Using Oracle Big Data SQL 3.0 to add Hadoop & NoSQL to your Oracle Data Warehouse
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The document discusses the integration of Hadoop and NoSQL technologies with Oracle's data warehousing solutions, focusing on using Oracle Big Data SQL to enable SQL processing on Hadoop data. It outlines the benefits of combining these technologies for enhanced data storage, analysis, and management, while also examining various tools like Apache Hive and Cloudera Impala for efficient data queries. Additionally, it emphasizes the performance and operational management improvements of utilizing SQL-like interfaces for big data within Oracle frameworks.
![info@rittmanmead.com www.rittmanmead.com @rittmanmead
•Create Hive table over HBase database as normal
•Typically done to add INSERT and DELETE capabilities to Hive, for DW dimension ETL
•Create Oracle external table as normal, using ORACLE_HIVE driver
Use of Hive Storage Handlers Transparent to BDS
CREATE EXTERNAL TABLE tablename colname coltype[, colname coltype,...]
ROW FORMAT
SERDE 'org.apache.hadoop.hive.hbase.HBaseSerDe'
STORED BY 'org.apache.hadoop.hive.hbase.HBaseStorageHandler'
WITH SERDEPROPERTIES (
'serialization.format'='1',
'hbase.columns.mapping'=':key,value:key,value:
CREATE TABLE tablename(colname colType[, colname colType...])
ORGANIZATION EXTERNAL
(TYPE ORACLE_HIVE DEFAULT DIRECTORY DEFAULT_DIR
ACCESS PARAMETERS
(access parameters)
)
REJECT LIMIT UNLIMITED;](https://image.slidesharecdn.com/oghdbaensqlcelebrationday-usingoraclebigdatasqltoaddhadoopnosqltoyouroracledatawarehouse-160607093723/85/Using-Oracle-Big-Data-SQL-3-0-to-add-Hadoop-NoSQL-to-your-Oracle-Data-Warehouse-40-320.jpg)
Using Oracle Big Data SQL 3.0 to add Hadoop & NoSQL to your Oracle Data Warehouse
- 1. [email protected] www.rittmanmead.com @rittmanmead Using Oracle Big Data SQL to add Hadoop + NoSQL to your Oracle Data Warehouse Mark Rittman, CTO, Rittman Mead SQL Celebration Day, Netherlands June 2016
- 2. [email protected] www.rittmanmead.com @rittmanmead 2 •Many customers and organisations are now running initiatives around “big data” •Some are IT-led and are looking for cost-savings around data warehouse storage + ETL •Others are “skunkworks” projects in the marketing department that are now scaling-up •Projects now emerging from pilot exercises •And design patterns starting to emerge Many Organisations are Running Big Data Initiatives
- 3. [email protected] www.rittmanmead.com @rittmanmead •Gives us an ability to store more data, at more detail, for longer •Provides a cost-effective way to analyse vast amounts of data •Hadoop & NoSQL technologies can give us “schema-on-read” capabilities •There’s vast amounts of innovation in this area we can harness •And it’s very complementary to Oracle BI & DW Why is Hadoop of Interest to Us?
- 4. [email protected] www.rittmanmead.com @rittmanmead 4 •Mark Rittman, Co-Founder of Rittman Mead ‣Oracle ACE Director, specialising in Oracle BI&DW ‣14 Years Experience with Oracle Technology ‣Regular columnist for Oracle Magazine •Author of two Oracle Press Oracle BI books ‣Oracle Business Intelligence Developers Guide ‣Oracle Exalytics Revealed ‣Writer for Rittman Mead Blog : http://www.rittmanmead.com/blog •Email : [email protected] •Twitter : @markrittman About the Speaker
- 5. [email protected] www.rittmanmead.com @rittmanmead Flexible Cheap Storage for Logs, Feeds + Social Data $50k Hadoop Node Voice + Chat Transcripts Call Center LogsChat Logs iBeacon Logs Website LogsCRM Data Transactions Social FeedsDemographics Raw Data Customer 360 Apps Predictive Models SQL-on-Hadoop Business analytics Real-time Feeds, batch and API
- 6. [email protected] www.rittmanmead.com @rittmanmead Incorporate Hadoop Data Reservoirs into DW Design
- 7. [email protected] www.rittmanmead.com @rittmanmead Oracle Big Data Product Architecture
- 8. [email protected] www.rittmanmead.com @rittmanmead •Oracle Big Data Appliance - Engineered System for running Hadoop alongside Exadata •Oracle Big Data Connectors - Utility from Oracle for feeding Hadoop data into Oracle •Oracle Data Integrator EE Big Data Option - Add Spark, Pig data transforms to Oracle ODI •Oracle BI Enterprise Edition - can connect to Hive, Impala for federated queries •Oracle Big Data Discovery - data wrangling + visualization tool for Hadoop data reservoirs •Oracle Big Data SQL - extend Oracle SQL language + processing to Hadoop Oracle Software Initiatives around Big Data
- 10. This is Hadoop
- 11. Where everything is non-relational
- 12. isn’t SQL on Hadoop somewhat missing the point?
