DBMS > Google BigQuery vs. GridGain vs. Immudb vs. InfluxDB vs. Memcached

System Properties Comparison Google BigQuery vs. GridGain vs. Immudb vs. InfluxDB vs. Memcached

Editorial information provided by DB-Engines

Name

Description

Large scale data warehouse service with append-only tables

GridGain is an in-memory computing platform, built on Apache Ignite

An open source immutable (append-only) database with cryptographic verification which makes it tamper-resistant and fully auditable.

DBMS for storing time series, events and metrics

In-memory key-value store, originally intended for caching

Primary database model

Relational DBMS

Key-value store
Relational DBMS

Key-value store

Time Series DBMS

Key-value store

Secondary database models

Relational DBMS

Spatial DBMS

with GEO package

DB-Engines Ranking measures the popularity of database management systems
Trend Chart

Score	60.38
Rank	#19	Overall
	#13	Relational DBMS

Score	1.47
Rank	#154	Overall
	#26	Key-value stores
	#72	Relational DBMS

Score	0.24
Rank	#305	Overall
	#43	Key-value stores

Score	25.83
Rank	#28	Overall
	#1	Time Series DBMS

Score	19.42
Rank	#32	Overall
	#4	Key-value stores

Website

cloud.google.com/bigquery

www.gridgain.com

github.com/codenotary/immudb
immudb.io

www.influxdata.com/products/influxdb-overview

www.memcached.org

Technical documentation

cloud.google.com/bigquery/docs

www.gridgain.com/docs/index.html

docs.immudb.io

docs.influxdata.com/influxdb

github.com/memcached/memcached/wiki

Developer

Google

GridGain Systems, Inc.

Codenotary

Danga Interactive

originally developed by Brad Fitzpatrick for LiveJournal

Initial release

2010

2007

2020

2013

2003

Current release

GridGain 8.5.1

1.2.3, April 2022

2.7.6, April 2024

1.6.25, March 2024

License

Commercial or Open Source

commercial

Open Source

Apache Version 2.0

Open Source

MIT-License; commercial enterprise version available

Open Source

BSD license

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

Providers of DBaaS offerings, please contact us to be listed.

Implementation language

Java, C++, .Net

Server operating systems

hosted

Linux
OS X
Solaris
Windows

BSD
Linux
macOS
Solaris
Windows
z/OS

Linux
OS X

through Homebrew

FreeBSD
Linux
OS X
Unix
Windows

Data scheme

yes

schema-free

Typing

predefined data types such as float or date

yes

Numeric data and Strings

XML support

Some form of processing data in XML format, e.g. support for XML data structures, and/or support for XPath, XQuery or XSLT.

yes

Secondary indexes

yes

SQL

Support of SQL

yes

ANSI-99 for query and DML statements, subset of DDL

SQL-like syntax

SQL-like query language

APIs and other access methods

RESTful HTTP/JSON API

HDFS API
Hibernate
JCache
JDBC
ODBC
Proprietary protocol
RESTful HTTP API
Spring Data

gRPC protocol
PostgreSQL wire protocol

HTTP API
JSON over UDP

Proprietary protocol

Supported programming languages

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

C#
C++
Java
PHP
Python
Ruby
Scala

.Net
Go
Java
JavaScript (Node.js)
Python

.Net
Clojure
Erlang
Go
Haskell
Java
JavaScript
JavaScript (Node.js)
Lisp
Perl
PHP
Python
R
Ruby
Rust
Scala

.Net
C
C++
ColdFusion
Erlang
Java
Lisp
Lua
OCaml
Perl
PHP
Python
Ruby

Server-side scripts

Stored procedures

user defined functions

in JavaScript

yes (compute grid and cache interceptors can be used instead)

Triggers

yes (cache interceptors and events)

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

in enterprise version only

none

Replication methods

Methods for redundantly storing data on multiple nodes

yes (replicated cache)

selectable replication factor

in enterprise version only

none

Repcached, a Memcached patch, provides this functionallity

MapReduce

Offers an API for user-defined Map/Reduce methods

yes (compute grid and hadoop accelerator)

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Foreign keys

Referential integrity

Transaction concepts

Support to ensure data integrity after non-atomic manipulations of data

Since BigQuery is designed for querying data

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

In-memory capabilities

Is there an option to define some or all structures to be held in-memory only.

yes

Depending on used storage engine

User concepts

Access control

Access privileges (owner, writer, reader) on dataset, table or view level

Google Cloud Identity & Access Management (IAM)

Security Hooks for custom implementations

simple rights management via user accounts

yes

using SASL (Simple Authentication and Security Layer) protocol

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