DBMS > Alibaba Cloud TSDB vs. Dgraph vs. Elasticsearch vs. Vertica

System Properties Comparison Alibaba Cloud TSDB vs. Dgraph vs. Elasticsearch vs. Vertica

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Editorial information provided by DB-Engines

Name

Description

A stable, reliable, and cost-effective online high-performance time series database service

Distributed and scalable native Graph DBMS

A distributed, RESTful modern search and analytics engine based on Apache Lucene

Elasticsearch lets you perform and combine many types of searches such as structured, unstructured, geo, and metric

Cloud or off-cloud analytical database and query engine for structured and semi-structured streaming and batch data. Machine learning platform with built-in algorithms, data preparation capabilities, and model evaluation and management via SQL or Python.

Primary database model

Column oriented

Secondary database models

Document store
Spatial DBMS
Vector DBMS

Spatial DBMS
Time Series DBMS

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

Score	0.24
Rank	#312	Overall
	#27	Time Series DBMS

Score	1.48
Rank	#158	Overall
	#15	Graph DBMS

Score	134.78
Rank	#7	Overall
	#1	Search engines

Score	11.40
Rank	#43	Overall
	#27	Relational DBMS

Website

www.alibabacloud.com/product/hitsdb

dgraph.io

www.elastic.co/elasticsearch

www.vertica.com

Technical documentation

www.alibabacloud.com/help/en/time-series-database/latest/what-is-tsdb

dgraph.io/docs

www.elastic.co/guide/en/elasticsearch/reference/current/index.html

vertica.com/documentation

Developer

Alibaba

Dgraph Labs, Inc.

Elastic

OpenText

previously Micro Focus and Hewlett Packard

Initial release

2016

2010

2005

Current release

8.6, January 2023

12.0.3, January 2023

License

Commercial or Open Source

commercial

Open Source

Apache 2.0

Open Source

Elastic License

commercial

Limited community edition free

Cloud-based only

Only available as a cloud service

yes

on-premises, all major clouds - Amazon AWS, Microsoft Azure, Google Cloud Platform and containers

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

Java

C++

Server operating systems

hosted

Linux
OS X
Windows

All OS with a Java VM

Linux

Data scheme

schema-free

Flexible type definitions. Once a type is defined, it is persistent

Yes, but also semi-structure/unstructured data storage, and complex hierarchical data (like Parquet) stored and/or queried.

Typing

predefined data types such as float or date

Numeric data and Strings

yes

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.

Secondary indexes

yes

All search fields are automatically indexed

No Indexes Required. Different internal optimization strategy, but same functionality included.

SQL

Support of SQL

SQL-like query language

Full 1999 standard plus machine learning, time series and geospatial. Over 650 functions.

APIs and other access methods

HTTP REST

GraphQL query language
gRPC (using protocol buffers) API
HTTP API

Java API
RESTful HTTP/JSON API

ADO.NET
JDBC
Kafka Connector
ODBC
RESTful HTTP API
Spark Connector
vSQL

character-based, interactive, front-end utility

Supported programming languages

Java

C#
C++
Go
Java
JavaScript (Node.js)
PHP
Python
Ruby

.Net
Groovy
Community Contributed Clients
Java
JavaScript
Perl
PHP
Python
Ruby

C#
C++
Go
Java
JavaScript (Node.js)
Perl
PHP
Python
R

Server-side scripts

Stored procedures

yes

yes, PostgreSQL PL/pgSQL, with minor differences

Triggers

yes

by using the 'percolation' feature

yes, called Custom Alerts

Partitioning methods

Methods for storing different data on different nodes

yes

Sharding

horizontal partitioning, hierarchical partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

Synchronous replication via Raft

yes

Multi-source replication

One, or more copies of data replicated across nodes, or object-store used for repository.

MapReduce

Offers an API for user-defined Map/Reduce methods

ES-Hadoop Connector

Bi-directional Spark integration

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency

Synchronous doc based replication. Get by ID may show delays up to 1 sec. Configurable write consistency: one, quorum, all

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

Memcached and Redis integration

User concepts

Access control

Access managed on TSDB instance level

Planned for future releases

fine grained access rights according to SQL-standard; supports Kerberos, LDAP, Ident and hash

More information provided by the system vendor

Specific characteristics

Deploy-anywhere database for large-scale analytical deployments. Deploy off-cloud,...
» more

Competitive advantages

Fast, scalable, and capable of high concurrency. Separation of compute/storage leverages...
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Typical application scenarios

Communication and network analytics, Embedded analytics, Fraud monitoring and Risk...
» more

Key customers

Abiba Systems, Adform, adMarketplace, AmeriPride, Anritsu, AOL, Avito, Auckland Transport,...
» more

Licensing and pricing models

Cost-based models and subscription-based models are both available. One license is...
» more

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and for displaying vendor-provided information such as key customers, competitive advantages and market metrics.

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