Determine the Right Analytic Database: A Survey of New Data Technologies

Transcription

1 Determine the Right Analytic Database: A Survey of New Data Technologies O Reilly Strata Conference February 1, 2011 Mark R. Madsen Atomic Avenue #1 by Glen Orbik Key Questions What technologies are available? What are they good for? How do you decide which to use? But first: why are analytic databases available now? Page 2

2 Consequences of Commoditization: Data Volume Spimes Chipping RFID Sensors GPS Data Generated You are here Time H

3 Lots of H More can become a qualitative rather than quantitative difference Really lots of H Databases are dead! famous last words

4 An Unexpected Consequence of Data Volumes Sums, counts and sorted results only get you so far. An Unexpected Consequence of Data Volumes Our ability to collect data is still outpacing our ability to derive meaning from it.

5 Don t worry about it. We ll just buy more hardware. CPUs, memory and storage track to very similar curves RIP Moore s Law: it nearly ground to a halt for silicon integrated circuits about four years ago.

6 Technology Has Changed (a lot) But We Haven t Calculations per second per $ ,000 X improvement Current DW architecture and methods start here in the mid-1980s Data: Ray Kurzweil, Mechanical Relay Vacuum tube Transistor Integrated circuit Multicore performance is not a linear extrapolation.

7 New Technology Evolution Means New Problems Uniprocessor and custom CPU era Symmetric multiprocessing era Massively parallel era Early engineering phase Exploring, learning, inventing Investment phase Improving, perfecting, applying Core problems solved Technology Maturity (time + engineering effort) What s different? Parallelism We re not getting more CPU power, but more CPUs. There are too many CPUs relative to other resources, creating an imbalance in hardware platforms. Most software is designed for a single worker, not high degrees of parallelism and won t scale well.

8 Core problem: software is not designed for parallel work Databases must be designed to permit local work with minimal global coordination and data redistribution. SOME TECHNOLOGY INNOVATIONS

9 Storage Improvements For data workloads, disk throughput still key. Improvements: Spinning disks at.05/gb Solid state disks remove some latencies, read speed of ~250MB/sec SSD capacity still rising Card storage (PCI), e.g. FusionIO at 1.5GB/sec SSD is still costly at $2/GB up to $30/GB Compression Applied to Stored Data 10x compression means 1 disk I/O can read 10x as much data, stretching your current hardware investment But it eats CPU and memory. YMMV

10 Scale up vs. Scale out Parallelism Uniprocessor environments required chip upgrades. SMP servers can grow to a point, then it s a forklift upgrade to a bigger box. MPP servers grow by adding mode nodes. Database and Hardware Deployment Models Three levels of software hardware integration: Database appliance (specialized hardware and software) Preconfigured (commodity) hardware with software Software on generic hardware Then there are the hardware database parallel models: Database DB DB Database OS OS OS OS OS OS Shared Everything Shared Disk Shared Nothing Page 20

11 In Memory Processing 1. Maybe not as fast you think. Depends entirely on the database (e.g. VectorWise) 2. So far, applied mainly to shared nothing models 3. Very large memories are more applicable to shared nothing than shared memory systems Box limited Limited by node scaling e.g. 2 TB max e.g. 16 nodes, 512MB per = 8TB 4. Still an expensive way to get performance Columnar Databases ID Name Salary 1 Marge Inovera $50,000 2 Anita Bath $120,000 3 Nadia Geddit $36,000 In a row-store model these three rows would be stored in sequential order as shown here, packed into a block Marge Inovera Anita Bath Nadia Geddit $50,000 $120,000 $36,000 In a column store model database they would be divided by columns and stored in different blocks. Not just changing the storage layout. Also involves changes to the execution engine and query optimizer.

12 Column Stores Rule the TPC H Benchmark Columnar Advantages and Disadvantages + Reduced I/O for queries not reading all columns + Better compression characteristics, meaning database size < raw data size (unlike row store) and less I/O + Ability to operate on compressed data, improving overall system performance +Less manual tuning Slower inserts and updates (causing ELT and tricklefeed problems*) Worse for small retrievals and random I/O Uses more system memory and CPU

13 Explosion of Analytic Techniques Machine learning GIS Visualization Statistics Information theory & IR Advanced Analytic Methods Numerical methods Rules engines & constraint programming Text mining & text analytics Map Reduce is a parallel programming framework that allows one to code more easily across a distributed computing environment, not a database. So how do I query the database? It s not a database, it s a keyvalue store! Ok, it s not a database How do I query it? You write a distributed mapreduce function in erlang. Did you just tell me to go to hell? I believe I did, Bob.

