While you use Amazon OpenSearch Service for time-bound information like server logs, service logs, utility logs, clickstreams, or occasion streams, storage value is likely one of the major drivers for the general value of your answer. Over the past 12 months, OpenSearch Service has launched options which have opened up new prospects for storing your log information in varied tiers, enabling you to commerce off information latency, sturdiness, and availability. In October 2023, OpenSearch Service introduced assist for im4gn information nodes, with NVMe SSD storage of as much as 30 TB. In November 2023, OpenSearch Service launched or1, the OpenSearch-optimized occasion household, which delivers as much as 30% price-performance enchancment over current situations in inner benchmarks and makes use of Amazon Easy Storage Service (Amazon S3) to offer 11 nines of sturdiness. Lastly, in Might 2024, OpenSearch Service introduced normal availability for Amazon OpenSearch Service zero-ETL integration with Amazon S3. These new options be part of OpenSearch’s current UltraWarm situations, which offer an as much as 90% discount in storage value per GB, and UltraWarm’s chilly storage choice, which helps you to detach UltraWarm indexes and durably retailer hardly ever accessed information in Amazon S3.
This put up works via an instance that will help you perceive the trade-offs out there in value, latency, throughput, information sturdiness and availability, retention, and information entry, as a way to select the suitable deployment to maximise the worth of your information and decrease the fee.
Look at your necessities
When designing your logging answer, you want a transparent definition of your necessities as a prerequisite to creating sensible trade-offs. Fastidiously look at your necessities for latency, sturdiness, availability, and price. Moreover, contemplate which information you select to ship to OpenSearch Service, how lengthy you keep information, and the way you propose to entry that information.
For the needs of this dialogue, we divide OpenSearch occasion storage into two lessons: ephemeral backed storage and Amazon S3 backed storage. The ephemeral backed storage class consists of OpenSearch nodes that use Nonvolatile Reminiscence Categorical SSDs (NVMe SSDs) and Amazon Elastic Block Retailer (Amazon EBS) volumes. The Amazon S3 backed storage class consists of UltraWarm nodes, UltraWarm chilly storage, or1 situations, and Amazon S3 storage you entry with the service’s zero-ETL with Amazon S3. When designing your logging answer, contemplate the next:
- Latency – should you want ends in milliseconds, then you will need to use ephemeral backed storage. If seconds or minutes are acceptable, you possibly can decrease your value by utilizing Amazon S3 backed storage.
- Throughput – As a normal rule, ephemeral backed storage situations will present greater throughput. Situations which have NVMe SSDs, just like the im4gn, usually present the perfect throughput, with EBS volumes offering good throughput. or1 situations benefit from Amazon EBS storage for major shards whereas utilizing Amazon S3 with phase replication to cut back the compute value of replication, thereby providing indexing throughput that may match and even exceed NVMe-based situations.
- Information sturdiness – Information saved within the sizzling tier (you deploy these as information nodes) has the bottom latency, and in addition the bottom sturdiness. OpenSearch Service supplies automated restoration of knowledge within the sizzling tier via replicas, which offer sturdiness with added value. Information that OpenSearch shops in Amazon S3 (UltraWarm, UltraWarm chilly storage, zero-ETL with Amazon S3, and or1 situations) will get the advantage of 11 nines of sturdiness from Amazon S3.
- Information availability – Finest practices dictate that you just use replicas for information in ephemeral backed storage. When you’ve a minimum of one reproduction, you possibly can proceed to entry all your information, even throughout a node failure. Nonetheless, every reproduction provides a a number of of value. In the event you can tolerate momentary unavailability, you possibly can cut back replicas via or1 situations, with Amazon S3 backed storage.
- Retention – Information in all storage tiers incurs value. The longer you keep information for evaluation, the extra cumulative value you incur for every GB of that information. Establish the utmost period of time you will need to retain information earlier than it loses all worth. In some instances, compliance necessities might limit your retention window.
- Information entry – Amazon S3 backed storage situations usually have a a lot greater storage to compute ratio, offering value financial savings however with inadequate compute for high-volume workloads. You probably have excessive question quantity or your queries span a big quantity of knowledge, ephemeral backed storage is the suitable selection. Direct question (Amazon S3 backed storage) is ideal for big quantity queries for occasionally queried information.
As you contemplate your necessities alongside these dimensions, your solutions will information your decisions for implementation. That will help you make trade-offs, we work via an prolonged instance within the following sections.
