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How to Select an Analytic DBMS Overview, checklists, and tips by Curt A. Monash, Ph.D. President, Monash Research Editor, DBMS2

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Presentation on theme: "How to Select an Analytic DBMS Overview, checklists, and tips by Curt A. Monash, Ph.D. President, Monash Research Editor, DBMS2"— Presentation transcript:

1 How to Select an Analytic DBMS Overview, checklists, and tips by Curt A. Monash, Ph.D. President, Monash Research Editor, DBMS2

2 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 ( -- the source for most of this talk) Feed at White papers and more at User and vendor consulting

3 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 ? Whats the best process for selecting an analytic DBMS?

4 Why are there specialized analytic DBMS? General-purpose database managers are optimized for updating short rows … … not for analytic query performance X price/performance differences are not uncommon At issue is the interplay between storage, processors, and RAM

5 Moores Law, Kryders 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! 1/5/2014 DRAFT!! THIRD TEST!!

6 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

7 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

8 Hardware strategies to optimize analytic I/O Lots of RAM Parallel disk access!!! Lots of networking Tuned MPP (Massively Parallel Processing) is the key

9 Specialty hardware strategies Custom or unusual chips (rare) Custom or unusual interconnects Fixed configurations of common parts Appliances or recommended configurations And theres also SaaS

10 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

11 General areas of feature differentiation Query performance Update/load performance Compatibilities Advanced analytics Alternate datatypes Manageability and availability Encryption and security

12 Major analytic DBMS product groupings Architecture is a hot subject Traditional OLTP Row-based MPP Columnar (Not covered tonight) MOLAP/array-based

13 Traditional OLTP examples Oracle (especially pre-Exadata) IBM DB2 (especially mainframe) Microsoft SQL Server (pre-Madison)

14 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

15 Drawbacks Complexity and people cost Hardware cost Software cost Absolute performance

16 Legitimate use scenarios When TCO isnt 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

17 Row-based MPP examples Teradata DB2 (open systems version) Netezza Oracle Exadata (sort of) DATAllegro/Microsoft Madison Greenplum Aster Data Kognitio HP Neoview

18 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

19 Tradeoffs among row MPP alternatives Enterprise standards Vendor size Hardware lock-in Total system price Features

20 Columnar DBMS examples Sybase IQ SAND Vertica ParAccel InfoBright Kickfire Exasol MonetDB SAP BI Accelerator (sort of)

21 Columnar pros and cons Bulk retrieval is faster Pinpoint I/O is slower Compression is easier Memory-centric processing is easier MPP is not quite as crucial

22 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

23 Basics of systematic segmentation Use cases Metrics Platform preferences

24 Use cases – a first cut Light reporting Diverse EDW Big Data Operational analytics

25 Metrics – a first cut Total raw/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

26 Basic platform issues Enterprise standards Appliance-friendliness Need for MPP? Cloud/SaaS

27 The selection process in a nutshell Figure out what youre trying to buy Make a shortlist Do free POCs* Evaluate and decide *The only part thats even slightly specific to the analytic DBMS category

28 Figure out what youre 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

29 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)

30 Use-case checklist – traditional BI Reports Today Future Dashboards and alerts Today Future Latency Ad-hoc Users Now that we have great response time …

31 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

32 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

33 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)

34 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?

35 A checklist for shortlists Whats your tolerance for specialized hardware? Whats your tolerance for set-up effort? Whats your tolerance for ongoing administration? What are your insert and update requirements? At what volumes will you run fairly simple queries? What are your complex queries like? For which third-party tools do you need support? and, most important, Are you madly in love with your current DBMS?

36 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?

37 The three big POC challenges Getting data Real? Politics Privacy Synthetic? Hybrid? Picking queries And more? Realistic simulation(s) Workload Platform Talent

38 POC tips Dont underestimate requirements Dont overestimate requirements Get SOME data ASAP Dont leave the vendor in control Test what youll be buying Use the baseball bat

39 Evaluate and decide It all comes down to Cost Speed Risk and in some cases Time to value Upside

40 Further information Curt A. Monash, Ph.D. President, Monash Research Editor, DBMS2

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