto done.
Solr vs Elasticsearch: Indexing and Search
Solr accepts data from different sources, including XML files, comma-separated value (csv) files, and data extracted from database tables, as well as common file formats such as Microsoft Word and PDF.
Elastic search also accepts data from many different sources. For example, AWS SQS, Dynamodb (Amazon nosql), file system, git, JDBC, JMS, Kafka, LDAP, MongoDB, Neo4j, Rabbitmq, Redis, Solr, and convulsions. There are also several plugins available.
Solr is more text-oriented, while elastic search is often used to parse queries, filter, and group.
The team behind elastic search always tries to make these queries more efficient (including methods to reduce memory usage and CPU usage) and improve performance by comparing in lucene and elastic search levels. It is clear that for applications that require not only text search but also time series complex search and aggregation, elastic search is a better option.
Both search engines use multiple parsers and markers to split text into terms or tags, which are then indexed. Elasticsearch allows you to specify a string of query parsers consisting of a series of parsers or tokeners per document or per query.
Connect multiple parsers so that the output of one parser becomes the input of the second parser. Conversely, Solr does not support this function.
You can index both search engines while using stop words and synonyms to match documents. In Solr, the connection index must be a single fragment and copied to all nodes to search for relationships between documents (e.g. sql connections).
This helps you find the main document with a child document that matches the criteria. Based on some performance tests, Elasticsearch can produce better results than Solr.
Solr vs Elasticsearch: API
If you know Apache Solr or elastic search, you know that they expose an HTTP API.
People familiar with Solr know that to get the search results from it, you need one of the query defined request handlers and pass parameters that define the query condition.
Depending on which query parser you choose to use, these parameters will be different, but the method will remain the same. An HTTP get request is sent to Solr for search results.
The good thing is that you’re not limited to a single answer format: you can choose to develop an answer writer for them in XML, Javabon, JSON format and various other formats.
So, you can choose the most convenient format for you and your search application. API not only involves queries, because you can also get some statistics about different search components or control Solr behaviour, such as collection creation.
What about the elastic search? Elasticsearch exposes a break API that can be accessed using the http get, delete, publish, and put methods.
Its API allows not only to query or delete documents, but also to create indexes, manage them, control analyses and get all the metrics describing the current state and elastic search settings. For any information about elastic search, you can get it through the rest of the API.
If you’re used to Solr, one thing that might be strange at first. The only elastic format lookup can answer in JSON, for example, no XML response. Another big difference between elastic search and Solr are queries. Structure as JSON representation.
Structure provides a lot of control over queries for JSON objects over how elastic lookups should understand the query and thus what results are returned.
Solr vs Elasticsearch: Cache
Another big difference is the architecture of elasticsearch and Solr. Do not delve into how caching works on both products, we will only point out the main differences between them.
A segment is a Lucene index built by several files, mostly immutable, and contains data. When indexing data, Lucene generates segments and can also merge several smaller existing segments into larger segments during a process called segment merging.
Solr has global caches, a single cache instance of a fragment of a given type, for all its segments. When you change a single segment, you need to override and clear the entire cache. This takes time and consumes hardware resources.
Solr is large and has many data analysis capabilities. We can start with the good old side: the first implementation allows you to cut and cut the data to understand and understand it.
Then comes the JSON aspect with similar functionality, but faster and less memory requirements, and finally the flow-based expressions are called flow expressions, which can combine data from multiple sources (like SQL, Solr, Polygon) and use various expressions (sort, extract, calculate important terms, etc.).
Elasticsearch provides a powerful aggregation engine that not only performs top-notch data analysis like most legacy aspects of Solr. But also allows for nested data analysis (e.g. calculating the average price of each product category in each store department). It also supports analysis above aggregate results, leading to actions such as the calculation of the moving average and other functions.
Finally, although marked as experimental, Elasticsearch provides support for array aggregation, which calculates statistics for a set of fields.
Solr vs Elasticsearch: Full-text search function
Solr and Elasticsearch take advantage of lucene’s features in almost real time. This allows queries to match documents immediately after indexing.
When you look at the Solr code base, the richness of features related to full-text search and features close to full-text search are huge.
Our Solr training course is full of these things! Starting with a wide selection of request parsers, implemented through various messages, to the ability to correct user misspellings using the spelling checker and extensive highlighting support, this is highly configurable.
Elasticsearch has a dedicated tip man API that hides user implementation details. It gives us an easier way to implement recommendations at the expense of reducing flexibility.
Solr is even more focused on text search. Elastic search, on the other hand, is often used for filtering and grouping (parsing query workloads) and not necessarily text searches.
Elastic search developers are putting a lot of effort to make this query more efficient (reducing memory usage and CPU usage) at lucene and elastic search levels.
If you’re going to ask a developer what elastic search he likes, the answer will be API, manageability and ease of installation.
When it comes to troubleshooting, ElasticSearch is easy to get information about its status, from disk usage information, job statistics through memory and garbage collection to internal elastic searches such as cache, buffer, and thread pool utilization.
Solr hasn’t done it yet – you can get some information through JMX MBean and the new Solr indicator API, but that means there are several places that need to be seen, not everything is there, although it is there.
Solr vs Elasticsearch: Non-flat data processing
Do you have non-flat data, many objects nested inside nested objects and inside another nested object? You don’t want to flatten the data, but just index your beautiful MongoDB JSON object and ready for full-text search?
Elasticsearch will be a perfect tool that supports objects, nested documents and parent-child relations. Solr may not be the best fit here, but note that it also supports parent-child and nested documents when indexing XML documents as well as JSON.
Solr supports query-time joins within and in different collections, so it’s not limited to parent-child processing of indexing time.
Let’s say aloud: Elasticsearch query language is really great. If you like JSON. It allows you to build the query using JSON, so it will be well structured and give you control over all the logic. You can mix different types of queries to write a very complex matching logic.
Text search is not everything; it can include aggregations, result collapse, etc. Everything you need from your data can be represented in the query language.
Solr, on the other hand, is still using URI search, at least in its most used API (the XML query parser and API JSON limited is also available). All parameters enter URI, which can lead to long and complex queries. Both search engines help users to find relevant results for a question.