Benchmarking Top NoSQL Databases: Apache Cassandra, Apache HBase and MongoDB

ReferentEinrichtung Titel des Vortrages 1

WP-Benchmarking Top NoSQL Databases

Apache Cassandra, Apache HBase and MongoDB

Presented By Athiq Ahamed

Supriya


Introduction Enormous amount of data-BigData Scalabilty issue in RDBMS Rise of NoSQL databases

Amazon Dynamo Big table CAP Theorem BASE system


CAP Theorem Consistency Availability Partition tolerance

CAP theorem states that only two of the properties can be achieved at a time.


RDBMS NoSQL

Supports powerful query language

Supports very simple query language

It has a fixed schema No fixed schema

Follows ACID (Atomicity, Consistency, Isolation and Durability)

It is only eventually consistent

Supports transactions Does not support transactions

RDBMS vs NoSQL

Content:tutorialspoint.com


Basically available: System guarantees availability, in terms of the CAP theorem

Soft state: State of the system may change over time, because of eventual consistency model

Eventual consistency: System will become consistent over time

BASE

Content:www.edureka.in


Fast Performance is the key. POC processes include right benchmarks:

Configurations Parameters Workloads

Making the right choice!

Selection of NoSQL


Yahoo Cloud Serving Benchmark (YCSB)

Top 3 NoSQL databases-Apache Cassandra, Apache Hbase and MongoDB.

Amazon Web Services EC2 instances for hosting the tests

Test performed 3 times on 3 different days

Benchmark configuration


The tests ran on large size instances (15GB RAM and 4 CPU cores)

Instances used customized Ubuntu with Oracle Java 1.6 installed as a base.

A customized script written to drive the benchmark processes

Benchmark configuration


Each NoSQL system performs differently, not alike.

Components and Internal working.

Apache Cassandra: Columnar database model

Apache HBase: Columnar database model

MongoDB: Document storage database model

Understanding NoSQL Databases


Apache Cassandra Cassandra is scalable, fault-tolerant, and

consistent. All nodes are equal.

Its distribution design is based on Amazon’s Dynamo and its data model on Google’s Bigtable.

Key components: Node, Cluster, Commit log, Mem-table, SSTable and Bloom filter

Content:http://www.tutorialspoint.com/cassandra/cassandra_architecture.htm


Ring structure, peer to peer architecture

All nodes are equal

This improves general database availablity

Scaling up and scaling down is easier

Cassandra has key-value, column oriented database

Apache Cassandra


Apache Cassandra

Content:http://demoiselle.sourceforge.net/component/demoiselle-cassandra/1.0.0/images/datamodel1.png


Cassandra has an internal keyspace called system, stores metadata about the cluster.

Metadata: The node‘s token The cluster name Keyspace n schema definitions (dynamic

loading) Whether or not the node is bootstrapped

Apache Cassandra

Content:https://www.edureka.co/blog/category/apache-cassandra/


Commit log: Crash recovery mechanism. Every write operation is written to commit log

Mem-Table: A memory resident data structure.

SSTable: It is a disk file to which the data is flushed from the mem-table

Apache Cassandra


Bloom filters are used as a performance booster

Bloom filter are very fast, quick algorithms for testing a member in the set.

Bloom filters serves as a special kind of cache – quick lookups/search as they reside in memory

Apache Cassandra


Gossip protocol: Communiction between nodes, co-ordination and failure check

Anti-Entropy protocol: Replica sync mechanism enusing data on different nodes are updated (Merkle trees)

Snitches ensures host proximity

Apache Cassandra


Apache Cassandra- Read/Write operation


Sparse, distributed, sorted map and multidimensional and consistent.

Hbase is a Key/value store

Consists Row key, Column family, columns and timestamp.

Apache HBase


Apache HBase

Content:http://zhangjunhd.github.io/assets/2013-02-25-apache-hbase/rowkey-columnkey.png


Region: Contiguous rows form a region Region server(RS): Serves one or more regions. Master server: Daemon responsible for

managing Hbase cluster HDFS: Distributed, open source file system

containing HBase‘s data Zookeeper: Distributed, open source co-

ordinated service for co-ordination of master and region servers.

Apache HBase Components

Content: https://www.mapr.com/blog/in-depth-look-hbase-architecture


Apache Hbase Architecture


Client obtains meta table RS from Zookeeper

Client gets RS which holds the corresponding rowkey

Client receives the row from the respective Region server

Client caches this information along with the location of meta table server.

First Read/Write to HBase


WAL: Write Ahead Log is a file on the distributed file system. It is used to store new data

Block Cache: It is the read cache. It stores frequently read data in memory

Mem Store: Write cache that stores new data which is not written to disk yet.

Hfiles stores the rows as sorted key values on disk

HBase RS Components


Client writes the data to the WAL file stored on disk

WAL is used to recover not yet persisted data in case a server crashes.

Once data is written to WAL, it is placed in Mem Store

Hbase Write steps (1)


All write/read are to/from the primary node.

HDFS replicates WAL and Hfile blocks. Replication happens automatically.

When data is written in HDFS, one copy is written locally and then it is replicated to a secondary node and later to tertiary node.

HDFS Write steps (2)


Cassandra usecase: Availability and Partition tolerant requirements.

Consistency is tunable by setting it high in the option

Hbase usecase: Consistency and Scalability. However, at less number of nodes/threads, availability is achieved high

Cassandra and Hbase


Document-oriented database

High performance and automatic scaling

High consistency and partition tolerant

Replication and failover for high availability

Low latency

Flexible indexing

MongoDB


Document is the basic unit for MongoDB(row)

Collection is similar to a table

A single instance has multiple independent databases

Every document has a special key, “_id”

Powerful JavaScript shell for administration

Configdb contains metadata of clusters

MongoDB Concepts


MongoDB Simple Architecture


A mongo receives queries from applications

Uses metadata from config server for the data

Mangos directs write operations to a particular shard

Mongos uses the cluster metadata from the config database

Read/Write MongoDB


Scalability Availability Partition Tolerant Consistency

MOST IMPORTANT PERFORMANCE Yahoo Cloud Serving Benchmark (YCSB)

Recap Importance of Benchmark and Factors


Results: Load Process


Results: Read/Write Mix Workload


Results: Read/Scan Mix Workload


Results: Read Latency across all workloads


Results: Insert Latency across all workloads


Lets MIGRATE from traditional data base !!!!

Live Demo


Identify data model for the application Corresponding data sets have to be known Whether the application requires replication Identify the performance requirements Prototype the application Test the performance of the prototype

Discussion


Conclusion NoSQL replaced tradition relational databases Performance is the key feature Importance of benchmarks Top three NoSQL data base’s performance

tested Cassandra outperforms all the other NoSQL data

bases Decide based on application

Benchmarking Top NoSQL Databases: Apache Cassandra, Apache HBase and MongoDB

Data & Analytics

Transcript of Benchmarking Top NoSQL Databases: Apache Cassandra, Apache HBase and MongoDB