It lets you structure the query using JSON, so it will be well structured and give you the control over the whole logic. You can mix different kinds of queries to write very sophisticated matching logic.
Of course, full-text search is not everything and you can include aggregations, results collapsing, and so on — basically, everything that you need from your data can be expressed in the query language. All the parameters go into the URI, which can lead to long and complicated queries. Both approaches have their pros and cons and novice users tend to need help with queries using both search engines.
It is beyond our control. In Solr, you have that control, which is a very good thing when you consider that during indexing the leaders are the ones that do more work, because of forwarding the data to all their replicas. With the ability to rebalance the leaders or explicitly say where they should be put we have the perfect ability to balance the load across the cluster, by providing exact information about where the leader shards should be.
Trending topic about which you will hear even more in the coming months and years: Machine :earning. In Solr it comes for free in a form of a contrib module and on top of streaming aggregations framework. With the use of the additional libraries in the contrib module, you can use the machine learned ranking models and feature extraction on top of Solr, while the streaming aggregations based machine learning is focused on text classification using logistic regression. On the other hand we have Elasticsearch and its X-Pack commercial plugin which comes with a plugin for Kibana that supports machine learning algorithms focused on anomaly detection and outlier detection in time series data.
When it comes to the Ecosystem, the tools that come with Solr are nice, but they feel modest. Of course, there are other tools, which can either read data from Solr, send data to Solr or use Solr as the data source — like Flume for example.
Most of the tools are developed and supported by a wide variety of enthusiasts. If you look at the ecosystem around Elasticsearch it is very modern and sorted. You have a new version of Kibana with new features popping up every month. Finally, those products are not only backed up by enthusiasts but also by large, commercial entities. This is obviously not an exhaustive list of Solr and Elasticsearch differences.
We could go on for several blog posts and make a book out of it, but hopefully, the above list gave you an idea on what to expect from one and the other. Published at DZone with permission of Rafal Kuc. See the original article here. Thanks for visiting DZone today,. Edit Profile. Sign Out View Profile. Over 2 million developers have joined DZone. This list will give you an idea of what to expect from Solr, what to expect from Elasticsearch, and how these expectations differ.
Though many core features are similar in each of these search engines, there are many significant differences—especially in regard to scalability, searches, and deployment. Based on Google trends, Elasticsearch appears to enjoy considerably more popularity than Solr. But Solr is by no means out of the game, since it also continues on with steady product releases, enjoys a sizable worldwide community, and open source publisher support.
Elasticsearch is fairly easy to setup, and it also has a considerably smaller footprint than Solr. As reference, version 5. In addition, for a basic configuration, you can install and run Elasticsearch within a few minutes while Solr takes much longer to install.
One of the reasons Solr typically takes longer is that its highly configurable, so if you want better tuning options, Solr gives that to you. Depending on your type of configuration, you may lean more towards Elasticsearch vs. Search engines must have the ability to integrate with large applications and manage huge collections that might contain millions or tens of millions of documents. Naturally, a search engine should be modular, scale well, and have facilities for replication—to permit easy clustering and accommodate a distributed architecture.
One area that Elasticsearch and Solr differ is in how they or you manage them in large clustered environments. When running Solr in a clustered architecture, it has an optional distributed SolrCloud deployment configuration that is similar to Elasticsearch, but is dependent on an entirely separate application—Apache ZooKeeper.
SolrCloud can provide a high-availability, fault-tolerant environments that distribute indexed content and manage queries across an array of servers. Solr adds complexity by requiring the ZooKeeper app, but it is more adept in avoiding inconsistencies that often arise from the split-brain issue that is common in Elasticsearch clusters. Elasticsearch takes a different approach, by including a built-in feature called Zen, that directly manages cluster states.
This built in capability makes Elasticsearch easier to start in a clustered environment vs. Solr, as long as inconsistencies are kept in check by the user. Apache Lucene forms the core of both Elasticsearch and Solr, and it employs shards as the partitioning unit for any index. An index is distributable by configuring shards to run on separate machines within a cluster. Sharding is one way to scale your search application.
Though Solr previously lacked the ability to change the number of shards for an index, SolrCloud now supports shard splitting —by which you can split one or more existing shards in an index. Sharding is the key to how Solr search scales as requirements increase. Moreover, Elastic, the company behind Elasticsearch, mixes code released under Apache 2.
Needless to say, the Elasticsearch user community is not pleased with this. AWS built their own Elasticsearch distribution under the Apache license and bundled a number of features, like alerting, security, etc. A number of organizations have chosen Solr over Elasticsearch as their horses in the search race e.
Cloudera, Hortonworks, MapR, etc. Both Solr and Elasticsearch have lively user and developer communities and are rapidly being developed. If you need to add certain missing functionality to either Solr or Elasticsearch, you may have more luck with Solr.
True, there are ancient Solr JIRA issues that are still open, but at least they are still open and not closed. Elasticsearch vs. Solr Contributors click to enlarge. Solr Commits source: Open Hub click to enlarge.
