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Once you use Amazon OpenSearch Service for time-bound information like server logs, service logs, software logs, clickstreams, or occasion streams, storage value is without doubt one of the main drivers for the general value of your resolution. During the last yr, OpenSearch Service has launched options which have opened up new potentialities 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 cases in inside benchmarks and makes use of Amazon Easy Storage Service (Amazon S3) to offer 11 nines of sturdiness. Lastly, in Could 2024, OpenSearch Service introduced common availability for Amazon OpenSearch Service zero-ETL integration with Amazon S3. These new options be part of OpenSearch’s current UltraWarm cases, which give an as much as 90% discount in storage value per GB, and UltraWarm’s chilly storage possibility, which helps you to detach UltraWarm indexes and durably retailer not often accessed information in Amazon S3.
This put up works by an instance that can assist you perceive the trade-offs accessible in value, latency, throughput, information sturdiness and availability, retention, and information entry, in an effort to select the correct deployment to maximise the worth of your information and decrease the associated fee.
Look at your necessities
When designing your logging resolution, 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 contains OpenSearch nodes that use Nonvolatile Reminiscence Specific SSDs (NVMe SSDs) and Amazon Elastic Block Retailer (Amazon EBS) volumes. The Amazon S3 backed storage class contains UltraWarm nodes, UltraWarm chilly storage, or1 cases, and Amazon S3 storage you entry with the service’s zero-ETL with Amazon S3. When designing your logging resolution, contemplate the next:
- Latency – if you happen to 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 through the use of Amazon S3 backed storage.
- Throughput – As a common rule, ephemeral backed storage cases will present larger throughput. Cases which have NVMe SSDs, just like the im4gn, typically present the perfect throughput, with EBS volumes offering good throughput. or1 cases make the most of Amazon EBS storage for main shards whereas utilizing Amazon S3 with section replication to scale back the compute value of replication, thereby providing indexing throughput that may match and even exceed NVMe-based cases.
- 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 offers automated restoration of knowledge within the sizzling tier by replicas, which give sturdiness with added value. Information that OpenSearch shops in Amazon S3 (UltraWarm, UltraWarm chilly storage, zero-ETL with Amazon S3, and or1 cases) will get the good thing about 11 nines of sturdiness from Amazon S3.
- Information availability – Greatest practices dictate that you just use replicas for information in ephemeral backed storage. When you have got at the least one duplicate, you possibly can proceed to entry your whole information, even throughout a node failure. Nonetheless, every duplicate provides a a number of of value. When you can tolerate non permanent unavailability, you possibly can cut back replicas by or1 cases, 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 circumstances, compliance necessities might prohibit your retention window.
- Information entry – Amazon S3 backed storage cases typically have a a lot larger 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 correct selection. Direct question (Amazon S3 backed storage) is ideal for big quantity queries for sometimes queried information.
As you contemplate your necessities alongside these dimensions, your solutions will information your decisions for implementation. That can assist you make trade-offs, we work by an prolonged instance within the following sections.
OpenSearch Service value mannequin
To grasp learn how to value an OpenSearch Service deployment, it is advisable perceive the associated 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. Once you use managed clusters, you configure information nodes, UltraWarm nodes, and cluster supervisor nodes, deciding on Amazon Elastic Compute Cloud (Amazon EC2) occasion sorts for every of those features. OpenSearch Service deploys and manages these nodes for you, offering OpenSearch and OpenSearch Dashboards by a REST endpoint. You possibly can select Amazon EBS backed cases or cases with NVMe SSD drives. OpenSearch Service prices an hourly value for the cases in your managed cluster. When you select Amazon EBS backed cases, the service will cost you for the storage provisioned, and any provisioned IOPs you configure. When you select or1 nodes, UltraWarm nodes, or UltraWarm chilly storage, OpenSearch Service prices for the Amazon S3 storage consumed. Lastly, the service prices for information transferred out.
Instance use case
We use an instance use case to look at the trade-offs in value and efficiency. The fee and sizing of this instance are based mostly on greatest practices, and are directional in nature. Though you possibly can anticipate to see related financial savings, all workloads are distinctive and your precise prices might fluctuate considerably from what we current on this put up.
For our use case, Fizzywig, a fictitious firm, is a big comfortable drink producer. They’ve many vegetation 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 every day. At this time, that has grown to three TB of log information every 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 yr 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. Once you index information in OpenSearch Service, OpenSearch builds and shops index information constructions which are often about 10% bigger than the supply information, and it is advisable go away 25% free space for storing for working overhead. Three TB of every day supply information will use 4.125 TB of storage for the primary (main) copy, together with overhead. Fizzywig follows greatest practices, utilizing two duplicate copies for max information sturdiness and availability, with the OpenSearch Service Multi-AZ with Standby possibility, growing the storage must 12.375 TB per day. To retailer 1 yr 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 cases, or or1.16.xlarge.search cases. 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 cases and im4gn cases each present larger throughput than r6g cases, which can 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 by to the underside line; they require scaling and modification of the index and shard technique to totally understand them. The previous desk and subsequent calculations take the overall assumption that these deployments are over-provisioned on compute, and are storage-bound. You’ll see extra financial savings for or1 and im4gn, in contrast with r6g, if you happen to needed to scale larger 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 cases, and Amazon EBS storage prices for the or1 and r6g cases.
| . |
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 duplicate copy of all the information, and “three copies” features a main and two replicas. As you possibly can see, every duplicate provides a a number of of value to the answer. After all, every duplicate provides availability and sturdiness to the information. With one copy (main solely), you’ll lose information within the case of a single node outage (with an exception for or1 cases). With one duplicate, you may lose some or all information in a two-node outage. With two replicas, you would lose information solely in a three-node outage.
