Choosing the right database is a critical choice when building any software application. All databases have different strengths and weaknesses when it comes to performance, so deciding which database has the most benefits and the most minor downsides for your specific use case and data model is an important decision. Below you will find an overview of the key concepts, architecture, features, use cases, and pricing models of AWS DynamoDB and Elasticsearch so you can quickly see how they compare against each other.
The primary purpose of this article is to compare how AWS DynamoDB and Elasticsearch perform for workloads involving time series data, not for all possible use cases. Time series data typically presents a unique challenge in terms of database performance. This is due to the high volume of data being written and the query patterns to access that data. This article doesn’t intend to make the case for which database is better; it simply provides an overview of each database so you can make an informed decision.
AWS DynamoDB vs Elasticsearch Breakdown
Key-value and document store
Distributed search and analytics engine, document-oriented
DynamoDB is a fully managed, serverless NoSQL database provided by Amazon Web Services (AWS). It uses a single-digit millisecond latency for high-performance use cases and supports both key-value and document data models. Data is partitioned and replicated across multiple availability zones within an AWS region, and DynamoDB supports eventual or strong consistency for read operations
Elasticsearch is built on top of Apache Lucene and uses a RESTful API for communication. It stores data in a flexible JSON document format, and the data is automatically indexed for fast search and retrieval. Elasticsearch can be deployed as a single node, in a cluster configuration, or as a managed cloud service (Elastic Cloud)
Serverless web applications, real-time bidding platforms, gaming leaderboards, IoT data management, high-velocity data processing
Full-text search, log and event data analysis, real-time application monitoring, analytics
Automatically scales to handle large amounts of read and write throughput, supports on-demand capacity and auto-scaling, global tables for multi-region replication
Horizontally scalable with support for data sharding, replication, and distributed querying
AWS DynamoDB Overview
Amazon DynamoDB is a managed NoSQL database service provided by AWS. It was first introduced in 2012, and it was designed to provide low-latency, high-throughput performance. DynamoDB is built on the principles of the Dynamo paper, which was published by Amazon engineers in 2007, and it aims to offer a highly available, scalable, and distributed key-value store.
Elasticsearch is an open-source distributed search and analytics engine built on top of Apache Lucene. It was first released in 2010 and has since become popular for its scalability, near real-time search capabilities, and ease of use. Elasticsearch is designed to handle a wide variety of data types, including structured, unstructured, and time-based data. It is often used in conjunction with other tools from the Elastic Stack, such as Logstash for data ingestion and Kibana for data visualization.
AWS DynamoDB for Time Series Data
DynamoDB can be used with time series data, although it may not be the most optimized solution compared to specialized time series databases. To store time series data in DynamoDB, you can use a composite primary key with a partition key for the entity identifier and a sort key for the timestamp. This allows you to efficiently query data for a specific entity and time range. However, DynamoDB’s main weakness when dealing with time series data is its lack of built-in support for data aggregation and downsampling, which are common requirements for time series analysis. You may need to perform these operations in your application or use additional services like AWS Lambda to process the data.
Elasticsearch for Time Series Data
Elasticsearch can be used for time series data storage and analysis, thanks to its distributed architecture, near real-time search capabilities, and support for aggregations. However, it might not be as optimized for time series data as dedicated time series databases. Despite this, Elasticsearch is widely used for log and event data storage and analysis which can be considered time series data.
AWS DynamoDB Key Concepts
Some of the key terms and concepts specific to DynamoDB include:
- Tables: In DynamoDB, data is stored in tables, which are containers for items. Each table has a primary key that uniquely identifies each item in the table.
- Items: Items are individual records in a DynamoDB table, and they consist of one or more attributes.
- Attributes: Attributes are key-value pairs that make up an item in a table. DynamoDB supports scalar, document, and set data types for attributes.
- Primary Key: The primary key uniquely identifies each item in a table, and it can be either a single-attribute partition key or a composite partition-sort key.
Elasticsearch Key Concepts
- Inverted Index: A data structure used by Elasticsearch to enable fast and efficient full-text searches.
