The document provides an overview of column databases. It begins with a quick recap of different database types and then defines and discusses column databases and column-oriented databases. It explains that column databases store data by column rather than by row, allowing for faster access to specific columns of data. Examples of column databases discussed include Cassandra, HBase, and Vertica. The document then focuses on Cassandra, describing its data model using concepts like keyspaces and column families. It also explains Cassandra's database engine architecture featuring memtables, SSTables, and compaction. The document concludes by mentioning some large companies that use Cassandra in production systems.

1. Overview of Column DBs
Presenter – Pooja Biswas
Presented On - DOL

2. Topics To
Cover
Quick Recap of the different type of Databases
What is a Column DB
Type of Column DBs
Example of Column DBs
Deep Dive into Cassandra’s Data Model, DB engine and
read write pattern
Usage of column DBs in real world systems

3. Let’s revise
the DB
hierarchy
once

4. What is a
column DB?
• Column database
• Column family database
• Column oriented database
• Wide column store database
• Wide column store
• Columnar database
• Columnar store

5. What is a column
Store or column-
oriented
DBMS or columnar
DBMS
As per Wikipedia A column-oriented DBMS or columnar
DBMS is a database management system (DBMS) that stores
data tables by column rather than by row
Practical use of a column store versus a row store differs little in
the relational DBMS world. Both columnar and row databases
can use traditional database query languages like SQL to load
data and perform queries.
Both row and columnar databases can become the backbone in
a system to serve data for common extract, transform, load (ETL)
and data visualization tools.
However, by storing data in columns rather than rows, the
database can more precisely access the data it needs to answer
a query rather than scanning and discarding unwanted data in
rows.

6. Example Sybase IQ, C-Store, Vertica, VectorWise, MonetDB, ParAccel, and Infobright.

7. Cont..
• A column-oriented database stores each
column continuously. i.e. on disk or in-
memory each column on the left will be stored
in sequential blocks.
• For analytical queries that perform aggregate
operations over a small number of columns
retrieving data in this format is extremely fast.
• As disc storage is optimized for block access,
by storing the data beside each other we
exploit locality of reference

8. Benefits of
Column
Store
Database
s
Compression. Column stores are very efficient at data
compression and/or partitioning.
Aggregation queries. Due to their structure, columnar databases
perform particularly well with aggregation queries (such as
SUM, COUNT, AVG, etc).
Scalability. Columnar databases are very scalable. They are well
suited to massively parallel processing (MPP), which involves
having data spread across a large cluster of machines – often
thousands of machines.
Fast to load and query. Columnar stores can be loaded
extremely fast. A billion row table could be loaded within a few
seconds. You can start querying and analysing almost
immediately.

10. What is a column
family or wide
column DB

11. As per
Wikipedia
• A wide-column store (or extensible
record stores) is a type
of NoSQL database.[1]
• It uses tables, rows, and columns,
but unlike a relational database, the
names and format of the columns
can vary from row to row in the
same table. A wide-column store
can be interpreted as a two-
dimensional key–value store.[1]

12. Cont..
• Wide-column stores such as Bigtable and
Apache Cassandra are not column stores in
the original sense of the term, since their two-
level structures do not use a columnar data
layout.
• In genuine column stores, a columnar data
layout is adopted such that each column is
stored separately on disk.
• Wide-column stores do often support the
notion of column families that are stored
separately
• However, each such column family typically
contains multiple columns that are used
together, similar to traditional relational
database tables
• Within a given column family, all data is
stored in a row-by-row fashion, such that the
columns for a given row are stored together,
rather than each column being stored
separately
• Wide-column stores that support column
families are also known as column family
databases.

13. Cont..
• As a 2-dimensional key-value store,
the first part of the key is used to
distribute the data across servers,
the second part of the key lets you
quickly find the data on the target
server
• Example Cassandra, Amazon
Dynamo DB, Bigtable, Hbase

14. Cassandra Deep
Dive

15. Cassandra
• Apache Cassandra is a distributed, wide-column
store, NoSQL database management system designed to
handle large amounts of data across many commodity servers.
• It is an AP system, i.e it targets providing high availability and
partition tolerance
• It also provides No single point of failure
• Cassandra offers support for clusters spanning multiple
datacentres, with asynchronous masterless replication allowing
low latency operations for all clients
• Cassandra was designed to implement a combination of
Amazon's Dynamo distributed storage and replication
techniques combined with Google's Bigtable data and storage
engine model.
• Cassandra and DynamoDB both origin from the same paper:
Dynamo: Amazon’s Highly Available Key-value store
•

