One Size Doesn’t Fit All:
The New Database Revolution Mark Madsen & Robin Bloor

Your Host
Eric.kavanagh@bloorgroup.com

Analysts Host
Bloor Madsen

Introduction Significant and revolutionary changes
are taking place in database technology In order to investigate and analyze these changes and where they may lead, The Bloor Group has teamed up with Third Nature to launch an Open Research project. This is the final webinar in a series of webinars and research activities that have comprised part of the project All published research will be made available through our web site: Databaserevolution.com

Sponsors of This Research

General Webinar Structure Market Changes,
Database Changes (Some Of The Findings) Workloads, Characteristics, Parameters A General Discussion of Performance How to Select A Database

Market Changes, Database
Changes

Database Performance Bottlenecks CPU saturation
Memory saturation Disk I/O channel saturation Locking Network saturation Parallelism – inefficient load balancing

Big Data = Scale Out

Cloud Hardware Architecture • It’s
a scale-out model. Uniform virtual node building blocks. • This is the future of software deployments, albeit with increasing node sizes, so paying attention to early adopters today will pay off. • This implies that an MPP database architecture will be needed for scale. X

Multiple Database Roles Now there
are more...

The Origin of Big Data

Let’s Stop Using the Term
NoSQL As the graph indicates, it’s just not helpful. In fact it’s downright confusing.

NoSQL Directions Some NDBMS do
not attempt to provide all ACID properties. (Atomicity, Consistency, Isolation, Durability) Some NDBMS deploy a distributed scale-out architecture with data redundancy. XML DBMS using XQuery are NDBMS. Some documents stores are NDBMS (OrientDB, Terrastore, etc.) Object databases are NDBMS (Gemstone, Objectivity, ObjectStore, etc.) Key value stores = schema-less stores (Cassandra, MongoDB, Berkeley DB, etc.) Graph DBMS (DEX, OrientDB, etc.) are NDMBS Large data pools (BigTable, Hbase, Mnesia, etc.) are NDBMS

The Joys of SQL? SQL:
very good for set manipulation. Works for OLTP and many query environments. Not good for nested data structures (documents, web pages, etc.) Not good for ordered data sets Not good for data graphs (networks of values)

The “Impedance Mismatch” The RDBMS
stores data organized according to table structures The OO programmer manipulates data organized according to complex object structures, which may have specific methods associated with them. The data does not simply map to the structure it has within the database Consequently a mapping activity is necessary to get and put data Basically: hierarchies, types, result sets, crappy APIs, language bindings, tools

The SQL Barrier SQL has:
DDL (for data definition) DML (for Select, Project and Join) But it has no MML (Math) or TML (Time) Usually result sets are brought to the client for further analytical manipulation, but this creates problems Alternatively doing all analytical manipulation in the database creates problems

Hadoop/MapReduce Hadoop is a parallel
processing environment Map/Reduce is a parallel processing framework Hbase turns Hadoop into a database of a kind Hive adds an SQL capability Pig adds analytics

Market Forces A new set
of products appear They include some fundamental innovations A few are sufficiently popular to last Fashion and marketing drive greater adoption Products defects begin to be addressed They eventually challenge the dominant products

Market forces affecting database choice Performance: trouble doing what you already do today ▪ Poor response times ▪
Not meeting data availability requirements Scalability: doing more of what you do today ▪ Adding users, processing more data Capability: doing something new with your data ▪ Data mining, recommendations, real‐time Cost or complexity: working more efficiently ▪ Consolidating / rehosting to simplify and reduce cost What’s desired is possible but limited by the cost of growing and supporting the existing environment. Page 20

Relational has a good conceptual model, but a prematurely standardized implementation The relational database is the franchise technology for storing and retrieving data, but… 1.
Global, static schema model 2. No rich typing system 3. No concept of ordering, creating challenges with e.g. time series 4. Many are not a good fit for network parallel computing, aka cloud 5. Limited API in atomic SQL statement syntax & simple result set return 6. Poor developer support

Big data? Unstructured data isn’t really unstructured. The problem is that this data is unmodeled.
The real challenge is complexity.