OpenSearch Service value mannequin
To know tips on how to value an OpenSearch Service deployment, you should perceive the fee dimensions. OpenSearch Service has two totally different deployment choices: managed clusters and serverless. This put up considers managed clusters solely, as a result of Amazon OpenSearch Serverless already tiers information and manages storage for you. While you use managed clusters, you configure information nodes, UltraWarm nodes, and cluster supervisor nodes, choosing Amazon Elastic Compute Cloud (Amazon EC2) occasion sorts for every of those capabilities. OpenSearch Service deploys and manages these nodes for you, offering OpenSearch and OpenSearch Dashboards via a REST endpoint. You possibly can select Amazon EBS backed situations or situations with NVMe SSD drives. OpenSearch Service fees an hourly value for the situations in your managed cluster. In the event you select Amazon EBS backed situations, the service will cost you for the storage provisioned, and any provisioned IOPs you configure. In the event you select or1 nodes, UltraWarm nodes, or UltraWarm chilly storage, OpenSearch Service fees for the Amazon S3 storage consumed. Lastly, the service fees for information transferred out.
Instance use case
We use an instance use case to look at the trade-offs in value and efficiency. The price and sizing of this instance are based mostly on greatest practices, and are directional in nature. Though you possibly can count on to see comparable financial savings, all workloads are distinctive and your precise prices might differ considerably from what we current on this put up.
For our use case, Fizzywig, a fictitious firm, is a big tender drink producer. They’ve many crops for producing their drinks, with copious logging from their manufacturing line. They began out small, with an all-hot deployment and producing 10 GB of logs day by day. At present, that has grown to three TB of log information day by day, and administration is mandating a discount in value. Fizzywig makes use of their log information for occasion debugging and evaluation, in addition to historic evaluation over one 12 months of log information. Let’s compute the price of storing and utilizing that information in OpenSearch Service.
Ephemeral backed storage deployments
Fizzywig’s present deployment is 189 r6g.12xlarge.search information nodes (no UltraWarm tier), with ephemeral backed storage. While you index information in OpenSearch Service, OpenSearch builds and shops index information buildings which can be normally about 10% bigger than the supply information, and you should go away 25% free space for storing for working overhead. Three TB of day by day supply information will use 4.125 TB of storage for the primary (major) copy, together with overhead. Fizzywig follows greatest practices, utilizing two reproduction copies for max information sturdiness and availability, with the OpenSearch Service Multi-AZ with Standby choice, rising the storage have to 12.375 TB per day. To retailer 1 12 months of knowledge, multiply by 12 months to get 4.5 PB of storage wanted.
To provision this a lot storage, they might additionally select im4gn.16xlarge.search situations, or or1.16.xlarge.search situations. The next desk provides the occasion counts for every of those occasion sorts, and with one, two, or three copies of the information.
| . | Max Storage (GB) per Node |
Major (1 Copy) |
Major + Reproduction (2 Copies) |
Major + 2 Replicas (3 Copies) |
| im4gn.16xlarge.search | 30,000 | 52 | 104 | 156 |
| or1.16xlarge.search | 36,000 | 42 | 84 | 126 |
| r6g.12xlarge.search | 24,000 | 63 | 126 | 189 |
The previous desk and the next dialogue are strictly based mostly on storage wants. or1 situations and im4gn situations each present greater throughput than r6g situations, which is able to cut back value additional. The quantity of compute saved varies between 10–40% relying on the workload and the occasion sort. These financial savings don’t cross straight via to the underside line; they require scaling and modification of the index and shard technique to completely notice them. The previous desk and subsequent calculations take the final assumption that these deployments are over-provisioned on compute, and are storage-bound. You’d see extra financial savings for or1 and im4gn, in contrast with r6g, should you needed to scale greater for compute.
The next desk represents the entire cluster prices for the three totally different occasion sorts throughout the three totally different information storage sizes specified. These are based mostly on on-demand US East (N. Virginia) AWS Area prices and embrace occasion hours, Amazon S3 value for the or1 situations, and Amazon EBS storage prices for the or1 and r6g situations.
| . |
Major (1 Copy) |
Major + Reproduction (2 Copies) |
Major + 2 Replicas (3 Copies) |
| im4gn.16xlarge.search | $3,977,145 | $7,954,290 | $11,931,435 |
| or1.16xlarge.search | $4,691,952 | $9,354,996 | $14,018,041 |
| r6g.12xlarge.search | $4,420,585 | $8,841,170 | $13,261,755 |
This desk provides you the one-copy, two-copy, and three-copy prices (together with Amazon S3 and Amazon EBS prices, the place relevant) for this 4.5 PB workload. For this put up, “one copy” refers back to the first copy of your information, with the replication issue set to zero. “Two copies” features a reproduction copy of the entire information, and “three copies” features a major and two replicas. As you possibly can see, every reproduction provides a a number of of value to the answer. In fact, every reproduction provides availability and sturdiness to the information. With one copy (major solely), you’d lose information within the case of a single node outage (with an exception for or1 situations). With one reproduction, you would possibly lose some or all information in a two-node outage. With two replicas, you could possibly lose information solely in a three-node outage.