16. Cassandra
Data Model
Cassandra is considered a wide-column
store, which manages data in column
families.
Columns are the first-class citizen in
Cassandra World
Rows in a wide-column database don’t
need to have the same columns
Enables developers to dynamically add and
remove new columns without impacting
the underlying table

17. Contd..
• Cassandra uses a concept called
a keyspace
• A keyspace is kind of like
a schema in the relational model
• The keyspace contains all the
column families (kind of like tables in
the relational model), which contain
rows, which contain columns.

18. Cont..
• The key difference between
column stores and a traditional
RDBMS is that, in a column
store, each record (think row in
an RDBMS) doesn’t require a
single value per column
• Instead, it’s possible to model
column families. A single record
may consist of an ID field, a
column family for “customer”
information, and another
column family for “order item”
information.

19. • It Also stores a time stamp with
each column Value, which helps it
to keep the data sorted in disc
• The model in Cassandra is that
rows contain columns. To access
the smallest unit of data (a
column) you have to specify first
the row name (key), then the
column name
• Each column is contained to its
row. It doesn’t span all rows like
in a relational database
• Each column contains a
name/value pair, along with a
timestamp, that’s why its is also
called a 2 dimensional key value
pair

20. Cassandra DB
engine
Architecture

21. • LSM Tree is the heart of Cassandra’s DB engine
• Partitioning Key — each column family has a
Partitioning Key. It helps with determining which
node in the cluster the data should be stored.
• Commit Log —the transactional log. It’s used for
transactional recovery in case of system failures.
It’s an append only file and provides durability.
• Memtable — a memory cache to store the in
memory copy of the data. Each node has a
memtable for each CQL table. The memtable
accumulates writes and provides read for data
which are not yet stored to disk.
• SSTable —the final destination of data in C*. They
are actual files on disk and are immutable.
• Compaction —the periodic process of merging
multiple SSTables into a single SSTable. It’s
primarily done to optimize the read operations.

22. Cassandra does Peer
to Peer Cluster
Communication
• It is a peer to peer database
where each node in the cluster
constantly communicates with
each other to share and receive
information (node status, data
ranges and so on)
• There is no concept of master
or slave in a Cassandra
cluster.Any Node can be
coordinator node for each
query

23. Writes in Cassandra
Cassandra also performs some special tasks
during/after writing the data to Cassandra.
These include :
•Compaction
•Hinted Handoff
•Caching

24. Compaction

25. Reads in Cassandra

26. Read repair
Read-repair is a lazy mechanism in Cassandra that ensures that the data you
request from the database is accurate and consistent
For every read request, the coordinator node requests to all the replica nodes
having the data requested by the client
All nodes return the data which client requested for
The most recent data is sent to the client and asynchronously, the coordinator
identifies any replicas that return obsolete data and issues a read-repair
request to each of these replicas to update their data based on the latest data.
Cassandras also uses bloom filter to optimize the reads

27. Strategies
for Reads
1.ONE — reads from the closest node holding the
data
2.QUORUM — returns a result from a quorum of
servers with the most recent timestamp of data
3.LOCAL_QUORUM — returns a result from a
quorum of servers with the most recent timestamp
for the data in the same data center as the
coordinator node
4.EACH_QUORUM — returns a result from a quorum
of servers with the most recent timestamp in all
data centers
5.ALL — returns a result from all replica nodes for a
row key

28. Cassandra suitable
for write heavy
workload
Cassandra's storage engine performs very well
on writes because it stores data in an append
only format
This makes great use of spinning disk drives
that have poor seek times
It can do serial writes very quickly
But the downside is that when you do a read,
you often need to scan through several
versions of an object to get the most recent
version to return to the caller

29. Who all are
using
Cassandra
• Twitter, Discord, Instagram, Reddit, Uber ….
• Azure Cosmos DB also supports CassandraAPIs
• “We’re using Cassandra in production for a bunch of things
atTwitter. A few examples: Our geo team uses it to store
and query their database of places of interest.The
research team uses it to store the results of data mining
done over our entire user base. Our analytics, operations
and infrastructure teams are working on a system that
uses cassandra for large-scale real time analytics for use
both internally and externally”

30. • Thank you