Text, Objects and Data Don’t Always Fit Together So this is what
they meant by “impedance mismatch”

Many new choices, one way to look at them
http://blog.nahurst.com/visual-guide-to-nosql-systems

What About Analytics? Machine learning
Visualization Statistics GIS Advanced Analytic Information Methods Numerical theory & IR methods Rules Text mining engines & & text constraint analytics programming

The holy grail of databases under current market hype A key problem is that we’re talking mostly about computation over data when we talk about “big data” and
analytics, a potential mismatch for both relational and nosql.

Technologies are not perfect replacements for one another. When replacing the old with the new (or ignoring
the new over the old) you always make tradeoffs, and usually you won’t see them for a long time.

Scalability and performance are not the same thing

Performance measures Throughput: the number of tasks completed in a given time period A measure of how much
work is or can be done by a system in a set amount of time, e.g. TPM or data loaded per hour. It’s easy to increase throughput without improving response time. Page 29

Performance measures Response time: the speed of a single task Response time is usually the measure of an
individual's experience using a system. Response time = time interval / throughput Page 30

Scalability vs throughput vs response time
Scalability = consistent performance for a task over an
increase in a scale factor

Scale: Data Volume The different ways people count make establishing rules of thumb for sizing hard. How do you measure it?
▪ Row counts ▪ Transaction counts ▪ Data size ▪ Raw data vs loaded data ▪ Schema objects People still have trouble scaling for databases as large as a single PC hard drive.

Scale: Concurrency (active and passive)

Scalability relationships As concurrency increases, response time (usually) decreases, This can be addressed
somewhat via workload management tools. When a system hits a bottleneck, response time and throughput will often get worse, not just level off.

Scale: Computational Complexity

A key point worth remembering: Performance over size <> performance over complexity Analytics performance is about the intersection of both. Database performance for BI is mostly related to size and query complexity.
Size, computational complexity and concurrency are the three dimensions that constrain a product’s performance. Workloads fall somewhere along all three.

Solving Your Problem Depends on the Diagnosis

Three General Workloads Online Transaction Processing ▪ Read, write, update ▪
User concurrency is the common performance limiter ▪ Low data, compute complexity Business Intelligence / Data warehousing ▪ Assumed to be read‐only, but really read heavy, write heavy, usually separated in time ▪ Data size is the common performance limiter ▪ High data complexity, low compute complexity Analytics ▪ Read, write ▪ Data size and complexity of algorithm are the limiters ▪ Moderate data , high compute complexity

Types of workloads Write‐biased: Read‐biased: ▪ OLTP
▪ Query ▪ OLTP, batch ▪ Query, simple retrieval ▪ OLTP, lite ▪ Query, complex ▪ Object persistence ▪ Query‐hierarchical / ▪ Data ingest, batch object / network ▪ Data ingest, real‐time ▪ Analytic Mixed Inline analytic execution, operational BI

Technology choice depends on workload & need Optimizing for: ▪ Response time?
▪ Throughput? ▪ both? Concerned about rapid growth in data? Unpredictable spikes in use? Extremely low latency (in or out) requirements? Bulk loads or incremental inserts and/or updates?

Important workload parameters to know
• Read‐intensive vs. write‐intensive

Important workload parameters to know
• Read‐intensive vs. write‐intensive
• Mutable vs. immutable data

Important workload parameters to know • Read‐intensive vs. write‐intensive • Mutable vs. immutable data
• Immediate vs. eventual consistency

Important workload parameters to know • Read‐intensive vs. write‐intensive • Mutable vs. immutable data
• Immediate vs. eventual consistency • Short vs. long access latency

Important workload parameters to know • Read‐intensive vs. write‐intensive • Mutable vs. immutable data
• Immediate vs. eventual consistency • Short vs. long data latency • Predictable vs. unpredictable data access patterns

Important workload parameters to know • Read‐intensive vs. write‐intensive • Mutable vs. immutable data
• Immediate vs. eventual consistency • Short vs. long data latency • Predictable vs. unpredictable data access patterns • Simple vs. complex data types

You must understand your workload mix ‐ throughput and response time requirements aren’t enough.
▪ 100 simple queries accessing month‐to‐date data ▪ 90 simple queries accessing month‐to‐date data and 10 complex queries using two years of history ▪ Hazard calculation for the entire customer master ▪ Performance problems are rarely due to a single factor.