14 What s Different No database No schema No metadata No query language* Good for: Processing lots of complex or non relational data Batch processing for very large amounts of data * Hive, Hbase, Pig, others Using MapReduce / Hadoop Hadoop is one implementation of MapReduce. There are different variations with different performance and resource characteristics e.g. Dryad, CGL MR, MPI variants Hadoop is only part of the solution. You need more for enterprise deployment. Cloudera s distribution for Hadoop shows what a complete environment could look like. 28 Image: Cloudera

15 How Hadoop fits into a traditional BI environment Developers Analysts End Users Development tools and IDEs Analysis tools, BI BI, Applications Data Warehouse File loads ETL Databases Documents Flat Files XML Queues ERP Applications Source Environments NoSQL theoretically = not only sql, in reality Data stores that augment or replace relational access and storage models with other methods. Different storage models: Key value stores Column families Object/ document stores Graphs Different access models: SQL(rarely) programming API get/put Reality: mostly suck for BI & analytics Analytic DB vendors are coming from the other direction: Aster Data SQL wrapped around MR EMC (Greenplum) MR on top of the database 30

16 Some realities to consider Cheap performance? Do you have 20 blades lying around unused? How much concurrency? How much effort to write queries? Debug them? Performance comparisons: 10x slower on the same hardware? The key is the workload type and the scale of it. Page 31 Do you really need a rack of blades for computing? Graphics co processors have been used for certain problems for years. Offer single system solution to offload very large computeintensive problems. Order of magnitude cost reduction, order of magnitude performance increase with current technology today (for compute intensive problems). We ve barely started with this.

17 Other Options for analytic software deployment The basic models. 1. Separate tools and systems (MapReduce and nosql are a simple variation on this theme) 2. Integrated with a database 3. Embedded in a database The primary arguments about deployment models center on whether to take data to the code or code to the data. 33 Leveraging the Database Levels of database integration: Native DB connector External integration Internal integration Embedded + Less data movement + Possible dev process support + Hardware / environment savings + Possible sandboxing support Limitations on techniques 34

18 In database Execution You can do a lot with standardscompliant SQL If the database has UDFs, you can code too (but it s harder) Parallel support for UDFs varies Some vendors build functions directly into the database, (usually scalar) Iterative algorithms (ones that converge on a solution) are problematic, more so in MPP 35 What are factors in the decision? User concurrency: one job or many Repetition is a key element: Execute once and apply (build a response or mortality model) Many executions daily (web cross sells) In process or Batch? Batch and use results segment, score In process reacts on demand detect fraud, recommend In process requires thinking about how it integrates with the calling application. (SQL sometimes not your friend) 36

19 MATCHING THE PROBLEMS TO TECHNOLOGIES The problem of size is three problems of volume. Computations! Amount of data! Number of users!

20 Hardware Architectures and Deployment Compute and data sizes are the key requirements Computations MF GF TF PF PC Shared nothing Shared everything or shared disk MR and related <10s GB 100s GB 1s TB 10s TB 100sTB PB Data volume 39 Hardware Architectures and Deployment Today s reality, and true for a while in most businesses. Computations MF GF TF PF The bulk of the market resides here! <10s GB 100s GB 1s TB 10s TB 100sTB PB Data volume 40

21 Hardware Architectures and Deployment Today s reality, and true for a while in most businesses. Computations MF GF TF PF but analytics pushes many things into the MPP zone. The bulk of the market resides here! <10s GB 100s GB 1s TB 10s TB 100sTB PB Data volume 41 The real question: why do you want a new platform? Trouble doing what you already do today Poor response times Not meeting availability deadlines Doing more of what you do today Adding users, mining more data Doing something new with your data Data mining, recommendations, embedded real time process support What s desired is possible but limited by the cost of supporting or growing the existing environment. Page 42

