The document discusses the importance of database awareness in relation to system performance, scalability, and cost, emphasizing the need to understand both database and system functionality. It covers various types of databases, including document, relational, row-based, and column-based, detailing their use cases and characteristics. Additionally, it explores specific database technologies and compares features like event stores, time-series databases, Solr vs Elasticsearch, and in-memory databases.

1. Database Awareness
09 Mar 2021
@irensaltali

2. Who am I?
İren Saltalı
.NET Consultant @kloia
Blog : irensaltali.medium.com
Tweet : @irensaltali
LinkedIn : /in/irensaltali
GitHub : github.com/irensaltali
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3. Agenda
• Why we need Database Awareness?
• Document vs Relational
• Row-based vs Column-based
• In-memory Database vs In-memory Data grids
• Graph
• Time-series
• Solr vs ElasticSearch
• Event Store

4. Why we need Database Awareness?
Databases directly affect our system performance, scalability, durability, consistency,
cost, and even how we code. We need to choose the database that meets our demands best. To
do that, we have to know two main topics.
• How database works (Database Awareness)
• How our system works (System Awareness)
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5. Document
Unstructured
Frequent updates to the data structure
Application-level joins
Horizontal scaling
Document based data modeling
MongoDB, Apache CouchDB, Couchbase
Table
Schema
No/less updates to the data structure
Server-level joins
Vertical scaling
Relational data modeling
MSSQL, MySQL, PostgreSQL
vs
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Document vs Relational

6. 6
Document vs Relational – Use Cases
Document
• Content management
• Logging
• Storing third party system’s data
• Web analytics
RDMS
• Banking/Finance
• Booking
• ERP

7. 7
Row-based vs Column-based
Name City Age
İren Ankara 34
Seren Yalova 31
Bilgehan İstanbul 25
İren Ankara 34 Seren Yalova 31 Bilgehan İstanbul 25
İren Seren Bilgehan Anlara Yalova İstanbul 34 31 25
Row-based
Column-based

8. Row-based vs Column-based - Write
İren Ankara 34 Seren Yalova 31 Bilgehan İstanbul 25 Doğa Ankara 2
İren Seren Bilgehan Doğa Ankara Yalova İstanbul Ankara 34 31 25 2
Row-based
Column-based
Doğa Ankara 2
New data

9. Row-based vs Column-based - Read
İren Ankara 34 Seren Yalova 31 Bilgehan İstanbul 25 Doğa Ankara 2
İren Seren Bilgehan Doğa Ankara Yalova İstanbul Ankara 34 31 25 2
Row-based
Column-based
Select * İren Seren Bilgehan Doğa Ankara Yalova İstanbul Ankara 34 31 25 2
Select Sum(Age) İren Seren Bilgehan Doğa Ankara Yalova İstanbul Ankara 34 31 25 2

10. 10
IMDB vs IMDG
SQL support
No MPP
Replace RDBMS
Network Latency
Redis
No/less SQL support
Massively Parallel Processing
Can’t replace RDBMS
On same server with application
Hazelcast
vs

11. Graph Databases
A graph is composed of two elements: a node and a relationship.
• Nodes represent entities.
• Edges (graphs, relationships), are the lines that connect nodes
to other nodes.
• Edges can be directed or undirected.
• Edges can store properties represented by key/value pairs.
• High performance on graph-like queries.
Some graph databases: Amazon Neptune, Neo4j, OrientDB
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image from https://aws.amazon.com/nosql/graph/

12. Times Series Database (TSDB)
A time series database (TSDB) is a database optimized for time-
stamped or time series data.
• Built specifically for handling metrics and events or
measurements that are time-stamped.
• Discrete characteristics from its continuous values.
• Best for server metrics, application performance monitoring,
network data, sensor data, events, clicks, trades in a market.
Some times series databases: Prometheus, Graphite, InfluxDB, Amazon Timestream
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13. Solr vs ElasticSearch
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XML, CSV, JSON, DB, Word, Pdf
DBC, CSV, XML, Tika, URL, Flat File
REST, Schemaless
Lucene Query
Span queries, Autocomplete, Faceting, Spatial/geo search
Visualisation: Banana (Port of Kibana)
Hard to manage scaling
JSON
ActiveMQ, Amazon SQS, CouchDB, DynamoDB, FileSystem, Git,
GitHub, Hazelcast, JDBC, JMS, Kafka, LDAP, MongoDB, neo4j,
OAI, RabbitMQ, Redis, RSS, Sofa, Solr, St9, Subversion, Twitter,
Wikipedia
Schemaless
Lucene Query, Query DSL
Span queries, Autocomplete, Faceting, Spatial/geo search
Visualisation: Kibana
Built for horizontal scaling
vs

14. Event Store
An event store databases optimized for storage of events.
• Event are not allowed to be changed.
• Optimized for writes
• Reproducibility
• Snapshots
Some event stores: IBM Db2 Event Store, EventStoreDB, NEventStore
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15. Q & A
Thank you for listening.
blog.kloia.com @kloia_com
kloia.com
@irensaltali
RESOURCES MENTIONED IN
THIS SESSION WILL BE
AVAILABLE ON MY TWITTER

16. Sources
• https://www.digitalocean.com/community/tutorials/a-comparison-of-nosql-database-
management-systems-and-models
• https://www.flydata.com/blog/whats-unique-about-a-columnar-database/
• https://www.mongodb.com/blog/post/6-rules-of-thumb-for-mongodb-schema-design-part-1
• https://dataschool.com/data-modeling-101/row-vs-column-oriented-databases/
• https://www.youtube.com/watch?v=Vw1fCeD06YI
• https://en.wikipedia.org/wiki/Time_series_database
• https://www.influxdata.com/time-series-database/
• https://logz.io/blog/solr-vs-elasticsearch/
• https://solr-vs-elasticsearch.com/
• https://en.wikipedia.org/wiki/Event_store
• https://docs.microsoft.com/en-us/dotnet/architecture/cloud-native/relational-vs-nosql-data