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![Page 1: slide deck](https://reader033.fdocuments.in/reader033/viewer/2022061110/54532b95af7959c07f8b6893/html5/thumbnails/1.jpg)
How to Select an Analytic DBMS
DRAFT!!
byCurt A. Monash, Ph.D.
President, Monash ResearchEditor, DBMS2
contact @monash.comhttp://www.monash.comhttp://www.DBMS2.com
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Curt Monash
Analyst since 1981, own firm since 1987 Covered DBMS since the pre-relational days Also analytics, search, etc.
Publicly available research Blogs, including DBMS2 (www.DBMS2.com -- the
source for most of this talk) Feed at www.monash.com/blogs.html White papers and more at www.monash.com
User and vendor consulting
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Our agenda
Why are there such things as specialized analytic DBMS?
What are the major analytic DBMS product alternatives?
What are the most relevant differentiations among analytic DBMS users?
What’s the best process for selecting an analytic DBMS?
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Why are there specialized analytic DBMS?
General-purpose database managers are optimized for updating short rows …
… not for analytic query performance 10-100X price/performance differences
are not uncommon
At issue is the interplay between storage, processors, and RAM
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Moore’s Law, Kryder’s Law, and a huge exception
Growth factors:
Transistors/chip:
>100,000 since 1971 Disk density:
>100,000,000 since 1956 Disk speed:
12.5 since 1956
The disk speed barrier dominates everything!
0%
5%
10%
15%
20%
25%
30%
35%
40%
45%
Compound Annual Growth Rate
Transistors/Chipssince 1971
Disk Density since 1956
Disk Speed since 1956
04/08/23 DRAFT!! THIRD TEST!!
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The “1,000,000:1” disk-speed barrier
RAM access times ~5-7.5 nanoseconds CPU clock speed <1 nanosecond Interprocessor communication can be ~1,000X slower
than on-chip
Disk seek times ~2.5-3 milliseconds Limit = ½ rotation i.e., 1/30,000 minutes i.e., 1/500 seconds = 2 ms
Tiering brings it closer to ~1,000:1 in practice, but even so the difference is VERY BIG
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Software strategies to optimize analytic I/O
Minimize data returned Classic query optimization
Minimize index accesses Page size
Precalculate results Materialized views OLAP cubes
Return data sequentially Store data in columns Stash data in RAM
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Hardware strategies to optimize analytic I/O
Lots of RAM Parallel disk access!!! Lots of networking
Tuned MPP (Massively Parallel Processing) is the key
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Specialty hardware strategies
Custom or unusual chips (rare) Custom or unusual interconnects Fixed configurations of common parts
Appliances or recommended configurations
And there’s also SaaS
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18 contenders (and there are more)
Aster Data Dataupia Exasol Greenplum HP Neoview IBM DB2 BCUs Infobright/MySQL Kickfire/MySQL Kognitio Microsoft Madison
Netezza Oracle Exadata Oracle w/o Exadata ParAccel SQL Server w/o
Madison Sybase IQ Teradata Vertica
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General areas of feature differentiation
Query performance Update/load performance Compatibilities Advanced analytics Alternate datatypes Manageability and availability Encryption and security
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Major analytic DBMS product groupings
Architecture is a hot subject
Traditional OLTP Row-based MPP Columnar (Not covered tonight) MOLAP/array-based
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Traditional OLTP examples
Oracle (especially pre-Exadata) IBM DB2 (especially mainframe) Microsoft SQL Server (pre-Madison)
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Analytic optimizations for OLTP DBMS
Two major kinds of precalculation Star indexes Materialized views
Other specialized indexes Query optimization tools OLAP extensions SQL 2003 Other embedded analytics
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Drawbacks
Complexity and people cost Hardware cost Software cost Absolute performance
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Legitimate use scenarios
When TCO isn’t an issue Undemanding performance (and therefore
administration too) When specialized features matter
OLTP-like Integrated MOLAP Edge-case analytics
Rigid enterprise standards Small enterprise/true single-instance
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Row-based MPP examples
Teradata DB2 (open systems version) Netezza Oracle Exadata (sort of) DATAllegro/Microsoft Madison Greenplum Aster Data Kognitio HP Neoview
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Typical design choices in row-based MPP
“Random” (hashed or round-robin) data distribution among nodes
Large block sizes Suitable for scans rather than random accesses