The or1 cases are an exception to this rule. or1 cases can assist a one-copy deployment. These cases use Amazon S3 as a backing retailer, writing all index information to Amazon S3, as a way of replication, and for sturdiness. As a result of all acknowledged writes are endured in Amazon S3, you possibly can run with a single copy, however with the danger of dropping availability of your information in case of a node outage. If a knowledge node turns into unavailable, any impacted indexes will probably be unavailable (pink) through the restoration window (often 10–20 minutes). Fastidiously consider whether or not you possibly can tolerate this unavailability together with your prospects in addition to your system (for instance, your ingestion pipeline buffer). If that’s the case, you possibly can drop your value from $14 million to $4.7 million based mostly on the one-copy (main) column illustrated within the previous desk.
Reserved Cases
OpenSearch Service helps Reserved Cases (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 an easy method 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 cases right down to $3.2 million.
Amazon S3 backed storage deployments
The previous deployments are helpful as a baseline and for comparability. Actually, you’ll select one of many Amazon S3 backed storage choices to maintain prices manageable.
OpenSearch Service UltraWarm cases 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, reminiscent of operating a number of queries towards 1 day of knowledge from 6 months in the past. Consider your entry patterns rigorously and contemplate whether or not UltraWarm’s cache-like conduct will serve you nicely. UltraWarm first-query latency scales with the quantity of knowledge it is advisable question.
When designing an OpenSearch Service area for UltraWarm, it is advisable resolve in your sizzling retention window and your heat retention window. Most OpenSearch Service prospects 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 state of affairs, we use 14 days sizzling retention and 351 days of UltraWarm retention. We additionally use a two-copy (main and one duplicate) deployment within the sizzling tier.
The 14-day, sizzling storage want (based mostly on a every day ingestion fee of 4.125 TB) is 115.5 TB. You possibly can deploy six cases of any of the three occasion sorts to assist this indexing and storage. UltraWarm shops a single duplicate in Amazon S3, and doesn’t want further storage overhead, making your 351-day storage want 1.158 PiB. You possibly can assist this with 58 UltraWarm1.massive.search cases. The next desk provides the entire value for this deployment, with 3-year AURIs for the new tier. The or1 cases’ Amazon S3 value is rolled into the S3 column.
| . | Sizzling | UltraWarm | S3 | Complete |
| 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 associated 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 difficulty an API name to reattach the goal indexes to the UltraWarm tier. A typical sample for 1 yr 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.massive.search nodes. The next desk illustrates the associated fee to run Fizzywig’s 3 TB every day workload. The or1 value for Amazon S3 utilization is rolled into the UltraWarm nodes + S3 column.
| . | Sizzling | UltraWarm nodes + S3 | Chilly | Complete |
| 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 cases.
OpenSearch Service zero-ETL for Amazon S3
Once 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, reminiscent of VPC Move Logs and WAF logs. For these deployments, you retain nearly all of sometimes queried information in Amazon S3, and solely the newest information in your sizzling tier. In some circumstances, you pattern your secondary information, holding a share within the sizzling tier as nicely. Fizzywig decides that they need to have 7 days of all of their information within the sizzling tier. They are going to entry the remaining with direct question (DQ).
Once you use direct question, you possibly can retailer your information in JSON, Parquet, and CSV codecs. Parquet format is perfect for direct question and offers about 75% compression on the information. Fizzywig is utilizing Amazon OpenSearch Ingestion, which might write Parquet format information on to Amazon S3. Their 3 TB of every day supply information compresses to 750 GB of every day Parquet information. OpenSearch Service maintains a pool of compute items for direct question. You’re 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 may have some debugging periods and run 50 queries every day over someday value of knowledge (750 GB). The next desk summarizes the annual value to run Fizzywig’s 3 TB every day workload, 7 days sizzling, 358 days in Amazon S3.
| . | Sizzling | DQ Price | OR1 S3 | Uncooked Information S3 | Complete |
| 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’ve checked out one information footprint to allow you to deal with information dimension, and the trade-offs you may make relying on the way you need to entry that information. OpenSearch has further 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 scale back the scale of knowledge you ship to OpenSearch Service. Sampling is suitable when your information as an entire has statistical traits the place a component will be consultant of the entire. For instance, if you happen to’re operating an observability workload, you possibly can typically 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 in your workloads. OpenSearch Service just lately launched assist for Zstandard (zstd) compression that may deliver larger compression charges and decrease decompression latencies as in comparison with the default, greatest compression.
Conclusion
With OpenSearch Service, Fizzywig was capable of steadiness value, latency, throughput, sturdiness and availability, information retention, and most well-liked entry patterns. They had been capable of save 4,800% for his or her logging resolution, and administration was thrilled.
Throughout the board, im4gn comes out with the bottom absolute greenback quantities. Nonetheless, there are a few caveats. First, or1 cases can present larger throughput, particularly for write-intensive workloads. This will likely imply further financial savings by diminished want for compute. Moreover, with or1’s added sturdiness, you possibly can keep availability and sturdiness with decrease replication, and due to this fact decrease value. One other issue to think about is RAM; the r6g cases present further RAM, which quickens queries for decrease latency. When coupled with UltraWarm, and with totally different sizzling/heat/chilly ratios, r6g cases can be a superb selection.
Do you have got a high-volume, logging workload? Have you ever benefitted from some or all of those strategies? Tell us!
In regards to the Creator
Jon Handler is a Senior Principal Options Architect at Amazon Net Providers based mostly in Palo Alto, CA. Jon works carefully with OpenSearch and Amazon OpenSearch Service, offering assist and steering to a broad vary of consumers 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 Pc Science and Synthetic Intelligence from Northwestern College.
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