You are currently viewing A Information to DynamoDB Secondary Indexes: GSI, LSI, Elasticsearch and Rockset – how to decide on the correct indexing technique

A Information to DynamoDB Secondary Indexes: GSI, LSI, Elasticsearch and Rockset – how to decide on the correct indexing technique

Many improvement groups flip to DynamoDB for constructing event-driven architectures and user-friendly, performant purposes at scale. As an operational database, DynamoDB is optimized for real-time transactions even when deployed throughout a number of geographic areas. Nonetheless, it doesn’t present robust efficiency for analytics workloads.

Analytics on DynamoDB

Whereas NoSQL databases like DynamoDB typically have wonderful scaling traits, they help solely a restricted set of operations which can be centered on on-line transaction processing. This makes it tough to develop analytics straight on them.

DynamoDB shops knowledge below the hood by partitioning it over a lot of nodes primarily based on a user-specified partition key discipline current in every merchandise. This user-specified partition key will be optionally mixed with a form key to signify a major key. The first key acts as an index, making question operations cheap. A question operation can do equality comparisons (=)
on the partition key and comparative operations (>, <, =, BETWEEN) on the kind key if specified.

Performing analytical queries not coated by the above scheme requires the usage of a scan operation, which is usually executed by scanning over your complete DynamoDB desk in parallel. These scans will be sluggish and costly by way of learn throughput as a result of they require a full learn of your complete desk. Scans additionally are likely to decelerate when the desk measurement grows, as there may be
extra knowledge to scan to supply outcomes. If we wish to help analytical queries with out encountering prohibitive scan prices, we are able to leverage secondary indexes, which we are going to focus on subsequent.

Indexing in DynamoDB

In DynamoDB, secondary indexes are sometimes used to enhance utility efficiency by indexing fields which can be queried steadily. Question operations on secondary indexes will also be used to energy particular options by means of analytic queries which have clearly outlined necessities.

Secondary indexes consist of making partition keys and non-compulsory kind keys over fields that we wish to question. There are two varieties of secondary indexes:

  • Native secondary indexes (LSIs): LSIs prolong the hash and vary key attributes for a single partition.
  • International secondary indexes (GSIs): GSIs are indexes which can be utilized to a complete desk as a substitute of a single partition.

Nonetheless, as Nike found, overusing GSIs in DynamoDB will be costly. Analytics in DynamoDB, until they’re used just for quite simple level lookups or small vary scans, may end up in overuse of secondary indexes and excessive prices.

The prices for provisioned capability when utilizing indexes can add up shortly as a result of all updates to the bottom desk should be made within the corresponding GSIs as properly. In truth, AWS advises that the provisioned write capability for a world secondary index ought to be equal to or larger than the write capability of the bottom desk to keep away from throttling writes to the bottom desk and crippling the appliance. The price of provisioned write capability grows linearly with the variety of GSIs configured, making it price prohibitive to make use of many GSIs to help many entry patterns.

DynamoDB can be not well-designed to index knowledge in nested constructions, together with arrays and objects. Earlier than indexing the info, customers might want to denormalize the info, flattening the nested objects and arrays. This might tremendously improve the variety of writes and related prices.

For a extra detailed examination of utilizing DynamoDB secondary indexes for analytics, see our weblog Secondary Indexes For Analytics On DynamoDB.

The underside line is that for analytical use circumstances, you’ll be able to achieve important efficiency and value benefits by syncing the DynamoDB desk with a unique software or service that acts as an exterior secondary index for operating advanced analytics effectively.

DynamoDB + Elasticsearch


One method to constructing a secondary index over our knowledge is to make use of DynamoDB with Elasticsearch. Cloud-based Elasticsearch, resembling Elastic Cloud or Amazon OpenSearch Service, can be utilized to provision and configure nodes in response to the scale of the indexes, replication, and different necessities. A managed cluster requires some operations to improve, safe, and preserve performant, however much less so than operating it completely by your self on EC2 situations.


Because the method utilizing the Logstash Plugin for Amazon DynamoDB is unsupported and moderately tough to arrange, we are able to as a substitute stream writes from DynamoDB into Elasticsearch utilizing DynamoDB Streams and an AWS Lambda operate. This method requires us to carry out two separate steps:

  • We first create a lambda operate that’s invoked on the DynamoDB stream to submit every replace because it happens in DynamoDB into Elasticsearch.
  • We then create a lambda operate (or EC2 occasion operating a script if it should take longer than the lambda execution timeout) to submit all the present contents of DynamoDB into Elasticsearch.

We should write and wire up each of those lambda capabilities with the right permissions with a view to make sure that we don’t miss any writes into our tables. When they’re arrange together with required monitoring, we are able to obtain paperwork in Elasticsearch from DynamoDB and may use Elasticsearch to run analytical queries on the info.

The benefit of this method is that Elasticsearch helps full-text indexing and a number of other varieties of analytical queries. Elasticsearch helps shoppers in numerous languages and instruments like Kibana for visualization that may assist shortly construct dashboards. When a cluster is configured accurately, question latencies will be tuned for quick analytical queries over knowledge flowing into Elasticsearch.

