How to Solve Elasticsearch Performance and Scaling Problems?

There is a software platform called Elasticsearch oriented on search and analytics of the large flows of the data which is an open-source and has recently gained widespread.

Yet, as data volumes and consumers increase and technologies are adopted, enterprises encounter performance and scalability issues with Elasticsearch implementations. In this article, they will discuss some of the familiar performance and scalable issues and offer recommendations on how to deal with them.

Cause: Slow indexing or query response times can be attributed to factors such as inefficient mappings, inappropriate analysis configurations, or resource constraints.

Solutions

• Optimize mappings and analysis configurations for your data and use cases.
• Implement field data filtering to reduce memory overhead.
• Use field data cache and query cache responsibly as they can help in boosting Elasticsearch’s performance. To optimize the query response time, more resources (CPU, memory, and disk I/O) should be assigned to the Elasticsearch nodes.
• Increase resources (CPU, memory, disk I/O) allocated to Elasticsearch nodes.

Cause: As data volumes grow, a single Elasticsearch cluster may no longer suffice, leading to scaling and rebalancing issues.

Solutions

• Implement horizontal scaling by adding more nodes to the cluster.
• Configure shard allocation awareness to distribute shards across nodes efficiently.
• Use dedicated master nodes to handle cluster management tasks more effectively.
• Leverage Elasticsearch’s cross-cluster replication for scaling across multiple clusters.

Cause: Elasticsearch stores data and indexing structures on disk, leading to potential disk space issues as data volumes grow.

Solutions

• Implement index lifecycle management policies to manage index retention and deletion.
• Leverage Elasticsearch’s built-in compression and compaction mechanisms.
• Consider using cloud storage solutions or remote repositories for backup and archiving.
• Implement data tiering strategies by moving cold data to lower-cost storage tiers.

Cause: High network traffic or inefficient cluster communication can lead to performance degradation and instability.

Solutions

• When building or fine-tuning an application, one must bear in mind that many properties, such as the TCP retransmission settings or the size of thread pools, can be fine-tuned to different prescriptive values depending on the environment into which the application is deployed.
• Place the nodes of Elasticsearch in the same rack, within the same data center, or at least within the same availability zone to avoid unnecessary network overhead.
• Implement query patterns that minimize data transfer between nodes (e.g., cross-cluster search, remote clusters).

Cause: Lack of proper monitoring and observability can make it difficult to identify and troubleshoot performance and scaling issues.

Solutions

• The monitoring solution for Elasticsearch may be taken from Elasticsearch itself; for instance, there is Marvel or X-Pack Monitoring or any other third-party tool.
• Implement centralized logging and log analysis solutions for Elasticsearch logs.
• Establish performance baselines and alerting mechanisms for early detection of issues.

To increase search speed in Elasticsearch, you can consider the following points:

• Shard and Index Sizing: Effective indexing and shard sizing are crucial. Aim for fewer large shards, ideally between 20GB and 40GB, rather than numerous small ones to minimize overhead. Avoid creating overly large indices; instead, split data into smaller, logically grouped indices, such as one per year or month.
• Field Data Types: Make sure you understand the field data types and use the appropriate one for your data. For instance, use structured data types for the fields that will be used in filtering and sorting, such as the “keyword” option, while using “text” for full-text search fields. Any field that does not require analysis should not at all be analyzed.
• Field Mappings: This is particularly important because mappings often fail, and, when they do, it is better to find out during development than in a live environment. For instance, for fields that do not require it, one could disable norms and docValues because they minimize memory and enhance the speed of searching.
• Caching: Caching is one of the powerful tuning parameters you should enable and tune the several caching mechanisms in Elasticsearch these are field data cache, query cache, and the request cache. For repetitive queries, caching can go a long way in improving efficiency and performance is a critical factor in this aspect.
• Pagination and Scrolling: When the result set being returned is huge use the most appropriate pagination or scrolling methodology to avoid the nature of a single large query overwhelming the cluster.
• Querying Techniques: Implement the intelligent query system rules like filtering before querying, prefix queries auto-complete, avoid using wildcards in querying.
• Hardware Resources: This is particularly important for big production environments; make sure your Elasticsearch cluster has enough CPU, RAM, and fast disks (SSDs are preferred). Elasticsearch is a relatively expensive service in terms of CPU and memory utilization and having enough resources at its disposal can help a lot.
• Cluster Health and Monitoring: Ensure that you have the aspects of your Elasticsearch cluster in check such as disk usage, heap usage, or other as may be required. This will help keep the system in check to ensure it is always running optimally by addressing any issues as they arise.

Regarding shard and index sizing for scale, here are some general guidelines:

• Shard Sizing: As mentioned above to avoid overhead, it is desirable to shoot for fewer but larger shards, ideally around 20 to 40 GB per shard. Although the ability to scale the database is built into the Gertrude platform, the precise shard size that works best may differ from one database to another based on its data and patterns of usage.
• Number of Shards: Ensure that the number of shards created is reasonable (for instance, 5-10 shards per index as a prudent start) and scale up: horizontally by adding nodes to the cluster. This means that it is very easy to scale or adjust the number of shards later, but this is costly.
• Index Sizing: To avoid bloating of indexes and to manage indexes afresh, do not keep large portions of data in a single index but split the data logically and partition them (for example one index per year or month. It also helps in managing and scaling individual indexes in order the intended goal.
• Replication: Set the number of replicas that you want to set based on your availability and resilience needs of your database. For there to be more replicas, there is more redundancy, but there is also more usage of the resource.
• Monitoring and Adjustments: It is recommended that you watch your cluster performance and rebalance the shards and/or indices from time to time. There are some tools as well as APIs available in Elasticsearch for monitoring and managing the shards and indexes.

Addressing Elasticsearch performance and scaling challenges requires a combination of architectural considerations, configuration optimizations, and monitoring practices. By implementing the solutions outlined above, organizations can ensure their Elasticsearch deployments remain performant and scalable as data volumes and usage patterns evolve.