An Engineering Approach to Database Evaluations

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2 likes•1,133 views

This talk will go over a methodical approach for making a decision, dig into interesting tradeoffs, and give tips about what things to look for under the hood and how to evaluate the tech behind the database.

An Engineering Approach
to Database Evalua5ons
Drew Paroski, MemSQL, VP of Engineering
Adam Prout, MemSQL, Chief Architect
MemSQL 1

8 Criteria To Keep In Mind While
Looking For Your Next Database
MemSQL 3

Do you understand anything they're saying?
Oh yes master Luke remember that I am ﬂuent in over 6
million forms of communica9on
MemSQL 4

1/ Pick the right language(s) including SQL
• Surface area supported: Joins, Aggregates, sub-queries, CTEs,
Window func>ons
• Parallelism: In a single machine, across a cluster of machines
• Query op>mizer maturity
• Proﬁling and query tuning support
MemSQL 5

2/ Performance
• Making use of modern hardware
• i.e. SIMD, ﬂash/NVMe
• Code Genera>on
MemSQL 7

3/ Database storage technology
• Columnstore, Rowstore, Documentstore
• Index types
• B-Tree, LSM-Tree, hash table, min-max index
• In-Memory AND On-Disk storage
MemSQL 9

Focus on Transac+ons
"We can pay you two thousand now,
plus ﬁ4een when we reach Alderaan"
MemSQL 10

4/ Transac*onality
• Point-updates and mass updates
• File systems (HDFS) are not intended for this func<onality
MemSQL 11

5/ Protec*on and durability
• Replica)on support (synchronous, asynchronous, log based,
statement based)
• Built-in transparent high availability or manual setup
• Backup and Restore support
MemSQL 13

6/ UDFs and Stored Procedures
• Custom func,ons
• Advantages of in-database opera,ons
MemSQL 15

7/ Data Ingest
• Fast, con+nuous, streaming ingest
• Running queries concurrently with ingest
MemSQL 17

8/ Security
• Encryp(on; Authen(ca(on
• Role Based Access Control
• Audit Logging; Strict Mode
MemSQL 19

Congratula*ons!
You are cer)ﬁed to rule the database universe
MemSQL 20

drew@memsql.com and adam@mesql.com
MemSQL 21

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The document discusses real-time image recognition using Apache Spark. It describes how images are analyzed to extract histogram of oriented gradients (HOG) descriptors, which are stored as feature vectors in a MemSQL table. Similar images can then be identified by comparing feature vectors using dot products, enabling searches of millions of images per second. A demo is shown generating HOG descriptors from an image and storing them as a vector for fast similarity matching.

The State of the Data Warehouse in 2017 and Beyond

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The document provides an overview of the changing analytic environment and the evolution of the data warehouse. It discusses how new requirements like performance, usability, optimization, and ecosystem integration are driving the adoption of a real-time data warehouse approach. A real-time data warehouse is described as having low latency ingestion, in-memory and disk-optimized storage, and the ability to power both operational and machine learning applications. Examples are given of companies using a real-time data warehouse to enable real-time analytics and improve business processes.

Teaching Databases to Learn in the World of AI

SingleStore

The document discusses how databases need to learn and adapt like artificial intelligence in order to power real-time applications, highlighting that databases must be simple, capable of real-time processing, and adaptable by learning behaviors and making autonomous decisions. It also promotes MemSQL's vision of teaching databases to learn by consolidating infrastructure, enabling real-time queries on fresh data, and allowing both transactions and analytics workloads.

Gartner Catalyst 2017: Image Recognition on Streaming Data

SingleStore

This document discusses using MemSQL to perform real-time image recognition on streaming data. Key points include: - Feature vectors extracted from images using models like TensorFlow can be stored in MemSQL tables for analysis. - MemSQL allows querying these feature vectors to find similar images based on cosine similarity calculations. - This enables applications like detecting duplicate or illegal images in real-time streams.

