For enterprise applications and startups to scale, they need to manage large volumes of data in real-time. Customers must have the ability to search for any product or service from your database within seconds. When you manage a relational database, data is spread across multiple tables. So, customers may experience lag during search and data retrieval. However, this is different with Elasticsearch and other NoSQL databases. In this article, we’ll look at Elasticsearch, the features that make Elasticsearch a scalable search engine, and those that make it not scalable for all use cases to see.
Elasticsearch is an open-source document-oriented search and analytics engine built on apache lucene. It is highly distributed, allowing users to store, search, and analyze large volumes of unstructured, semi-structured, structured, numerical, and textual data in near real-time. Unlike relational databases that are slower with querying large data sets, Elasticsearch stores data in JSON format and uses Lucene StandardAnalyzer for indexing.
Since it has a distributed search system architecture, Elasticsearch can increase to multiple servers and store petabytes of data. Querying is still fast because it searches an index rather than text. This is like using the index of a book instead of scanning for a particular word in every page of the book. Learning how to use Elasticsearch is quite easy because of its user-friendly interface.
Docker, Dell, The Guardian and many other large corporations use Elasticsearch and it has been featured on the top tech websites over the years. Let’s explore the major reasons why Elasticsearch is so popular.
Elasticsearch numerous search features makes it easier to find what you’re searching for. Even in the case of a spelling error, the fuzzy search feature makes it possible to get a result. With autocompletion and instant search functionality, users are able to see relevant results as they type. This happens via prediction based on search history, suggestion of existing tags, etc.
Elasticsearch is able to run searches based on language and return relevant data that match search conditions. It can perform faceted searches, customized text splitting, full-text searches, and more.
As you have seen above, the full-text search capabilities of Elasticsearch makes it a speedier option when compared with relational databases. Rather than storing data in rows and columns, Elasticsearch stores data in the form of objects. It stores complex entities as a structured JSON object and is able to index all fields.
This makes querying faster because objects can be connected in complex structures that can’t be achieved with rows and columns in relational databases. It’s a better option for analyzing logs since it can execute complex processes based on relevance to queries and return results in real time.
Unlike traditional SQL databases that may require performance tuning for faster executions, Elasticsearch is able to perform queries in real-time. It will take an SQL database more than ten seconds to retrieve search query data, but Elasticsearch can do this within microseconds. It’s able to cache structured queries that are used as filters for particular search results. This makes it easy to execute only once and refer to the cache for any other request that is in a cached filter.
Elasticsearch is able to function across many nodes at the same time. It can split indices into shards with multiple replicas across available nodes. This makes it faster to execute queries because data can be retrieved from relevant shards simultaneously. Even when new documents are added, routing and rebalancing can still be done automatically. All of these actions are built-in for users to have great experiences.
To make the most use of Elasticsearch, your team must be able to use this tool. This is even more important in use cases that concern enterprise applications. If you decide to use Elasticsearch, you must acknowledge the time and resources that will be spent on learning Elasticsearch or hiring those who are Elasticsearch experts.
Depending on data volume and use case, your team must be able to handle various configurations in Elasticsearch. Your team should know how to configure appropriate heap sizes, node types, adequate number of shards for an index in a cluster, and more.
Although Elasticsearch can handle multiple nodes, as you scale, it requires adequate hardware to perform at optimal speed and capacity. Elasticsearch performs best on a group of servers that are 64GB of RAM each. So, any capacity less than that may lead to memory troubles.
If you decide to use too many small servers, it could create too much overhead. On the other hand, if you use a few powerful servers, failovers can still happen.
Nonetheless, queries execute faster on data stored in SSDs than rotating disks. So, your infrastructure can be quite expensive since SSDs are costlier. As the system scales, you’ll have to manage terabytes or petabytes of data and keep fine-tuning your Elasticsearch infrastructure.
Fine-tuning is a problem that is more common with enterprise applications. When dealing with less data, it’s easier to fine-tune clusters manually. But as the database becomes enormous, you may encounter management overhead. You’ll need to organize data and infrastructure to deliver at scale and get the most out of Elasticsearch. For seamless querying, you must have an organized hierarchy of indexes, types etc. Nodes must be healthy with the adequate number of replica shards and so much more.
Stackify’s Application Performance Management tool, Retrace, can help with fine-tuning clusters with Errors, Logs, and APM data. Use Retrace to understand how Elasticsearch is performing and affecting app performance and user satisfaction. With Retrace, collect and monitor key server and app metrics, aggregate Elasticsearch app logs for viewing and searching, and collect, summarize, and monitor all Elasticsearch HTTP requests. Try your free two week trial of Retrace.
Elasticsearch offers a wide range of benefits to organizations that want to deliver improved customer experiences. As a distributed search engine, it offers nearly real-time search features and caters for multiple needs of specific business and operational use cases. Although companies, like GoDaddy, use Elasticsearch to analyze billions of events and logs, it may not be a scalable option for a startup team that is looking to acquire their first users or customers.