Druid vs MemSQL

Overview

MemSQL

Stacks86

Followers184

Votes44

Druid

Stacks376

Followers867

Votes32

Druid vs MemSQL: What are the differences?

Data Storage: Druid stores data in a column-oriented manner optimized for time-series data and ad-hoc queries, while MemSQL stores data in row-oriented tables, making it more suitable for transactional workloads that require strong consistency.
Scalability: Druid is highly scalable for read-heavy workloads through distributed query engines and aggregators, whereas MemSQL is known for its high scalability by enabling the distribution of data across multiple nodes in a cluster.
Data Ingestion: Druid supports real-time data ingestion from streams like Kafka and supports batch data ingestion, while MemSQL excels in transactional workload ingestion and provides tools like pipelines and change data capture for data processing.
Data Model: Druid uses a star-tree data model that optimizes queries for time-series data, while MemSQL follows a relational data model with support for SQL queries and joins, making it suitable for various analytical and transactional use cases.
Query Performance: Druid focuses on sub-second query response times for large-scale data sets with its query optimizations and indexing strategies, while MemSQL leverages in-memory processing and query compilation to achieve high-speed query performance.
Use Case Focus: Druid is best suited for companies needing real-time analytics for event data, time-series data, and interactive exploration, while MemSQL is ideal for organizations that require a combination of real-time analytics, transactional processing, and high availability.

In Summary, Druid and MemSQL differ in data storage formats, scalability options, data ingestion methods, data modeling, query performance strategies, and target use cases in the realm of real-time analytics and database management.

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Detailed Comparison

MemSQL	Druid
MemSQL converges transactions and analytics for sub-second data processing and reporting. Real-time businesses can build robust applications on a simple and scalable infrastructure that complements and extends existing data pipelines.	Druid is a distributed, column-oriented, real-time analytics data store that is commonly used to power exploratory dashboards in multi-tenant environments. Druid excels as a data warehousing solution for fast aggregate queries on petabyte sized data sets. Druid supports a variety of flexible filters, exact calculations, approximate algorithms, and other useful calculations.
ANSI SQL Support;Fully-distributed Joins;Compiled Queries; ACID Compliance;In-Memory Tables;On-Disk Tables; Massively Parallel Execution;Lock Free Data Structures;JSON Support; High Availability; Online Backup and Restore;Online Replication	-
Statistics
Stacks 86	Stacks 376
Followers 184	Followers 867
Votes 44	Votes 32
Pros & Cons
Pros 9 Distributed 5 Realtime 4 JSON 4 Columnstore 4 Concurrent	Pros 15 Real Time Aggregations 6 Batch and Real-Time Ingestion 5 OLAP 3 OLAP + OLTP 2 Combining stream and historical analytics Cons 3 Limited sql support 2 Joins are not supported well 1 Complexity
Integrations
Google Compute Engine MySQL QlikView	Zookeeper

What are some alternatives to MemSQL, Druid?

Redis

Redis is an open source (BSD licensed), in-memory data structure store, used as a database, cache, and message broker. Redis provides data structures such as strings, hashes, lists, sets, sorted sets with range queries, bitmaps, hyperloglogs, geospatial indexes, and streams.

Apache Spark

Spark is a fast and general processing engine compatible with Hadoop data. It can run in Hadoop clusters through YARN or Spark's standalone mode, and it can process data in HDFS, HBase, Cassandra, Hive, and any Hadoop InputFormat. It is designed to perform both batch processing (similar to MapReduce) and new workloads like streaming, interactive queries, and machine learning.

Presto

Distributed SQL Query Engine for Big Data

Hazelcast

With its various distributed data structures, distributed caching capabilities, elastic nature, memcache support, integration with Spring and Hibernate and more importantly with so many happy users, Hazelcast is feature-rich, enterprise-ready and developer-friendly in-memory data grid solution.

Amazon Athena

Amazon Athena is an interactive query service that makes it easy to analyze data in Amazon S3 using standard SQL. Athena is serverless, so there is no infrastructure to manage, and you pay only for the queries that you run.

Aerospike

Aerospike is an open-source, modern database built from the ground up to push the limits of flash storage, processors and networks. It was designed to operate with predictable low latency at high throughput with uncompromising reliability – both high availability and ACID guarantees.

Apache Ignite

It is a memory-centric distributed database, caching, and processing platform for transactional, analytical, and streaming workloads delivering in-memory speeds at petabyte scale

Apache Flink

Apache Flink is an open source system for fast and versatile data analytics in clusters. Flink supports batch and streaming analytics, in one system. Analytical programs can be written in concise and elegant APIs in Java and Scala.

lakeFS

It is an open-source data version control system for data lakes. It provides a “Git for data” platform enabling you to implement best practices from software engineering on your data lake, including branching and merging, CI/CD, and production-like dev/test environments.

SAP HANA

It is an application that uses in-memory database technology that allows the processing of massive amounts of real-time data in a short time. The in-memory computing engine allows it to process data stored in RAM as opposed to reading it from a disk.

Related Comparisons

Druid vs MemSQL: What are the differences?

Data Storage: Druid stores data in a column-oriented manner optimized for time-series data and ad-hoc queries, while MemSQL stores data in row-oriented tables, making it more suitable for transactional workloads that require strong consistency.
Scalability: Druid is highly scalable for read-heavy workloads through distributed query engines and aggregators, whereas MemSQL is known for its high scalability by enabling the distribution of data across multiple nodes in a cluster.
Data Ingestion: Druid supports real-time data ingestion from streams like Kafka and supports batch data ingestion, while MemSQL excels in transactional workload ingestion and provides tools like pipelines and change data capture for data processing.
Data Model: Druid uses a star-tree data model that optimizes queries for time-series data, while MemSQL follows a relational data model with support for SQL queries and joins, making it suitable for various analytical and transactional use cases.
Query Performance: Druid focuses on sub-second query response times for large-scale data sets with its query optimizations and indexing strategies, while MemSQL leverages in-memory processing and query compilation to achieve high-speed query performance.
Use Case Focus: Druid is best suited for companies needing real-time analytics for event data, time-series data, and interactive exploration, while MemSQL is ideal for organizations that require a combination of real-time analytics, transactional processing, and high availability.

Druid vs MemSQL

Overview