This is an attempt to compare different noSql stores from an architect's point of view.

1. Open-source noSQL DB comparison
- Ajit Bhingarkar, Principal Architect, Capiot Inc.
Summary:
This document tries to compare a different set of features particularly from an architect's point
of view (NFRs + some more) than most well known comparisons available easily elsewhere, and
using merely free/community software editions. It covers 4 systems which have been evaluated
for an IoT centric storage during a PoC. It lists down an important aspect of a storage system,
and then identifies capability of all 4 products in that specific area. I have also identified if a
certain feature is available with the commercial version of the product.
Feature ElasticSearch Couchbase MongoDB Cassandra
General
information
Elasticsearch is a
search server
based on Lucene.
It provides a
distributed,
multitenant-
capable full-text
search engine
with a RESTful
web interface
and schema-free
JSON
documents.
Elasticsearch is
developed in
Java and is
released as open
source under the
terms of the
Apache License.
Couchbase
Server originally
known as
Membase, is an
open source,
distributed
(shared-nothing
architecture)
NoSQL
document-
oriented
database that is
optimized for
interactive
applications.
These
applications
must service
many concurrent
users; creating,
storing,
retrieving,
aggregating,
manipulating
and presenting
MongoDB is an
open-source
database used
by companies of
all sizes, across
all industries and
for a wide
variety of
applications. It is
an agile database
that allows
schemas to
change quickly
as applications
evolve, while still
providing the
functionality
developers
expect from
traditional
databases, such
as secondary
indexes, a full
query language
and strict
The Apache
Cassandra
database is the
right choice
when you need
scalability and
high availability
without
compromising
performance.
Linear scalability
and proven fault-
tolerance on
commodity
hardware or
cloud
infrastructure
make it the
perfect platform
for mission-
critical data.
Cassandra’s
support for
replicating across

2. data. In support
of these kinds of
application
needs,
Couchbase is
designed to
provide easy-to-
scale key-value
or document
access with low
latency and high
sustained
throughput. It is
designed to be
clustered from a
single machine
to very large
scale
deployments.
consistency.
MongoDB is built
for scalability,
performance and
high availability,
scaling from
single server
deployments to
large, complex
multi-site
architectures. By
leveraging in-
memory
computing,
MongoDB
provides high
performance for
both reads and
writes.
MongoDB’s
native
replication and
automated
failover enable
enterprise-grade
reliability and
operational
flexibility.
multiple data-
centers is best-
in-class,
providing lower
latency for your
users and the
peace of mind of
knowing that you
can survive
regional outages.
Cassandra’s data
model offers the
convenience of
column indexes
with the
performance of
log-structured
updates, strong
support for
denormalization
and materialized
views, and
powerful built-in
caching.
License and
version
Free edition
(Apache), with
commercial plug-
ins
Version: 1.7.2
Apache 2.0
Commercial
editions come
with several
features and
support.
Source code
available only in
EE.
Version: 4.0.0.RC
Community
edition is free,
Enterprise
edition is
subscription
based.
Version: 3.0.6
Community
edition is free.
Enterprise
edition is
subscription
based, and there
are significant
gaps.
Version: 2.0.14
Storage JSON JSON BSON (binary Data is stored in

3. encoded JSON) column families,
in an immutable
SSTable, which is
sort of a map of
maps.
In order to work
with json
document, a json
has to be
mapped as an
map.
Written In Java Erlang C++ Java
REST Interface    
Monitoring
capabilities
Through paid
plug-in, none
with free
version.
May be third
party plug-ins
are available.
REST and CLI
based
monitoring is
available for
node as well as
for a bucket.
Console shows
several logs as
well.
Tools are
provided, and a
web console too
is provided
running on port
1000.
Special tools like
cloud manager
or in-premises
cluster manager,
are available
with Enterprise
edition only.
Through JMX
and a tool called
node tool.
Commercial
version has
OpsCenter.
Reporting
capabilities or
Dashboards
Kibana, free
dashboard.
No special tools
which are out of
the box. There
are some custom
projects, and one
using
ElasticSearch and
its Kibana
dashboard for
reporting and BI.
Nothing out of
the box, but
several third
party tools are
available to build
a dashboard.
Building a
custom one
seemed easy
using
JSONStudio.
MMS is not free.
Nothing out of
the box, but
several third
party tools are
available to build
a dashboard.
JasperSoft,
Pentaho, and
even MicroSoft
Excel (with ODBC
drivers) can be
used for BI,
reporting.
Commercial

