Elasticsearch vs SingleStore
Compare and contrast Elasticsearch and SingleStore by architecture, ingestion, queries, performance, and scalability.
Elasticsearch Architecture vs SingleStore
Elasticsearch is an open-source distributed search engine built on Apache Lucene, a full text search library. Elasticsearch is a distributed system, which means that it is designed to operate across multiple nodes, each responsible for a part of the data.
SingleStore is a proprietary distributed relational database that handles both transactional and analytical workloads. It relies on memory and a persistent cache to deliver low latency queries. For longer term data retention, SingleStore Cloud separates compute from cloud object storage. SingleStore Cloud pricing is based on compute and storage usage.
Elasticsearch Ingestion vs SingleStore
Elasticsearch has a number of integrations as well as a REST API. It is a NoSQL database and natively supports semi-structured data. Transformations typically occur upstream so that data can be modeled for optimal performance before it is indexed in Elasticsearch.
SingleStore has integrations to common data lakes and streams. With SingleStore pipelines, users can perform common data transformations during the ingestion process. SingleStore provides limited support for semi-structured data with its JSON column type. Many users structure data prior to ingestion for optimal query performance.
Elasticsearch Queries vs SingleStore
Elasticsearch has its own domain specific language (DSL) based on JSON. Joins are not a first class citizen in Elasticsearch requiring a number of complex and expensive workarounds. Elasticsearch is known for its developer tooling and supports a number of client libraries. Kibana is the visualization layer for Elasticsearch and is frequently used for log analytics and monitoring.
SingleStore supports SQL as its native query language and can perform SQL joins. It is designed for querying structured data with static schemas. Users can create data APIs to execute SQL statements against the database over an HTTP connection. Common SingleStore use cases include business intelligence and analytics, and the database offers a number of integrations to visualization tools.
Elasticsearch Performance vs SingleStore
Elasticsearch is a search engine that utilizes an inverted index. Although this approach leads to storage amplification, it also enables low-latency queries that demand less computation. Elasticsearch is tailored to accommodate large scale, append-only data such as logs, events, and metrics. To manage frequently updated data, users often utilize the Bulk API to minimize computational costs and ensure consistent query performance.
SingleStore has two storage formats: a rowstore and a columnar store referred to as universal storage. The columnar store is used for analytical workloads, loading data in batch and relying on memory to achieve seconds of data latency. The columnar store can also execute queries in seconds when the working set is contained in memory. SingleStore provides the ability to configure and manage additional indexes on the data for faster performance.
Elasticsearch Scalability vs SingleStore
Elasticsearch is horizontally scalable and can scale by adding more nodes to the cluster. Its tightly coupled architecture means that compute and storage scale together for performance. This often results in resource contention and overprovisioning. Scaling Elasticsearch often requires deep expertise as there are many levels of the system that need to be managed- the server, operating system, network and software.
SingleStore Cloud can be sized up or down using the UI or the Management API. There is no ability to scale out by increasing or decreasing the leaf and aggregator nodes in the cloud offering. In the self-managed offering, horizontal and vertical scaling can occur by updating command-line arguments or the cluster directly. Horizontal scaling does require rebalancing