Selectivity and number of columns queried Row store or column store, indexed or not? Chart from “The
Mimicking Octopus: Towards a one-size-fits-all Database Architecture”, Alekh Jindal

Characteristics of query workloads Workload Selectivity Retrieval
Repetition Complexity Reporting / BI Moderate Low Moderate Moderate Dashboards / Moderate Low High Low scorecards Ad‐hoc query and Low to Moderate Low Low to analysis high to low moderate Analytics (batch) Low High Low to High Low* Analytics (inline) High Low High Low* Operational / High Low High Low embedded BI * Low for retrieving the data, high if doing analytics in SQL

Characteristics of read‐write workloads Workload Selectivity Retrieval
Repetition Complexity Online OLTP High Low High Low Batch OLTP Moderate to Moderate High Moderate to low to high high Object High Low High Low persistence Bulk ingest Low (write) n/a High Low Realtime ingest High (write) n/a High Low With ingest workloads we’re dealing with write-only, so selectivity and retrieval don’t apply in the same way, instead it’s write volume.

Workload parameters and DB types at data scale Workload Write‐ Read‐ Updateable
Eventual Un‐ Compute parameters biased biased data consistency predictable intensive ok? query path Standard RDBMS Parallel RDBMS NoSQL (kv, dht, obj) Hadoop* Streaming database You see the problem: it’s an intersection of multiple

Problem: Architecture Can Define Options

A general rule for the read‐write axes As workloads increase in both intensity and complexity, we move into a realm of specialized databases adapted to specific workloads.
NewSQL Read intensity NoSQL OldSQL Write intensity

In general… Relational row store databases for conventionally tooled low to mid‐scale OLTP Relational databases for ACID requirements Parallel databases (row or column) for unpredictable or
variable query workloads Specialized databases for complex data query workjloads NoSQL (KVS, DHT) for high scale OLTP NoSQL (KVS, DHT) for low latency read‐mostly data access Parallel databases (row or column) for analytic workloads over tabular data NoSQL / Hadoop for batch analytic workloads over large data volumes

How To Select A Database

How To Select A Database
- (1) 1. What are the data management requirements and policies (if any) in respect of: - Data security (including regulatory requirements)? - Data cleansing? - Data governance? - Deployment of solutions in the cloud? - If a deployment environment is mandated, what are its technical characteristics and limitations? Best of breed, no standards for anything, “polyglot persistence” = silos on steroids, data integration challenges, shifting data movement architectures 2. What kind of data will be stored and used? - Is it structured or unstructured? - Is it likely to be one big table or many tables?

How To Select A Database
- (2) 3. What are the data volumes expected to be? - What is the expected daily ingest rate? - What will the data retention/archiving policy be? - How big do we expect the database to grow to? (estimate a range). 4. What are the applications that will use the database? - Estimate by user numbers and transaction numbers - Roughly classify transactions as OLTP, short query, long query, long query with analytics. - What are the expectations in respect of growth of usage (per user) and growth of user population? 5. What are the expected service levels? - Classify according to availability service levels - Classify according to response time service levels - Classify on throughput where appropriate

How To Select A Database
- (3) 6. What is the budget for this project and what does that cover? 7. What is the outline project plan? - Timescales - Delivery of benefits - When are costs incurred? 8. Who will make up the project team? - Internal staff - External consultants - Vendor consultants 9. What is the policy in respect of external support, possibly including vendor consultancy for the early stages of the project?

How To Select A Database
- (4) 10.What are the business benefits? - Which ones can be quantified financially? - Which ones can only be guessed at (financially)? - Are there opportunity costs?

A random selection of databases
Sybase IQ, ASE EnterpriseDB Algebraix Teradata, Aster Data LucidDB Intersystems Caché Oracle, RAC Vectorwise Streambase Microsoft SQLServer, PDW MonetDB SQLStream IBM DB2s, Netezza Exasol Coral8 Paraccel Illuminate Ingres Kognitio Vertica Postgres EMC/Greenplum InfiniDB Cassandra Oracle Exadata 1010 Data CouchDB SAP HANA SAND Mongo Infobright Endeca Hbase MySQL Xtreme Data Redis MarkLogic IMS RainStor Tokyo Cabinet Hive Scalaris And a few hundred more…

Product Selection Preliminary investigation Short-list
(usually arrived at by elimination) Be sure to set the goals and control the process. Evaluation by technical analysis and modeling Evaluation by proof of concept. Do not be afraid to change your mind Negotiation

Conclusion Wherein all is revealed,
or ignorance exposed

Thank You
For Your
Attention