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Elasticsearch vs SingleStore

Compare and contrast Elasticsearch and SingleStore by architecture, ingestion, queries, performance, and scalability.

Compare Elasticsearch to Rockset here

Compare SingleStore to Rockset here

Elasticsearch vs SingleStore Architecture

Architecture
Elasticsearch
SingleStore
Deployment model
On-prem, PaaS options
Self managed and SaaS deployment options
Use of storage hierarchy
• Hot, warm and cold storage on disk • Frozen storage on cloud storage
• Memory - for data requiring the highest performance • High-performance block storage for persistent cache - the working dataset should fit within the persistent cache • Cloud object storage for long-term retention
Isolation of ingest and query
No - There are dedicated ingestion nodes but indexing, compaction and updates occur on the data nodes
No - databases share ingest and queries
Separation of compute and storage
No
Yes - Singlestore Cloud uses cloud object storage for separation of compute and storage
Isolation for multiple applications
Full isolation with replication
No

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 vs SingleStore Ingestion

Ingestion
Elasticsearch
SingleStore
Data sources
• Logstash JDBC input plugin for relational databases • Open-source Kafka plugin or Kafka Elasticsearch Service Sink Connector (available only to managed Confluent and Elasticsearch) • REST APIs or client libraries to sync data directly from the application
Integrations to: Amazon S3, Apache Beam, GCS, HDFS, Kafka, Spark, Qlik Replicate, HVR
Semi structured data
Yes- Ingests JSON and XML without a predefined schema
Ingests JSON as a JSON column type
Transformations and rollups
Yes - Ingest pipelines can be configured to remove fields, extract values from text and enrich data. Ingest pipelines require ingest nodes in the cluster. Rolling up historical data is in technical preview
SingleStore pipelines do common data shaping including normalizing and denormalizing data, adding computed columns, filtering data, mapping data, splitting records into multiple destination tables

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 vs SingleStore Performance

Performance
Elasticsearch
SingleStore
Updates
Update API can update, delete or skip modifying the document. The entire document must be reindexed; in-place updates are not supported
SingleStore columnar store/universal storage is immutable. Updates are fast when the data still resides in memory
Indexing
Inverted index
Indexes can be manually configured: Skiplist index, hash index, full-text index, geospatial index
Query latency
50-1000ms queries on 100s of TBs
50-1000ms queries when the working set is contained in memory
Storage format
JSON documents
Two table formats-either use the rowstore or columnstore/universal storage
Streaming ingest
• Ingests on a per-record or batch basis • Data latency on a per-record basis is typically 1-2 seconds
• Columnnar store/universal storage ingests on a batch basis • Data latency is typically seconds by relying on memory

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 vs SingleStore Queries

Queries
Elasticsearch
SingleStore
Joins
No- Need to use workarounds including data denormalization, application-side joins, nested objects or parent-child relationships
Yes
Query language
DSL - domain specific language
SQL
Developer tooling
• REST API • Java, Javascript, Go, .NET, PHP, Perl, Python, Ruby, Rust
• API for querying data via POST command • JDBC driver, Python client • Compatibility with MySQL and MariaDB to support additional drivers
Visualization tools
• Kibana • PowerBI, Qlik, Tableau
Integrations with Cognos Analytics, Dremio, Looker, Microstrategy, Power BI, Sisense, Tableau and Tibco Spotfire

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 vs SingleStore Scalability

Scalability
Elasticsearch
SingleStore
Vertical scaling
Manually resize machines
• Cloud offering: Resize compute workspaces in the UI or using the Management API • Self-managed offering: Change cluster configuration by updating command-line arguments or to the cluster directly.
Horizontal scaling
• Elasticsearch is horizontally scalable and can scale by adding nodes to the cluster • When using managed Elastic, autoscaling policies can be used to self-monitor cluster health and it is the responsibility of the operator to update resource allocations either manually or using APIs. Elasticsearch rebalances the data automatically obeying shard allocation rules • There are many cluster-level operations that need to be monitored when scaling
Self-managed offering: Increase or decrease the number of nodes in the cluster. Rebalancing required

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