Optimize storage prices in Amazon OpenSearch Service utilizing Zstandard compression

This publish is co-written with Praveen Nischal, Mulugeta Mammo, and Akash Shankaran from Intel.

Amazon OpenSearch Service is a managed service that makes it simple to safe, deploy, and function OpenSearch clusters at scale within the AWS Cloud. In an OpenSearch Service area, the information is managed within the type of indexes. Primarily based on the utilization sample, an OpenSearch cluster could have a number of indexes, and their shards are unfold throughout the information nodes within the cluster. Every information node has a hard and fast disk dimension and the disk utilization depends on the variety of index shards saved on the node. Every index shard could occupy completely different sizes primarily based on its variety of paperwork. Along with the variety of paperwork, one of many essential elements that decide the scale of the index shard is the compression technique used for an index.

As a part of an indexing operation, the ingested paperwork are saved as immutable segments. Every phase is a group of assorted information buildings, equivalent to inverted index, block Okay dimensional tree (BKD), time period dictionary, or saved fields, and these information buildings are answerable for retrieving the doc quicker through the search operation. Out of those information buildings, saved fields, that are largest fields within the phase, are compressed when saved on the disk and primarily based on the compression technique used, the compression velocity and the index storage dimension will fluctuate.

On this publish, we talk about the efficiency of the Zstandard algorithm, which was launched in OpenSearch v2.9, amongst different out there compression algorithms in OpenSearch.

Significance of compression in OpenSearch

Compression performs an important position in OpenSearch, as a result of it considerably impacts the efficiency, storage effectivity and total usability of the platform. The next are some key causes highlighting the significance of compression in OpenSearch:

  1. Storage effectivity and value financial savings OpenSearch usually offers with huge volumes of information, together with log recordsdata, paperwork, and analytics datasets. Compression methods cut back the scale of information on disk, resulting in substantial value financial savings, particularly in cloud-based and/or distributed environments.
  2. Decreased I/O operations Compression reduces the variety of I/O operations required to learn or write information. Fewer I/O operations translate into diminished disk I/O, which is significant for enhancing total system efficiency and useful resource utilization.
  3. Environmental impression By minimizing the storage necessities and diminished I/O operations, compression contributes to a discount in vitality consumption and a smaller carbon footprint, which aligns with sustainability and environmental objectives.

When configuring OpenSearch, it’s important to think about compression settings rigorously to strike the correct steadiness between storage effectivity and question efficiency, relying in your particular use case and useful resource constraints.

Core ideas

Earlier than diving into varied compression algorithms that OpenSearch provides, let’s look into three commonplace metrics which can be usually used whereas evaluating compression algorithms:

  1. Compression ratio The unique dimension of the enter in contrast with the compressed information, expressed as a ratio of 1.0 or higher
  2. Compression velocity The velocity at which information is made smaller (compressed), expressed in MBps of enter information consumed
  3. Decompression velocity The velocity at which the unique information is reconstructed from the compressed information, expressed in MBps

Index codecs

OpenSearch offers assist for codecs that can be utilized for compressing the saved fields. Till OpenSearch 2.7, OpenSearch offered two codecs or compression methods: LZ4 and Zlib. LZ4 is analogous to best_speed as a result of it offers quicker compression however a lesser compression ratio (consumes extra disk area) when in comparison with Zlib. LZ4 is used because the default compression algorithm if no express codec is specified throughout index creation and is most well-liked by most as a result of it offers quicker indexing and search speeds although it consumes comparatively extra space than Zlib. Zlib is analogous to best_compression as a result of it offers a greater compression ratio (consumes much less disk area) when in comparison with LZ4, nevertheless it takes extra time to compress and decompress, and due to this fact has larger latencies for indexing and search operations. Each LZ4 and Zlib codecs are a part of the Lucene core codecs.

Zstandard codec

The Zstandard codec was launched in OpenSearch as an experimental feature in version 2.7, and it offers Zstandard-based compression and decompression APIs. The Zstandard codec relies on JNI binding to the Zstd native library.

Zstandard is a quick, lossless compression algorithm aimed toward offering a compression ratio akin to Zlib however with quicker compression and decompression velocity akin to LZ4. The Zstandard compression algorithm is offered in two completely different modes in OpenSearch: zstd and zstd_no_dict. For extra particulars, see Index codecs.

Each codec modes intention to steadiness compression ratio, index, and search throughput. The zstd_no_dict choice excludes a dictionary for compression on the expense of barely bigger index sizes.

With the latest OpenSearch 2.9 release, the Zstandard codec has been promoted from experimental to mainline, making it appropriate for manufacturing use instances.

