DATABASE DESIGN II - 1DL400

Transcription

1 DATABASE DESIGN II - 1DL400 Fall 2016 A second course in database systems Kjell Orsborn Uppsala Database Laboratory Department of Information Technology, Uppsala University, Uppsala, Sweden 13/12/16 1

2 Introduction to NoSQL Databases Kjell Orsborn and Tore Risch Department of Information Technology Uppsala University, Uppsala, Sweden 13/12/16 2

3 DataBase Management Systems, DBMS and Data managers, key/value stores DataBase Management Systems e.g. Oracle, DB2, SQL Server, MySQL Data managers, key/value stores e.g. BerkeleyDB, Hadoop, Riak, Hbase, Cassandra, DynamoDB Dynamic SQL Queries Java/C++/JavaScript programs DBMS Query Processor Data manager Data Manager Meta data Stored Data Stored Data 13/12/16 3

4 Evolution of DBMS technology Distributed databases MapReduce HIVE Google F1 SQL NoSQL SQL Files IMS CODASYL RDB Object Stores (key/value) ORDB Federated Streaming data Cloud databases Highly Distributed databases DSMS BIG table F1 Databases Web sources NoSQL SQL NoSQL SQL+ SQL+ New QL SQL- SQL+ 13/12/16 4

5 Kinds of DBMS support Query (SQL) Relational DBMSs Object-Relational DBMSs No Query (NoSQL) File systems (scalable) Storage managers Object Stores Simple Data Complex data 13/12/16 5

6 Classification of Modern Database applications Complex Queries SQL, New QL Business operations Business analytics Personal db Multimedia search Custom Datatypes Data streams Web analytics Simple Queries SQL- Web shop E-business Web search No Queries NoSQL Text, data logs Simple computations Web documents CAD system Complex computations Web surfing Simple Data Complex data 13/12/16 6

7 Kind of database support Complex Queries SQL, New QL Relational DBMS Object-Relational DBMS Data Stream Mgmt. Syst. Google F1 Simple Queries SQL- No Queries NoSQL Google App Engine (GQL) Amazon SimpleDB Microsoft Azure File systems Content Mgmt. Syst. Google search engine MongoDB Object Stores Google BigTable Yahoo Hadoop CouchDB Simple Data Complex data 13/12/16 7

8 What is a NoSQL Database? A key/value store Basic index manager, no complete query language E.g. Google BigTable, Amazon Dynamo An extensible column-based database E.g. Cassandra, Hbase A web document database For web documents, not for small business transactions E.g. MongoDB, CouchDB A DBMS with a limited query language Provides for high volume small business transactions Sometimes called cloud databases E.g. Google App Engine, Microsoft Azure, Amazon SimpleDB; MongoDB 13/12/16 8

9 Who is using them? 13/12/16 9

10 What is a NoSQL Database? A DBMS where MapReduce is used instead of queries Manual programs to iterate over entire data sets E.g. Hadoop, CouchDB, Dynamo A MapReduce engine with a query language on top: HIVE on top of Hadoop provides HiveQL Provides non-procedural data analytics (select from groupby) without detailed programming Executed in batch as parallel Hadoop jobs A DBMS with a new query language for new applications Streambase, Virtuoso, Neo4J, Amos II, Google F1 Other non-relational databases Including Object Stores, array databases, etc. 13/12/16 10

11 NoSQL Characteristics Highly distributed and parallel architectures Typically runs on data centers This is similar to parallel databases! Highly scalable systems by compromised consistency No 2-phase commit as in distributed databases Eventual consistency Or perhaps never consistency Similar options available in modern DBMSs too Puts burden on programmer to handle consistency! Race conditions Recovery Customized implementation of transactions WARNING: Consistency best handled in kernel! Mainly suitable for applications not needing consistency 13/12/16 11

12 NoSQL Characteristics New query languages for new applications SQL- for cloud databases Many simple web applications do not need full SQL Simple SQL permits high scalability Familiar model Full SQL too complex for new systems Graph query languages SPARQL for RDF RDF data model For searching linked data ( Stream query languages CQL Variant of SQL for streams AmosQL (UU) Functional parallel data stream query language 13/12/16 12

13 MapReduce Parallel batch processing using MapReduce Many NoSQL databases uses MapReduce for parallel batch processing of data stored in data centers Highly scalable implementation of parallel batch processing of same (e.g. Java) program over large amounts of data stored in different files based on a scalable file system (e.g. HDFS) The MapReduce function: Applies a (costly) user function mapper producing key/value pairs in parallel on many nodes accessing files in a cluster Applies a user aggregate function on the key/value pairs produced by the mapper Very similar to GROUP BY in SQL Read reference article on MapReduce 13/12/16 13