Disadvantages embody that the setup and upkeep price of the answer will be excessive. Even managed Elasticsearch requires coping with replication, resharding, index development, and efficiency tuning of the underlying situations.

Elasticsearch has a tightly coupled structure that doesn’t separate compute and storage. This implies sources are sometimes overprovisioned as a result of they can’t be independently scaled. As well as, a number of workloads, resembling reads and writes, will contend for a similar compute sources.

Elasticsearch additionally can’t deal with updates effectively. Updating any discipline will set off a reindexing of your complete doc. Elasticsearch paperwork are immutable, so any replace requires a brand new doc to be listed and the outdated model marked deleted. This ends in extra compute and I/O expended to reindex even the unchanged fields and to jot down total paperwork upon replace.

As a result of lambdas fireplace after they see an replace within the DynamoDB stream, they will have have latency spikes attributable to chilly begins. The setup requires metrics and monitoring to make sure that it’s accurately processing occasions from the DynamoDB stream and capable of write into Elasticsearch.

Functionally, by way of analytical queries, Elasticsearch lacks help for joins, that are helpful for advanced analytical queries that contain multiple index. Elasticsearch customers typically should denormalize knowledge, carry out application-side joins, or use nested objects or parent-child relationships to get round this limitation.


  • Full-text search help
  • Help for a number of varieties of analytical queries
  • Can work over the most recent knowledge in DynamoDB


  • Requires administration and monitoring of infrastructure for ingesting, indexing, replication, and sharding
  • Tightly coupled structure ends in useful resource overprovisioning and compute rivalry
  • Inefficient updates
  • Requires separate system to make sure knowledge integrity and consistency between DynamoDB and Elasticsearch
  • No help for joins between completely different indexes

This method can work properly when implementing full-text search over the info in DynamoDB and dashboards utilizing Kibana. Nonetheless, the operations required to tune and keep an Elasticsearch cluster in manufacturing, its inefficient use of sources and lack of be part of capabilities will be difficult.

DynamoDB + Rockset


Rockset is a completely managed search and analytics database constructed primarily to help real-time purposes with excessive QPS necessities. It’s typically used as an exterior secondary index for knowledge from OLTP databases.

Rockset has a built-in connector with DynamoDB that can be utilized to maintain knowledge in sync between DynamoDB and Rockset. We will specify the DynamoDB desk we wish to sync contents from and a Rockset assortment that indexes the desk. Rockset indexes the contents of the DynamoDB desk in a full snapshot after which syncs new adjustments as they happen. The contents of the Rockset assortment are all the time in sync with the DynamoDB supply; no quite a lot of seconds aside in regular state.


Rockset manages the info integrity and consistency between the DynamoDB desk and the Rockset assortment routinely by monitoring the state of the stream and offering visibility into the streaming adjustments from DynamoDB.


With out a schema definition, a Rockset assortment can routinely adapt when fields are added/eliminated, or when the construction/sort of the info itself adjustments in DynamoDB. That is made doable by robust dynamic typing and sensible schemas that obviate the necessity for any extra ETL.

The Rockset assortment we sourced from DynamoDB helps SQL for querying and will be simply utilized by builders with out having to be taught a domain-specific language. It will also be used to serve queries to purposes over a REST API or utilizing shopper libraries in a number of programming languages. The superset of ANSI SQL that Rockset helps can work natively on deeply nested JSON arrays and objects, and leverage indexes which can be routinely constructed over all fields, to get millisecond latencies on even advanced analytical queries.

Rockset has pioneered compute-compute separation, which permits isolation of workloads in separate compute items whereas sharing the identical underlying real-time knowledge. This provides customers larger useful resource effectivity when supporting simultaneous ingestion and queries or a number of purposes on the identical knowledge set.

As well as, Rockset takes care of safety, encryption of information, and role-based entry management for managing entry to it. Rockset customers can keep away from the necessity for ETL by leveraging ingest transformations we are able to arrange in Rockset to switch the info because it arrives into a group. Customers may also optionally handle the lifecycle of the info by organising retention insurance policies to routinely purge older knowledge. Each knowledge ingestion and question serving are routinely managed, which lets us concentrate on constructing and deploying reside dashboards and purposes whereas eradicating the necessity for infrastructure administration and operations.

Particularly related in relation to syncing with DynamoDB, Rockset helps in-place field-level updates, in order to keep away from pricey reindexing.


  • Constructed to ship excessive QPS and serve real-time purposes
  • Utterly serverless. No operations or provisioning of infrastructure or database required
  • Compute-compute separation for predictable efficiency and environment friendly useful resource utilization
  • Stay sync between DynamoDB and the Rockset assortment, in order that they’re by no means quite a lot of seconds aside
  • Monitoring to make sure consistency between DynamoDB and Rockset
  • Computerized indexes constructed over the info enabling low-latency queries
  • In-place updates that avoids costly reindexing and lowers knowledge latency
  • Joins with knowledge from different sources resembling Amazon Kinesis, Apache Kafka, Amazon S3, and many others.

We will use Rockset for implementing real-time analytics over the info in DynamoDB with none operational, scaling, or upkeep issues. This may considerably pace up the event of real-time purposes. If you would like to construct your utility on DynamoDB knowledge utilizing Rockset, you may get began without cost on right here.

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