Create an index with the Zstd codec

You need to use the index.codec throughout index creation to create an index with the Zstd codec. The next is an instance utilizing the curl command (this command requires the person to have vital privileges to create an index):

# Creating an index
curl -XPUT "http://localhost:9200/your_index" -H 'Content material-Sort: utility/json' -d'
  "settings": {
    "index.codec": "zstd"

Zstandard compression ranges

With Zstandard codecs, you’ll be able to optionally specify a compression degree utilizing the index.codec.compression_level setting, as proven within the following code. This setting takes integers within the [1, 6] vary. The next compression degree ends in the next compression ratio (smaller storage dimension) with a trade-off in velocity (slower compression and decompression speeds result in larger indexing and search latencies). For extra particulars, see Choosing a codec.

# Creating an index
curl -XPUT "http://localhost:9200/your_index" -H 'Content material-Sort: utility/json' -d'
  "settings": {
    "index.codec": "zstd",
    "index.codec.compression_level": 2

Replace an index codec setting

You’ll be able to replace the index.codec and index.codec.compression_level settings any time after the index is created. For the brand new configuration to take impact, the index must be closed and reopened.

You’ll be able to replace the setting of an index utilizing a PUT request. The next is an instance utilizing curl instructions.

Shut the index:

# Shut the index 
curl -XPOST "http://localhost:9200/your_index/_close"

Replace the index settings:

# Replace the index.codec and codec.compression_level setting
curl -XPUT "http://localhost:9200/your_index/_settings" -H 'Content material-Sort: utility/json' -d' 
  "index": {
    "codec": "zstd_no_dict", 
    "codec.compression_level": 3 

Reopen the index:

# Reopen the index
curl -XPOST "http://localhost:9200/your_index/_open"

Altering the index codec settings doesn’t instantly have an effect on the scale of present segments. Solely new segments created after the replace will mirror the brand new codec setting. To have constant phase sizes and compression ratios, it could be essential to carry out a reindexing or different indexing processes like merges.

Benchmarking compression efficiency of compression in OpenSearch

To know the efficiency advantages of Zstandard codecs, we carried out a benchmark train.


The server setup was as follows:

  1. Benchmarking was carried out on an OpenSearch cluster with a single information node which acts as each information and coordinator node and with a devoted cluster_manager node.
  2. The occasion sort for the information node was r5.2xlarge and the cluster_manager node was r5.xlarge, each backed by an Amazon Elastic Block Retailer (Amazon EBS) quantity of sort GP3 and dimension 100GB.

Benchmarking was arrange as follows:

  1. The benchmark was run on a single node of sort c5.4xlarge (sufficiently massive to keep away from hitting client-side useful resource constraints) backed by an EBS quantity of sort GP3 and dimension 500GB.
  2. The variety of shoppers was 16 and bulk dimension was 1024
  3. The workload was nyc_taxis

The index setup was as follows:

  1. Variety of shards: 1
  2. Variety of replicas: 0


From the experiments, zstd offers a greater compression ratio in comparison with Zlib (best_compression) with a slight achieve in write throughput and with related learn latency as LZ4 (best_speed). zstd_no_dict offers 14% higher write throughput than LZ4 (best_speed) and a barely decrease compression ratio than Zlib (best_compression).

The next desk summarizes the benchmark outcomes.


Though Zstd offers the perfect of each worlds (compression ratio and compression velocity), it has the next limitations:

  1. Sure queries that fetch all the saved fields for all of the matching paperwork could observe a rise in latency. For extra info, see Changing an index codec.
  2. You’ll be able to’t use the zstd and zstd_no_dict compression codecs for k-NN or Security Analytics indexes.


Zstandard compression offers steadiness between storage dimension and compression velocity, and is ready to tune the extent of compression primarily based on the use case. Intel and the OpenSearch Service crew collaborated on including Zstandard as one of many compression algorithms in OpenSearch. Intel contributed by designing and implementing the preliminary model of compression plugin in open-source which was launched in OpenSearch v2.7 as experimental function. OpenSearch Service crew labored on additional enhancements, validated the efficiency outcomes and built-in it into the OpenSearch server codebase the place it was launched in OpenSearch v2.9 as a usually out there function.

In the event you would wish to contribute to OpenSearch, create a GitHub issue and share your concepts with us. We’d even be enthusiastic about studying about your expertise with Zstandard in OpenSearch Service. Please be at liberty to ask extra questions within the feedback part.

In regards to the Authors

Praveen Nischal is a Cloud Software program Engineer, and leads the cloud workload efficiency framework at Intel.

Mulugeta Mammo is a Senior Software program Engineer, and presently leads the OpenSearch Optimization crew at Intel.

Akash Shankaran is a Software program Architect and Tech Lead within the Xeon software program crew at Intel. He works on pathfinding alternatives, and enabling optimizations for information companies equivalent to OpenSearch.

Sarthak Aggarwal is a Software program Engineer at Amazon OpenSearch Service. He has been contributing in direction of open-source growth with indexing and storage efficiency as a major space of curiosity.

Prabhakar Sithanandam is a Principal Engineer with Amazon OpenSearch Service. He primarily works on the scalability and efficiency features of OpenSearch.

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