4. version has
Admin UI as well
as OpsCenter.
APIs for
integration
Java API, JMX,
Excellent REST
interface
Java, REST, node,
Python
Drivers: Java,
Perl, Python,
node, Scala, C++
Java, node, Go,
Python, C#, Ruby
etc.
Commercial
product offers
more language
bindings.
Security None with free
version; requires
Shield which is
paid.
elasticsearch-
jetty has been
claimed to
provide
password based
security, and role
based access.
Simple password
based
authentication is
available.
Advanced auth
available with
commercial
edition.
Password based
and X509 based
auth is available.
Advanced
security like
LDAP integration
with Enterprise
Edition.
Simple password
based
authentication is
available.
Enterprise level
security is with
commercial
edition.
Support for
events
(Alerts/Notificati
ons)
None, requires
paid plug-in.
Watcher (paid) is
quite powerful.
Alerts regarding
basic system
level monitoring
are supported.
Functional alerts
like changes in
documents etc.
can be notified,
but no alerts at
this point in
time.
Not supported
except for some
basic system
alerts.
Through
commercial
editions (MMS)
and MongoLab
(for Cloud) alerts
are available.
Seems not to be
there from initial
reading.
HA-Cluster
mgmt,
replication, XDCR
(Cross data
center
replication) etc.
Cluster mgmt
can be done
manually and
also through
elasticsearch-
head plug-in
Nicely done
through UI.
Master to master
replication
through replica-
Automatic fail-
over through
leader election
when master
fails for 10
seconds.
Peer to peer
distributed
cluster, with no
single point of
failure.

5. which allows for
UI based cluster
monitoring.
Replication
(async) is
configurable,
and creating
cluster is
automatic
because of auto-
discovery.
XDCR: NA
sets.
Auto scaling.
Asynchronous
replication
Automatic
failover
supported.
XDCR supported.
Asynchronous
replication.
Cluster mgmt UI
is available too.
DR easily
supported.
Highly available
cluster, XDCR
supported.
DR easily
supported.
Access Control None with free
version
Simple user
name/password
based.
Role based
access control
Password/role
based.
Commercial
version offers
extensive control
mechanism.
Query language Query DSL.
REST API uses
json like query
parameters.
N1QL No specific
name.
Queries are
made on
collection docs,
and query
parameters are
like json.
REST API too
uses json like
query
parameters.
JavaScript like
queries can be
executed using
Mongo shell.
CQL
Transactions/Dur
ability
Not designed to
be an ACID
compliant
system.
Supports ACID
transactions on a
per-document
level.
Document level
ACID compliance
seems to be in
place.
Durable,
eventual
consistent. String
consistency

6. Supports
concurrent
updates to an
individual
document, but
not across
multiple
documents.
For further
concurrency, ES
supports locking.
You can use
either CAS
(Check and
Set/Compare
and Swap) for
optimistic
concurrency or
use GetAndLock
to actually lock a
document for
pessimistic
concurrency
scenarios.
Further support
for concurrent
updates can
come through
usage of locks.
Generally it is
one write lock,
and a shared
access for
multiple
concurrent
reads.
through
replication
factor.
Further support
for concurrent
updates can
come through
usage of locks.
Connectors Spark, Hadoop,
Storm,
Hadoop, Kafka,
ElasticSearch,
Spark, also an
SDK is provided
ES, Spark,
Hadoop, API is
available.
Miscellaneous Based on Apache
Lucene.
User friendly.
Extends
memcashed
protocol.
User friendly.
Can handle high
velocity data;
however some
folks have
commented
about
operational
complexity.
Based on
Dynamo
(clustering) and
BigTable (data
model).
Can handle very
high incoming
data velocity.
Top time series
data storage.
Data model
could be the key.
UPSERT
performance on
a scale of 5
3.5 4 4 5
Query
performance on
a scale of 5
3.5
Friendly
[An index per
data type is a
good idea.]
3.5
[Some
limitations like
joins across
buckets]
4
Fast
4
Fast

7. References:
1. http://developer.couchbase.com/documentation/server/4.0/introduction/editions.html
2. http://mobz.github.io/elasticsearch-head/
3. http://kkovacs.eu/cassandra-vs-mongodb-vs-couchdb-vs-redis
4. http://www.altoros.com/download_white_papers.html
5. http://jsonstudio.com/mongodb-reporting-5-minutes/
6. http://codehardblog.azurewebsites.net/real-time-data-analytics-with-couchbase-and-
elasticsearch/
7. http://info.couchbase.com/Couchbase105-Dec3.html
8. http://www.couchbase.com/connect/agenda/enterprise-reporting-visualization-bi-etl-
couchbase-n1ql-odbc-jdbc/
9. http://foorious.com/blog/2014/09/cassandra-json/
10. https://academy.datastax.com/demos/brief-introduction-apache-cassandra
11. http://bigdata-madesimple.com/a-deep-dive-into-nosql-a-complete-list-of-nosql-
databases/