Apache Solr is an open-source enterprise search server.
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Elasticsearch
Score 8.7 out of 10
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Elasticsearch is an enterprise search tool from Elastic in Mountain View, California.
$16
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Apache Solr and Elasticsearch are both open-source enterprise search software solutions that allow users to search and retrieve data within an organization. Both software options integrate with tools like databases or intranets where information can be collected or displayed. Businesses of all sizes use both Apache Solr and Elasticsearch.
Features
Apache Solr and Elasticsearch both provide essential enterprise search features, including data retrieval and display. Despite this, both software options have a few standout features that set them apart from each other.
Apache Solr offers robust text search features that allow users to search for materials by their content. Apache Solr has many contributors to its open-source code. Developers and code committers for Apache Solr are selected from that community of contributors. This approach to development means bugfixes and updates are frequent, and features can be developed quickly. Lastly, Apache Solr provides detailed documentation for developers, including multiple examples.
Elasticsearch is lightweight to the extent that a business can install and run the Elasticsearch in a matter of minutes. Similarly, Elasticsearch configuration is based on JSON, which makes file configuration simple, if a little inflexible in terms of documentation. JSON compatibility also makes Elasticsearch a great choice when working with JSON applications. Elasticsearch focuses on complex querying and filtering, though it also offers basic text search. Lastly, Elasticsearch is designed for the cloud and supports clustering, leading to a highly scalable option.
Limitations
Though Apache Solr and Elasticsearch have robust sets of features, they both have a few limitations that are important to consider.
Apache Solr offers text search features but is limited when it comes to more complex querying and filtering. Lack of complex querying can make Apache Solr a poor choice for applications that need non-text search features. Additionally, Apache Solr is a heavier software option compared to Elasticsearch, which can make installation more challenging for lightweight applications.
Elasticsearch is open-source in that all users have access to the source code. However, unlike many open-source technologies, all changes to the code must be approved by Elastic developers. As a result, Elasticsearch provides the financial benefits of open-source software but doesn’t offer the same level of community development as Apache Solr. Additionally, though Elastisearch provides complex search features, its text search features are more limited compared to Apache Solr.
Pricing
Apache Solr and Elasticsearch are both open-source technologies, meaning their source code is available for free. Despite this, both software options also have vendors that provide cloud hosting services. Pricing for Apache Solr and Elasticsearch is dependent on factors such as the vendor, support needs, and amount of indexed nodes. Apache Solr pricing usually starts around $10.00 per month, while Elasticsearch starts around $16.00 per month.
Very effective for end-user searching applications and for generating search results. Also very well suited to those looking for high reliability and performance. If [you're doing] fuzzy searching or if you are working on a smaller end-user application or an internal application that does not require high performance and flexible/adapting searching then it may not be necessary to use Solr.
Elasticsearch is really well suited for searching text (Natural Language Processing) and you can fine tune the searches and scoring very well. I like the ability to find Significant Terms in the Index, where you can find aggregations that are really relevant to a specific search. It also allows for queries to lead to new queries via aggregations which is great for navigating your data. It is less suited to doing more complex aggregations where slices of data are required to be processing using guassian normalizations. And doing searches which join different documents is very very hard, and requires serious thought on how to denormalize data.
Faceted navigation and field collapsing/grouping : filtering and quick results were what we needed for our websites. Our customers needed to have this functionalities for good and efficient results.
We tested them with our customers' registered searches (they received all new goods matching with their registered searches by emails and/or mobile push). Results were incredible by comparison with our old system (old MySQL requests).
Note : we didn't put all our data in Solr. Just what we need for searching uses. Other data stayed in our MySQL database.
Auto-suggest : our old auto-suggest wasn't performing well. With Apache Solr, our new one was worked really well ! The suggestions came quickly and suggestions were good.
We also extended auto-suggestion with geo-spatial data and it worked well.
Hit highlighting : we used this functionality and we didn't have problem and nasty surprise.
Keep all data status during data upgrading (see next details for improvements)
Setting Java memory thresholds can be a pain for those not accustomed to things like Eden Space & Old Generation which can lead to over allocation, or more likely, under allocation. Apache Solr had a similar issue. It would be nice if the program would take an extra step and dogfood it's own advice by analyzing the system & processes to return a solid recommendation for that configuration. The proper configuration information is outlined in the documentation, it would be nice if that was automated.
The only health check that ElasticSearch reports back is a "red" status without any real solid information about what is going on, though its usually memory thresholds or disk I/O. I am currently on ElasticSearch 1.5 so that may have changed for newer versions. When the status goes "red", I as the administrator of the software, feel like I lose control of whats going on which should rarely happen. Something more verbose would eliminate that.
This is more of a critique of the ElasticStack in general. The whole top to bottom stack is starting to get feature creep with things that are better suited in other software and increasing the barrier for entry for people to get started with setting up a robust logging infrastructure. ElasticSearch as a storage search engine, is pretty streamlined, but I can see that the tools that comprise the ELK Stack are going to require a certification with constant study at some point. During major release for Logstash a while back, it literally took a month to learn a new language because Elastic completely changed the syntax. For a medium sized organization of only a couple of admins, that is a pretty high bar where time is money. They really should work on refining/automating the tools & search engine they have, instead of shoehorning/changing things on to an already rock solid foundation.
It takes some time to deploy and currectly maintein it. And also, to learn how to use and integrate in the enviroment as well. Once you get theses steps done, it usability is very simple, and almost of the time it don't require no further attention on it. Even for maintence, if you deploy it on a cluster mode, it is very reliable and easy to take one host down.
To get started with Elasticsearch, you don't have to get very involved in configuring what really is an incredibly complex system under the hood. You simply install the package, run the service, and you're immediately able to begin using it. You don't need to learn any sort of query language to add data to Elasticsearch or perform some basic searching. If you're used to any sort of RESTful API, getting started with Elasticsearch is a breeze. If you've never interacted with a RESTful API directly, the journey may be a little more bumpy. Overall, though, it's incredibly simple to use for what it's doing under the covers.
We've only used it as an opensource tooling. We did not purchase any additional support to roll out the elasticsearch software. When rolling out the application on our platform we've used the documentation which was available online. During our test phases we did not experience any bugs or issues so we did not rely on support at all.
We switched from search indexes stored in MySQL to soar and it's made a world of difference for our growing businesses. The relational databases are very poor for handling the complex data searches require and Solr delivered all the tools we need to get the performance our end users are demanding.
Elasticsearch is the most well-known and supported free data platform that we identified. We are taking advantage of community knowledge and practices. In terms of flexibility and breadth of use cases no other competitor came close to Elasticsearch. We've tried Solr in the past be we encountered issues which were deal-breaking for us. MongoDB - it just did not pass our evaluation parameters as a main data platform. We still use it for smaller purposes, though.
It's enabled us to deliver fast, relevant search results on our new website. The site is still in beta and being actively developed so our complete ROI is still unknown.
It integrates very well with Drupal so it has saved us from having to develop a custom solution.
I am not in finance and I suspect even if I was this would be hard to measure. But for sure, Elasticsearch has enabled us to have the most flexible data model in the industry for our customer's data, and in doing so we have attracted many many technical customers and got much of their $$$.
One problem with Elasticsearch is that because it runs on the JVM, there can be some stop-the-world JVM garbage collections happening that can take down nodes and reduce indexing speed. The solution for that tends to be "let's just upgrade the CPU on that machine". And before you know it you are paying $$$ because this'll happen with 40+ machines.
On the other hand, I do think that ES is more efficient than other systems and so it requires fewer nodes to keep it highly tolerant and available, so we probably saved some money that way.