As you can see, Elasticsearch numbers are trending sharply upward, and now more than double Solr Commit activity. This is not a very precise or absolutely correct way to compare open source projects, but it gives us an idea.
Moreover, Elasticsearch repository contains documentation, not just code, while Solr keeps its documentation in a Wiki. This contributes to higher numbers for both commits and contributors for Elasticsearch. Elasticsearch is a bit easier to get started — a single download and a single command to get everything started. Solr has traditionally required a bit more work and knowledge, but Solr has recently made great strides to eliminate this and now just has to work on changing its reputation.
Operationally speaking, Elasticsearch is a bit simpler to work with — it has just a single process. ZooKeeper is super mature, super widely used, etc. That said, if you are using Hadoop, HBase, Spark, Kafka, or a number of other newer distributed software, you are likely already running ZooKeeper somewhere in your organization.
While Elasticsearch has built-in ZooKeeper-like component called Zen, ZooKeeper is better at preventing the dreaded split-brain problem sometimes seen in Elasticsearch clusters. To be fair, Elasticsearch developers are aware of this problem and have improved this aspect of Elasticsearch over the years.
Both have good commercial support consulting, production support, training, integrations, etc. Both have good operational tools around it, although Elasticsearch has, because of its easier-to-work-with API, attracted the DevOps crowd a lot more, thus enabling a livelier ecosystem of tools around it. In Solr, you need the managed-schema file former schema. Of course, you can have all fields defined as dynamic fields and create them on the fly, but you still need at least some degree of index configuration.
Elasticsearch is a bit different — it can be called schemaless. What exactly does this mean, you may ask. In short, it means one can launch Elasticsearch and start sending documents to it in order to have them indexed without creating any sort of index schema and Elasticsearch will try to guess field types.
Of course, you can also define the index structure so called mappings and then create the index with those mappings, or even create the mappings files for each type that will exist in the index and let Elasticsearch use it when a new index is created. Sounds pretty cool, right? In addition to that, when a new, previously unseen field is found in a document being indexed, Elasticsearch will try to create that field and will try to guess its type.
As you may imagine, this behavior can be turned off. In Solr, the configuration of all components, search handlers, index specific things such as merge factor or buffers, caches, etc. After each change you need to restart Solr node or reload it. All configs in Elasticsearch are written to elasticsearch. Learn more about this in Elasticsearch Shard Placement Control.
Another major difference between Elasticsearch and Solr is the node discovery and cluster management in general. This is one of the responsibilities of so-called node discovery. Elasticsearch uses its own discovery implementation called Zen that, for full fault tolerance i. Solr uses Apache ZooKeeper for discovery and leader election. Apache Solr uses a different approach for handling search cluster. Solr uses Apache ZooKeeper ensemble — which is basically one or more ZooKeeper instances running together.
ZooKeeper is used to store the configuration files and monitoring — for keeping track of the status of all nodes and of the overall cluster state. In order for a new node to join an existing cluster Solr needs to know which ZooKeeper ensemble to connect to.
Generally speaking, Elasticsearch is very dynamic as far as placement of indices and shards they are built of is concerned. It can move shards around the cluster when a certain action happens — for example when a new node joins or a node is removed from the cluster. Solr tends to be more static out of the box. However, with Solr 7 and later, we have the AutoScaling API: we can now define cluster-wide rules and collection-specific policies that control the shard placement, we can automatically add replicas and tell Solr to utilize node in the cluster based on the defined rules.
Those of you familiar with Solr know that in order to get search results from it you need to query one of the defined request handlers and pass in the parameters that define your query criteria. Depending on which query parser you choose to use, these parameters will be different, but the method is still the same — an HTTP GET request is sent to Solr in order to fetch search results.
The good thing is that you are not limited to a single response format — you may choose to get results in XML, in JSON in JavaBin format and several other formats that have response writers developed for them. You can thus choose the format that is the most convenient for you and your search application. Of course, Solr API is not only about querying as you can also get some statistics about different search components or control Solr behavior, such as collection creation for example.
This becomes a serious problem if you need to update your search index regularly. Solr just was not meant for real-time big-data search applications. The web applications today demand that new content generated by users be indexed in real time. The distributed nature of Elasticsearch allows it to keep up with concurrent search and index requests without skipping a beat.
The major area where Elasticsearch takes the stage is the distributed search. Elasticsearch, unlike Solr was built with distribution in mind, to be EC2-friendly. What it actually means is that Elasticsearch runs a search index on multiple servers, in a fail-safe and efficient way. Distributed systems are, in general, hard to program, but when done correctly such a system is resilient in the face of malice, degrades gracefully, and its security is far superior to the others.
Elasticsearch allows you to break indices into shards with one or more replicas. The shards are hosted in a data node within the cluster that delegates operations to the correct shards with rebalancing and routing done automatically. This ensures that even, in case of some catastrophic hardware or software failure, the chances of your search server going completely offline are close to none.
Elasticsearch provides a cloud support for amazon S3, as well as GigaSpaces, Coherence and Terracotta.
0コメント