- Cluster: A group of Elasticsearch nodes that work together to distribute data and processing tasks.
- Shard: A partition of an Elasticsearch index that allows data to be distributed across multiple nodes for improved performance and fault tolerance.
AWS DynamoDB Architecture
DynamoDB is a NoSQL database that uses a key-value store and document data model. It is designed to provide high availability, durability, and scalability by automatically partitioning data across multiple servers and using replication to ensure fault tolerance. Some of the main components of DynamoDB include:
- Partitioning: DynamoDB automatically partitions data based on the partition key, which ensures that data is evenly distributed across multiple storage nodes.
- Replication: DynamoDB replicates data across multiple availability zones within an AWS region, providing high availability and durability.
- Consistency: DynamoDB offers two consistency models: eventual consistency and strong consistency, allowing you to choose the appropriate level of consistency for your application.
Elasticsearch is a distributed, RESTful search and analytics engine that uses a schema-free JSON document data model. It is built on top of Apache Lucene and provides a high-level API for indexing, searching, and analyzing data. Elasticsearch’s architecture is designed to be horizontally scalable, with data distributed across multiple nodes in a cluster. Data is indexed using inverted indices, which enable fast and efficient full-text searches.
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AWS DynamoDB Features
DynamoDB can automatically scale its read and write capacity based on the workload, allowing you to maintain consistent performance without over-provisioning resources.
Backup and restore
DynamoDB provides built-in support for point-in-time recovery, enabling you to restore your table to a previous state within the last 35 days.
DynamoDB global tables enable you to replicate your table across multiple AWS regions, providing low-latency access and data redundancy for global applications.
DynamoDB Streams capture item-level modifications in your table and can be used to trigger AWS Lambda functions for real-time processing or to synchronize data with other AWS services.
Elasticsearch provides powerful full-text search capabilities with support for complex queries, scoring, and relevance ranking.
Elasticsearch’s distributed architecture enables horizontal scalability, allowing it to handle large volumes of data and high query loads.
Elasticsearch supports various aggregation operations, such as sum, average, and percentiles, which are useful for analyzing and summarizing data.
AWS DynamoDB Use Cases
DynamoDB can be used to store session data for web applications, providing fast and scalable access to session information.
DynamoDB can be used to store player data, game state, and other game-related information for online games, providing low-latency and high-throughput performance.
Internet of Things
DynamoDB can be used to store and process sensor data from IoT devices, enabling real-time monitoring and analysis of device data.
Elasticsearch Use Cases
Log and Event Data Analysis
Elasticsearch is widely used for storing and analyzing log and event data, such as web server logs, application logs, and network events, to help identify patterns, troubleshoot issues, and monitor system performance.
Elasticsearch is a popular choice for implementing full-text search functionality in applications, websites, and content management systems due to its powerful search capabilities and flexible data model.
Elasticsearch, in combination with other Elastic Stack components, can be used for security analytics, such as monitoring network traffic, detecting anomalies, and identifying potential threats.
AWS DynamoDB Pricing Model
DynamoDB offers two pricing options: provisioned capacity and on-demand capacity. With provisioned capacity, you specify the number of reads and writes per second that you expect your application to require, and you are charged based on the amount of provisioned capacity. This pricing model is suitable for applications with predictable traffic or gradually ramping traffic. You can use auto scaling to adjust your table’s capacity automatically based on the specified utilization rate, ensuring application performance while reducing costs.
On the other hand, with on-demand capacity, you pay per request for the data reads and writes your application performs on your tables. You do not need to specify how much read and write throughput you expect your application to perform, as DynamoDB instantly accommodates your workloads as they ramp up or down. This pricing model is suitable for applications with fluctuating or unpredictable traffic patterns.
Elasticsearch Pricing Model
Elasticsearch is open-source software and can be self-hosted without any licensing fees. However, operational costs, such as hardware, hosting, and maintenance, should be considered. Elasticsearch also offers a managed cloud service called Elastic Cloud, which provides various pricing tiers based on factors like storage, computing resources, and support. Elastic Cloud includes additional features and tools, such as Kibana, machine learning, and security features.
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