22 The World According to Gartner: One Magical Quadrant SQL Server 2008 R2 (PDW) Official production customers? EMC / Greenplum SQL limitations Memory / concurrency issues Ingres OLTP database Illuminate SQL limitations Very limited scalability Sun MySQL for a DW, is this a joke? Magic Quadrant for Data Warehouse Database Management Systems 43 The assumption of the warehouse as a database is gone Non-traditional data (logs, audio, documents) Parallel programming platforms Message streams Traditional tabular or structured data Databases Streaming DBs/engines Data at rest Data in motion Copyright Third Nature, Inc. 44 Slide 44

23 Data Access Differences Basic data access styles: Standard BI and reporting Dashboards / scorecards Operational BI Ad hoc query and analysis Batch analytics Embedded analytics Data loading styles: Refresh Incremental Constant Evaluating ADB Options Storage style: Files, tables, columns, cubes, KV Storage type: Memory, disk, hybrid, compressed Scaling model: SMP, clustered, MPP, distributed Deployment model: Appliance, cloud, SaaS, on premise Data access model: SQL, MapReduce, R, languages, etc. License options: CPU, data size, subscription Page 46

24 What s it going to cost? A small sample at list: Solution Pricing model Price/unit 1 TB solution Remarks DatAupia Node $ 19,500/2TB $ 19,500 You can t buy a 1 TB Satori server Kickfire (out of business) Data Volume (raw) $ 50,000,-/TB $ 50,000 Includes MySQL 5.1 Enterprise Vertica Data Volume (raw) $ 100,000/TB $ 200,000 Based on 5 nodes, $ 20,000 each ParAccel Data Volume (raw) $ 100,000/TB $ 200,000 Based on 5 nodes, $ 20,000 each EXASOL Data Volume (active) $ 1,350/GB ( 1,000/GB) $ 350,000* Based on 4 nodes, $ 20,000 each Teradata Node $ 99,000 / TB $ 99,000** Based on 2550 base configuration * 1TB raw ± 200 GB active, **realistic configuration likely 2x this price 47 Factors and Tradeoffs The core tradeoff is not always money for performance. What else do you trade? Load time Trickle feeds New ETL tools New BI tools Operational complexity: Data integration and management Backups Hardware maintenance Page 48

25 The Path to Performance 1. Laborware tuning 2. Upgrade try to solve the problem without changing out the database 3. Extend add an ADB or Hadoop cluster to the environment to offload a specific workload 4. Replace out with the old, in with the new Page 49 One Word: PoC!

26 The Future Assuming database market embraces MPP, you have compute power that exceeds what the DB itself needs. Why not execute the code at the data? Even without MPP, moving to in database analytic processing is a future direction and is workable for a large number of people. 51 Thank you!

27 Image Attributions Thanks to the people who supplied the images used in this presentation: Atomic Avenue #1 by Glen Orbik spices.jpg Black hole galaxy weaver peru.jpg rc toy truck.jpg automat purple2.jpg open_air_market_bologna bored_girl.jpg path_vecchia.jpg fast kids truck peru.jpg What s best for which types of problems?* Shared nothing will be best for solving large data problems, regardless of workload or concurrency. Column stores will improve query response time problems for most traditional query and aggregation workloads. Row stores will be better for operational BI or embedded BI. Fast storage always makes things better, but is only cost effective for medium scale or smaller data. Compression will help everyone, but column stores more than row stores because of how the engines work. Map Reduce and distributed filesystems offer advantages of a schemaless storage & analytic layer that can process into relational databases. SMP and in memory will be better for high complexity problems under moderate data scale, shared nothing and MR for large data scale. *The answer is always it depends Page 54

28 About the Presenter Mark Madsen is president of Third Nature, a technology research and consulting firm focused on business intelligence, analytics and performance management. Mark is an award-winning author, architect and former CTO whose work has been featured in numerous industry publications. During his career Mark received awards from the American Productivity & Quality Center, TDWI, Computerworld and the Smithsonian Institute. He is an international speaker, contributing editor at Intelligent Enterprise, and manages the open source channel at the Business Intelligence Network. For more information or to contact Mark, visit About Third Nature Third Nature is a research and consulting firm focused on new and emerging technology and practices in business intelligence, data integration and information management. If your question is related to BI, open source, web 2.0 or data integration then you re at the right place. Our goal is to help companies take advantage of information-driven management practices and applications. We offer education, consulting and research services to support business and IT organizations as well as technology vendors. We fill the gap between what the industry analyst firms cover and what IT needs. We specialize in product and technology analysis, so we look at emerging technologies and markets, evaluating the products rather than vendor market positions.