The or1 situations are an exception to this rule. or1 situations can assist a one-copy deployment. These situations use Amazon S3 as a backing retailer, writing all index information to Amazon S3, as a method of replication, and for sturdiness. As a result of all acknowledged writes are persevered in Amazon S3, you possibly can run with a single copy, however with the chance of shedding availability of your information in case of a node outage. If an information node turns into unavailable, any impacted indexes might be unavailable (purple) through the restoration window (normally 10–20 minutes). Fastidiously consider whether or not you possibly can tolerate this unavailability along with your clients in addition to your system (for instance, your ingestion pipeline buffer). In that case, you possibly can drop your value from $14 million to $4.7 million based mostly on the one-copy (major) column illustrated within the previous desk.
Reserved Situations
OpenSearch Service helps Reserved Situations (RIs), with 1-year and 3-year phrases, with no up-front value (NURI), partial up-front value (PURI), or all up-front value (AURI). All reserved occasion commitments decrease value, with 3-year, all up-front RIs offering the deepest low cost. Making use of a 3-year AURI low cost, annual prices for Fizzywig’s workload provides prices as proven within the following desk.
| . | Major | Major + Reproduction | Major + 2 Replicas |
| im4gn.16xlarge.search | $1,909,076 | $3,818,152 | $5,727,228 |
| or1.16xlarge.search | $3,413,371 | $6,826,742 | $10,240,113 |
| r6g.12xlarge.search | $3,268,074 | $6,536,148 | $9,804,222 |
RIs present a simple option to save value, with no code or structure modifications. Adopting RIs for this workload brings the im4gn value for 3 copies right down to $5.7 million, and the one-copy value for or1 situations right down to $3.2 million.
Amazon S3 backed storage deployments
The previous deployments are helpful as a baseline and for comparability. In reality, you’d select one of many Amazon S3 backed storage choices to maintain prices manageable.
OpenSearch Service UltraWarm situations retailer all information in Amazon S3, utilizing UltraWarm nodes as a sizzling cache on prime of this full dataset. UltraWarm works greatest for interactive querying of knowledge in small time-bound slices, equivalent to operating a number of queries in opposition to 1 day of knowledge from 6 months in the past. Consider your entry patterns rigorously and contemplate whether or not UltraWarm’s cache-like habits will serve you properly. UltraWarm first-query latency scales with the quantity of knowledge you should question.
When designing an OpenSearch Service area for UltraWarm, you should determine in your sizzling retention window and your heat retention window. Most OpenSearch Service clients use a sizzling retention window that varies between 7–14 days, with heat retention making up the remainder of the total retention interval. For our Fizzywig situation, we use 14 days sizzling retention and 351 days of UltraWarm retention. We additionally use a two-copy (major and one reproduction) deployment within the sizzling tier.
The 14-day, sizzling storage want (based mostly on a day by day ingestion fee of 4.125 TB) is 115.5 TB. You possibly can deploy six situations of any of the three occasion sorts to assist this indexing and storage. UltraWarm shops a single reproduction in Amazon S3, and doesn’t want extra storage overhead, making your 351-day storage want 1.158 PiB. You possibly can assist this with 58 UltraWarm1.giant.search situations. The next desk provides the entire value for this deployment, with 3-year AURIs for the recent tier. The or1 situations’ Amazon S3 value is rolled into the S3 column.
| . | Scorching | UltraWarm | S3 | Whole |
| im4gn.16xlarge.search | $220,278 | $1,361,654 | $333,590 | $1,915,523 |
| or1.16xlarge.search | $337,696 | $1,361,654 | $418,136 | $2,117,487 |
| r6g.12xlarge.search | $270,410 | $1,361,654 | $333,590 | $1,965,655 |
You possibly can additional cut back the fee by shifting information to UltraWarm chilly storage. Chilly storage reduces value by lowering availability of the information—to question the information, you will need to problem an API name to reattach the goal indexes to the UltraWarm tier. A typical sample for 1 12 months of knowledge retains 14 days sizzling, 76 days in UltraWarm, and 275 days in chilly storage. Following this sample, you utilize 6 sizzling nodes and 13 UltraWarm1.giant.search nodes. The next desk illustrates the fee to run Fizzywig’s 3 TB day by day workload. The or1 value for Amazon S3 utilization is rolled into the UltraWarm nodes + S3 column.
| . | Scorching | UltraWarm nodes + S3 | Chilly | Whole |
| im4gn.16xlarge.search | $220,278 | $377,429 | $261,360 | $859,067 |
| or1.16xlarge.search | $337,696 | $461,975 | $261,360 | $1,061,031 |
| r6g.12xlarge.search | $270,410 | $377,429 | $261,360 | $909,199 |
By using Amazon S3 backed storage choices, you’re capable of cut back value even additional, with a single-copy or1 deployment at $337,000, and a most of $1 million yearly with or1 situations.
