DBMS > AlaSQL vs. Google BigQuery vs. Snowflake vs. Solr vs. XTDB

System Properties Comparison AlaSQL vs. Google BigQuery vs. Snowflake vs. Solr vs. XTDB

Editorial information provided by DB-Engines

Name

Description

JavaScript DBMS library

Large scale data warehouse service with append-only tables

Cloud-based data warehousing service for structured and semi-structured data

A widely used distributed, scalable search engine based on Apache Lucene

A general purpose database with bitemporal SQL and Datalog and graph queries

Primary database model

Document store
Relational DBMS

Relational DBMS

Search engine

Document store

Secondary database models

Spatial DBMS

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

Score	0.46
Rank	#260	Overall
	#40	Document stores
	#121	Relational DBMS

Score	60.38
Rank	#19	Overall
	#13	Relational DBMS

Score	121.33
Rank	#9	Overall
	#6	Relational DBMS

Score	42.91
Rank	#24	Overall
	#3	Search engines

Score	0.11
Rank	#343	Overall
	#46	Document stores

Website

alasql.org

cloud.google.com/bigquery

www.snowflake.com

solr.apache.org

github.com/xtdb/xtdb
www.xtdb.com

Technical documentation

github.com/AlaSQL/alasql

cloud.google.com/bigquery/docs

docs.snowflake.net/manuals/index.html

solr.apache.org/resources.html

www.xtdb.com/docs

Developer

Andrey Gershun & Mathias R. Wulff

Google

Snowflake Computing Inc.

Apache Software Foundation

Juxt Ltd.

Initial release

2014

2010

2014

2006

2019

Current release

9.6.0, April 2024

1.19, September 2021

License

Commercial or Open Source

Open Source

MIT-License

commercial

Open Source

Apache Version 2

Open Source

MIT 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

JavaScript

Java

Clojure

Server operating systems

server-less, requires a JavaScript environment (browser, Node.js)

hosted

All OS with a Java VM

runs as a servlet in servlet container (e.g. Tomcat, Jetty is included)

All OS with a Java 8 (and higher) VM
Linux

Data scheme

schema-free

yes

support of semi-structured data formats (JSON, XML, Avro)

yes

Dynamic Fields enables on-the-fly addition of new fields

schema-free

Typing

predefined data types such as float or date

yes

supports customizable data types and automatic typing

yes, extensible-data-notation format

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

All search fields are automatically indexed

yes

SQL

Support of SQL

Close to SQL99, but no user access control, stored procedures and host language bindings.

yes

Solr Parallel SQL Interface

limited SQL, making use of Apache Calcite

APIs and other access methods

JavaScript API

RESTful HTTP/JSON API

CLI Client
JDBC
ODBC

Java API
RESTful HTTP/JSON API

HTTP REST
JDBC

Supported programming languages

JavaScript

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

JavaScript (Node.js)
Python

.Net
Erlang
Java
JavaScript
any language that supports sockets and either XML or JSON
Perl
PHP
Python
Ruby
Scala

Clojure
Java

Server-side scripts

Stored procedures

user defined functions

in JavaScript

user defined functions

Java plugins

Triggers

yes

similar concept for controling cloud resources

yes

User configurable commands triggered on index changes

Partitioning methods

Methods for storing different data on different nodes

none

yes

Sharding

none

Replication methods

Methods for redundantly storing data on multiple nodes

none

yes

yes, each node contains all data

MapReduce

Offers an API for user-defined Map/Reduce methods

spark-solr: github.com/lucidworks/spark-solr and streaming expressions to reduce

Consistency concepts

Methods to ensure consistency in a distributed system

none

Immediate Consistency

Eventual Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

yes

only for local storage and DOM-storage

Since BigQuery is designed for querying data

ACID

optimistic locking

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

by using IndexedDB, SQL.JS or proprietary FileStorage

yes

yes, flexibel persistency by using storage technologies like Apache Kafka, RocksDB or LMDB

In-memory capabilities

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

yes

User concepts

Access control

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

Google Cloud Identity & Access Management (IAM)

Users with fine-grained authorization concept, user roles and pluggable authentication

yes

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