Limited indexing alternatives Or little optimization for using the full boat
Carefully balanced hardware High-end networking
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Tradeoffs among row MPP alternatives
Enterprise standards Vendor size Hardware lock-in Total system price Features
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Columnar DBMS examples
Sybase IQ SAND Vertica ParAccel InfoBright Kickfire Exasol MonetDB SAP BI Accelerator (sort of)
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Columnar pros and cons
Bulk retrieval is faster Pinpoint I/O is slower Compression is easier Memory-centric processing is easier
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Segmentation – a first cut
One database to rule them all One analytic database to rule them all Frontline analytic database Very, very big analytic database Big analytic database handled very cost-
effectively
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Basics of systematic segmentation
Use cases Metrics Platform preferences
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Use cases – a first cut
Light reporting Diverse EDW Big Data Operational analytics
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Metrics – a first cut
Total user data Below 1-2 TB, references abound 10 TB is another major breakpoint
Total concurrent users 5, 15, 50, or 500?
Data freshness Hours Minutes Seconds
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Basic platform issues
Enterprise standards Appliance-friendliness Need for MPP? (SaaS)
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The selection process in a nutshell
Figure out what you’re trying to buy Make a shortlist Do free POCs* Evaluate and decide
*The only part that’s even slightly specific to the analytic DBMS category
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Figure out what you’re trying to buy
Inventory your use cases Current Known future Wish-list/dream-list future
Set constraints People and platforms Money
Establish target SLAs Must-haves Nice-to-haves
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Use-case checklist -- generalities
Database growth As time goes by … More detail New data sources
Users (human) Users/usage (automated) Freshness (data and query results)
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Use-case checklist – traditional BI
Reports Today Future
Dashboards and alerts Today Future Latency
Ad-hoc Users Now that we have great response time …
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Use-case checklist – data mining
How much do you think it would improve results to Run more models? Model on more data? Add more variables? Increase model complexity?
Which of those can the DBMS help with anyway? What about scoring?
Real-time Other latency issues
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SLA realism
What kind of turnaround truly matters? Customer or customer-facing users Executive users Analyst users
How bad is downtime? Customer or customer-facing users Executive users Analyst users
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Short list constraints
Cash cost But purchases are heavily negotiated
Deployment effort Appliances can be good
Platform politics Appliances can be bad You might as well consider incumbent(s)
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Filling out the shortlist
Who matches your requirements in theory?
What kinds of evidence do you require? References?
How many? How relevant?
A careful POC? Analyst recommendations? General “buzz”?
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A checklist for shortlists
What is your tolerance for specialized hardware? What is your tolerance for set-up effort? What is your tolerance for ongoing administrative
burden? What are your insert and update requirements? At what volumes will you run fairly simple
queries? What are your complex queries like?
and, most important,
Are you madly in love with your current DBMS?
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Proof-of-Concept basics
The better you match your use cases, the more reliable the POC is
Most of the effort is in the set-up You might as well do POCs for several vendors –
at (almost) the same time! Where is the POC being held?
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The three big POC challenges
Getting data Real?
Politics Privacy
Synthetic? Hybrid?
Picking queries And more?
Realistic simulation(s) Workload Platform Talent
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POC tips
Don’t underestimate requirements Don’t overestimate requirements Get SOME data ASAP Don’t leave the vendor in control Test what you’ll be buying Use the baseball bat
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Evaluate and decide
It all comes down to
Cost Speed Risk
and in some cases
Time to value Upside
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Further information
Curt A. Monash, Ph.D.President, Monash Research
Editor, DBMS2
contact @monash.comhttp://www.monash.comhttp://www.DBMS2.com