OpenSearch Service zero-ETL for Amazon S3
While you use OpenSearch Service zero-ETL for Amazon S3, you retain all of your secondary and older information in Amazon S3. Secondary information is the higher-volume information that has decrease worth for direct inspection, equivalent to VPC Circulate Logs and WAF logs. For these deployments, you retain nearly all of occasionally queried information in Amazon S3, and solely the newest information in your sizzling tier. In some instances, you pattern your secondary information, maintaining a proportion within the sizzling tier as properly. Fizzywig decides that they need to have 7 days of all of their information within the sizzling tier. They’ll entry the remainder with direct question (DQ).
While you use direct question, you possibly can retailer your information in JSON, Parquet, and CSV codecs. Parquet format is perfect for direct question and supplies about 75% compression on the information. Fizzywig is utilizing Amazon OpenSearch Ingestion, which may write Parquet format information on to Amazon S3. Their 3 TB of day by day supply information compresses to 750 GB of day by day Parquet information. OpenSearch Service maintains a pool of compute models for direct question. You might be billed hourly for these OpenSearch Compute Items (OCUs), scaling based mostly on the quantity of knowledge you entry. For this dialog, we assume that Fizzywig could have some debugging classes and run 50 queries day by day over sooner or later value of knowledge (750 GB). The next desk summarizes the annual value to run Fizzywig’s 3 TB day by day workload, 7 days sizzling, 358 days in Amazon S3.
| . | Scorching | DQ Value | OR1 S3 | Uncooked Information S3 | Whole |
| im4gn.16xlarge.search | $220,278 | $2,195 | $0 | $65,772 | $288,245 |
| or1.16xlarge.search | $337,696 | $2,195 | $84,546 | $65,772 | $490,209 |
| r6g.12xlarge.search | $270,410 | $2,195 | $0 | $65,772 | $338,377 |
That’s fairly a journey! Fizzywig’s value for logging has come down from as excessive as $14 million yearly to as little as $288,000 yearly utilizing direct question with zero-ETL from Amazon S3. That’s a financial savings of 4,800%!
Sampling and compression
On this put up, we now have checked out one information footprint to allow you to deal with information measurement, and the trade-offs you may make relying on the way you need to entry that information. OpenSearch has extra options that may additional change the economics by lowering the quantity of knowledge you retailer.
For logs workloads, you possibly can make use of OpenSearch Ingestion sampling to cut back the scale of knowledge you ship to OpenSearch Service. Sampling is acceptable when your information as a complete has statistical traits the place an element will be consultant of the entire. For instance, should you’re operating an observability workload, you possibly can usually ship as little as 10% of your information to get a consultant sampling of the traces of request dealing with in your system.
You possibly can additional make use of a compression algorithm to your workloads. OpenSearch Service lately launched assist for Zstandard (zstd) compression that may deliver greater compression charges and decrease decompression latencies as in comparison with the default, greatest compression.
Conclusion
With OpenSearch Service, Fizzywig was capable of stability value, latency, throughput, sturdiness and availability, information retention, and most popular entry patterns. They had been capable of save 4,800% for his or her logging answer, and administration was thrilled.
Throughout the board, im4gn comes out with the bottom absolute greenback quantities. Nonetheless, there are a few caveats. First, or1 situations can present greater throughput, particularly for write-intensive workloads. This may increasingly imply extra financial savings via decreased want for compute. Moreover, with or1’s added sturdiness, you possibly can keep availability and sturdiness with decrease replication, and subsequently decrease value. One other issue to think about is RAM; the r6g situations present extra RAM, which accelerates queries for decrease latency. When coupled with UltraWarm, and with totally different sizzling/heat/chilly ratios, r6g situations can be a wonderful selection.
Do you’ve a high-volume, logging workload? Have you ever benefitted from some or all of those strategies? Tell us!
Concerning the Creator
Jon Handler is a Senior Principal Options Architect at Amazon Internet Providers based mostly in Palo Alto, CA. Jon works intently with OpenSearch and Amazon OpenSearch Service, offering assist and steerage to a broad vary of shoppers who’ve vector, search, and log analytics workloads that they need to transfer to the AWS Cloud. Previous to becoming a member of AWS, Jon’s profession as a software program developer included 4 years of coding a large-scale, ecommerce search engine. Jon holds a Bachelor’s of the Arts from the College of Pennsylvania, and a Grasp’s of Science and a PhD in Laptop Science and Synthetic Intelligence from Northwestern College.