Spark Summit West 2017: Real-Time Image Recognition with MemSQL and Spark

SingleStore

Real-Time Analytics at Uber Scale

SingleStore

James Burkhart explains how Uber supports millions of analytical queries daily across real-time data with Apollo. James covers the architectural decisions and lessons learned building an exactly-once ingest pipeline storing raw events across in-memory row storage and on-disk columnar storage and a custom metalanguage and query layer leveraging partial OLAP result set caching and query canonicalization. Putting all the pieces together provides thousands of Uber employees with subsecond p95 latency analytical queries spanning hundreds of millions of recent events.

Machines and the Magic of Fast Learning

SingleStore

Human-machine interaction is no longer the exclusive province of science fiction. The advance of the internet and connected devices has inspired data scientists to create machine-learning applications to extract value from these new forms of data. So what's the next frontier? Join MemSQL Engineer Michael Andrews and Sr. Director Mike Boyarski to learn how to use real-time data as a vehicle for operationalizing machine-learning models. Michael and Mike will explore advanced tools, including TensorFlow, Apache Spark, and Apache Kafka, and compelling use cases demonstrating the power of machine learning to effect positive change. You will learn: Top technologies for building the ideal machine-learning stack How to power machine-learning applications with real-time data A use case and demo of machine learning for social good

Machines and the Magic of Fast Learning - Strata Keynote

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Enabling Real-Time Analytics for IoT

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Real-Time Analytics with Spark and MemSQL

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Driving the On-Demand Economy with Predictive Analytics

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An Engineering Approach to Database Evaluations

1. An Engineering Approach to Database Evalua5ons Drew Paroski, MemSQL, VP of Engineering Adam Prout, MemSQL, Chief Architect MemSQL 1

2. MemSQL 2

3. 8 Criteria To Keep In Mind While Looking For Your Next Database MemSQL 3

4. Do you understand anything they're saying? Oh yes master Luke remember that I am ﬂuent in over 6 million forms of communica9on MemSQL 4

5. 1/ Pick the right language(s) including SQL • Surface area supported: Joins, Aggregates, sub-queries, CTEs, Window func>ons • Parallelism: In a single machine, across a cluster of machines • Query op>mizer maturity • Proﬁling and query tuning support MemSQL 5

6. Aim for Peak Performance MemSQL 6

7. 2/ Performance • Making use of modern hardware • i.e. SIMD, ﬂash/NVMe • Code Genera>on MemSQL 7

8. Choose the right storage model MemSQL 8

9. 3/ Database storage technology • Columnstore, Rowstore, Documentstore • Index types • B-Tree, LSM-Tree, hash table, min-max index • In-Memory AND On-Disk storage MemSQL 9

10. Focus on Transac+ons "We can pay you two thousand now, plus ﬁ4een when we reach Alderaan" MemSQL 10

11. 4/ Transac*onality • Point-updates and mass updates • File systems (HDFS) are not intended for this func<onality MemSQL 11

12. Protect with redundancy MemSQL 12

13. 5/ Protec*on and durability • Replica)on support (synchronous, asynchronous, log based, statement based) • Built-in transparent high availability or manual setup • Backup and Restore support MemSQL 13

14. Invest in Procedures MemSQL 14

15. 6/ UDFs and Stored Procedures • Custom func,ons • Advantages of in-database opera,ons MemSQL 15

16. Prepare for Fast Ingest MemSQL 16

17. 7/ Data Ingest • Fast, con+nuous, streaming ingest • Running queries concurrently with ingest MemSQL 17

18. Guarantee Security MemSQL 18

19. 8/ Security • Encryp(on; Authen(ca(on • Role Based Access Control • Audit Logging; Strict Mode MemSQL 19

20. Congratula*ons! You are cer)ﬁed to rule the database universe MemSQL 20

21. drew@memsql.com and adam@mesql.com MemSQL 21

An Engineering Approach to Database Evaluations

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