14 MapReduce File I/O Data file Map Partition Data file Data file Data file Map Map Map Partition Partition Partition Reduce Reduce Output Writer Result file Data file Map Partition Reduce Data file Map Partition 13/12/16 14

15 Hadoop MapReduce manager architecture 13/12/16 15

16 Hive system architecture and components. 13/12/16 16

17 The Hadoop v1 vs. Hadoop v2 schematic. 13/12/16 17

18 MapReduce code (pseudocode) function map(string name, String document): // name: document name, i.e. HDFS file contents // document: document contents, parsed HDFS file tokens // Can make own parser as preprocessor for each word w in document: emit (w, 1) function reduce(string word, Iterator partialcounts): // word: a word // partialcounts pc: a list of aggregated partial counts (word, cnt) sum = 0; for each pc in partialcounts: sum += ParseInt(pc); emit (word, sum) 13/12/16 18

19 Input reader MapReduce stages System component that reads files from scalable file system (e.g. HDFS) and sends to map functions applied in parallel Map function Applied in parallel on many different files Parses input file data from HDFS Does some (expensive) computation Emits key value pairs as result Result stored by MapReduce system as file Partition function (optional) Partitions output key/value pairs from map function into groups of key/value pairs to be reduced in parallel Usually hash partitioning Reduce function Iterates over set of key/value pairs to produce a reduced set of key value pairs stored in the file system Compare with: aggregate functions 13/12/16 19

20 Wordcount in HiveQL FROM ( MAP docs.doctext USING 'python wc_mapper.py' AS (word, cnt) FROM docs CLUSTER BY word ) REDUCE word, cnt USING 'pythonwc_reduce.py'; 13/12/16 20

21 Wordcount in SQL Alt 1, assume words on documents stored in table: CREATE TABLE docs(id INTEGER, word VARCHAR(20), PRIMARY KEY(id)) The query becomes: SELECT d.word, COUNT(d.word) FROM docs d GROUP BY d.word Problem: DOCS is table, not stored in file Alt 2, use user-defined table function to access documents in file: SELECT d.word, COUNT(d.word) FROM mydocuments( C:/mydocuments ) AS d GROUP BY d.word 13/12/16 21

22 HIVEQL vs raw mapreduce Raw MapReduce: Java (Python, C++, etc.) program does map and reduce Very common use of MapReduce: Statistics collection over files (count, sum, stdev, etc) HIVEQL handles basic statistics 80% of applications When advanced statistics not supported in HIVEQL (or SQL): Alt 1: User defined aggregate functions in HIVE (UDAF) Can be generally used in other queries too Alt 2: Raw MapReduce Code may be complicated Code cannot be used in queries 13/12/16 22

23 Reading raw data files to RDBMS Major point with MapReduce: Save time to load database and build index Accessing CSV (Comma Separated Values) file Can treat CSV file as relational table Modern DBMS have bulk load facilities Never use insert to bulk load At least do not do commit after each insert (MySQL autocommit default) Bulk load speed approaches file copy time Orders of magnitude faster than naïve inserts Automatic parallel bulk loading Binary and formatted bulk loading supported Indexes are voluntary and can be built afterwards 13/12/16 23

24 Relational Databases vs MapReduce Modern RDBMSs have user defined table functions and aggregate functions Can read data from files using UDFs User defined aggregate functions are incremental and parallelized RDBMSs have indexing and high parallelism to provide scalability Notice that it takes time to build index during database loading => May slow down database loading considerable When is HIVE with MapReduce better than RDBs? One shot queries when no indexing is needed Massively parallel very expensive brute force computations Google F1: embarrassingly parallel computations Use highly distributed DBMS to select input to brute force MapReduce jobs 13/12/16 24

25 10 performance rules for simple databases (CACM June 2011) 1. Look for shared nothing Distributed database design 2. High level languages are good and need not hurt performance Query language with query optimization needed 3. Plan to carefully leverage main memory databases Just running on RAM disk only marginally faster than disk DBMS Need DBMS designed for MM (TimesTen, MySQL Cluster, SAP HANA) 4. High availability and automatic recovery essential for scalability Many nodes => Likelihood of node failure increases drastically Should be possible to recover failed nodes without bringing down DB (e.g. MySQL Cluster,) 5. On-line everything Schema changes (data independence), software upgrades without bringing down database. 13/12/16 25

26 10 performance rules for simple databases (CACM June 2011) 6. Avoid multi-node operations Expensive having operations over data from many nodes Distributed database design, logical fragmentation, sharding 7. Don t try to build ACID consistency yourself Don t build transactions yourself Eventual consistency may cause very severe headaches! E.g. updates of multiple-nodes may cause severe problems if not supported by transactions 8. Look for administrative simplicity Often very difficult to install DBMS 9. Pay attention to node performance Parallel brute force waists energy and other resources Index! 10. Open source gives you more control over your future 13/12/16 26