10 Elasticsearch Architecture Best Practices

Elasticsearch is a powerful tool that can help you to index and search large amounts of data very quickly. However, it is important to design your Elasticsearch architecture in a way that will ensure good performance and reliability.

In this article, we will discuss 10 best practices for designing an Elasticsearch architecture. By following these best practices, you can be sure that your Elasticsearch implementation will be scalable, reliable, and performant.

1. Use dedicated master nodes

Elasticsearch is a distributed system, which means that it relies on multiple nodes working together to provide a single, cohesive service. One of the key components of this distribution is the master node. The master node is responsible for managing the cluster, including tasks like adding and removing nodes, tracking node health, and allocating shards (the individual pieces of data that make up an index).

Because the master node plays such a critical role in the functioning of the cluster, it’s important to ensure that it has the resources it needs to do its job effectively. This means giving it its own dedicated server, with plenty of CPU, memory, and storage. It’s also important to configure the network so that the master node has a low latency connection to the other nodes in the cluster.

By following these best practices, you can be sure that your Elasticsearch cluster is well-equipped to handle the demands of your applications.

2. Don’t use a single node cluster

If you only have one node, and that node goes down for any reason, your entire Elasticsearch cluster will go down with it. This is obviously not ideal, as it leads to downtime and data loss.

A single node cluster is also less performant than a multi-node cluster, as it can’t take advantage of the distributed nature of Elasticsearch.

So, if you’re serious about using Elasticsearch, make sure to set up a multi-node cluster. It’s more resilient and will provide better performance in the long run.

3. Set up your data nodes to have enough memory for both the JVM heap and filesystem cache

The JVM heap is where Elasticsearch stores all of its data structures. The filesystem cache is used by the operating system to cache frequently accessed files from disk. If you don’t have enough memory for both, then your node will start swapping to disk, which will severely impact performance.

To avoid this, make sure you allocate at least 32GB of RAM for each data node. If you can afford it, 64GB is even better.

4. Configure shard allocation awareness

Shard allocation awareness prevents primary and replica shards from being allocated on the same node. This is important because if a node goes down, all of the shards on that node will be unavailable. By configuring shard allocation awareness, you can ensure that your data is always available, even in the event of a node failure.

To configure shard allocation awareness, simply add the following setting to your elasticsearch.yml file:

cluster.routing.allocation.awareness.attributes: rack_id

This will ensure that your shards are allocated across different racks, so if one rack goes down, your data will still be accessible.

5. Disable swapping on all Elasticsearch nodes

When Elasticsearch is indexing or searching data, it uses a lot of memory. If the system doesn’t have enough memory available, the operating system will start swapping memory to disk, which severely degrades performance. In fact, even if there’s just a little bit of swapping, it can still cause problems.

To avoid this, make sure you have plenty of memory available on your system, and that you’ve disabled swapping. You can do this by adding the following line to /etc/sysctl.conf:

vm.swappiness=0

And then reboot your system for the change to take effect.

6. Monitor open file descriptors

Open file descriptors are a measure of the number of files and other resources that a process has open at any given time. If a process doesn’t have enough open file descriptors, it can run into problems because it won’t be able to access the files it needs. This can lead to all sorts of issues, from slow performance to data loss.

To avoid these problems, it’s important to monitor open file descriptors so you can identify when a process is running into trouble. There are a few different ways to do this, but one of the simplest is to use the lsof command. This will list all of the open files for a given process, which will give you a good idea of how many file descriptors it is using.

If you see that a process is using a lot of file descriptors, you may need to increase the number of open files allowed for that process. This can usually be done by editing the /etc/security/limits.conf file.

7. Tune Linux settings for optimal performance

The Linux kernel is a complex beast with many settings that can be tuned to improve performance. Some of these settings are specific to Elasticsearch, while others are more general and can be applied to any type of workload.

Applying the wrong settings can have negative consequences, so it’s important to understand what each setting does before changing it. The best way to do this is to consult the documentation for your particular version of Elasticsearch.

Once you’ve decided which settings to change, you can use the sysctl command to modify them on the fly. You can also add them to /etc/sysctl.conf to make them persistent across reboots.

8. Use SSDs for best indexing performance

While hard disk drives (HDDs) are less expensive per gigabyte than solid state drives (SSDs), they are also much slower. This is because HDDs rely on spinning disks that need to be accessed sequentially in order to read or write data, while SSDs have no moving parts and can therefore access data much faster.

If you’re using Elasticsearch for indexing purposes, it’s important to use SSDs in order to get the best performance. Not only will this improve the speed of your indexing operations, but it will also reduce the load on your CPU and memory, which can help improve overall system performance.

9. Make sure you have enough storage space

If you don’t have enough storage space, your Elasticsearch nodes can get into a “split brain” situation. This happens when the primary node goes down and the replica nodes take over, but they can’t communicate with each other because they’re on different sides of a network partition. As a result, you can end up with two clusters that can’t share data, which is obviously not ideal.

To avoid this, you need to make sure you have at least 50% more storage space than you need for your data. So, if you have 10GB of data, you should have at least 15GB of storage space.

10. Use hot/warm architecture

A hot/warm architecture is a type of data storage architecture in which data is divided into two categories: hot and warm. Hot data is the data that is most frequently accessed and is typically stored on faster, more expensive storage devices. Warm data is data that is less frequently accessed and is typically stored on slower, less expensive storage devices.

The advantage of using a hot/warm architecture is that it can help improve performance and reduce costs. By storing hot data on faster, more expensive storage devices, you can improve performance because access to hot data will be faster. And by storing warm data on slower, less expensive storage devices, you can reduce costs because you won’t be paying for the speed that you don’t need.