- 14. [email protected] www.rittmanmead.com @rittmanmead Where Can SQL Processing Be Useful with Hadoop? •Hadoop is not a cheap substitute for enterprise DW platforms - don’t use it like this •But adding SQL processing and abstraction can help in many scenarios: • Query access to data stored in Hadoop as an archive • Aggregating, sorting, filtering and transforming data • Set-based transformation capabilities for other frameworks (e.g. Spark) • Ad-hoc analysis and data discovery in-real time • Providing tabular abstractions over complex datatypes SQL! Though SQL isn’t actually relational According to Chris Date SQL is just mappings Tedd Codd used Predicate Calculus and there’s never been a mainstream relational DBMS but it is the standard language for RDBMSs and it’s great for set-based transforms & queries so Yes SQL!
- 15. [email protected] www.rittmanmead.com @rittmanmead 15 •Original developed at Facebook, now foundational within the Hadoop project •Allows users to query Hadoop data using SQL-like language •Tabular metadata layer that overlays files, can interpret semi-structured data (e.g. JSON) •Generates MapReduce code to return required data •Extensible through SerDes and Storage Handlers •JDBC and ODBC drivers for most platforms/tools •Perfect for set-based access + batch ETL work Apache Hive : SQL Metadata + Engine over Hadoop
- 16. [email protected] www.rittmanmead.com @rittmanmead •Hive uses a RBDMS metastore to hold table and column definitions in schemas •Hive tables then map onto HDFS-stored files ‣Managed tables ‣External tables •Oracle-like query optimizer, compiler, executor •JDBC and OBDC drivers, plus CLI etc 16 How Does Hive Translate SQL into MapReduce? Hive Thrift Server JDBC / ODBC Parser Planner Execution Engine Metastore MapReduc e HDFS HueCLI
- 17. [email protected] www.rittmanmead.com @rittmanmead •Hive uses a RBDMS metastore to hold table and column definitions in schemas •Hive tables then map onto HDFS-stored files ‣Managed tables ‣External tables •Oracle-like query optimizer, compiler, executor •JDBC and OBDC drivers, plus CLI etc 17 How Does Hive Translate SQL into MapReduce? hive> select count(*) from src_customer; Total MapReduce jobs = 1 Launching Job 1 out of 1 Number of reduce tasks determined at compile time: 1 In order to change the average load for a reducer (in bytes): set hive.exec.reducers.bytes.per.reducer= In order to limit the maximum number of reducers: set hive.exec.reducers.max= In order to set a constant number of reducers: set mapred.reduce.tasks= Starting Job = job_201303171815_0003, Tracking URL = http://localhost.localdomain:50030/jobdetails.jsp… Kill Command = /usr/lib/hadoop-0.20/bin/ hadoop job -Dmapred.job.tracker=localhost.localdomain:8021 -kill job_201303171815_0003 2013-04-17 04:06:59,867 Stage-1 map = 0%, reduce = 0% 2013-04-17 04:07:03,926 Stage-1 map = 100%, reduce = 0% 2013-04-17 04:07:14,040 Stage-1 map = 100%, reduce = 33% 2013-04-17 04:07:15,049 Stage-1 map = 100%, reduce = 100% Ended Job = job_201303171815_0003 OK 25 Time taken: 22.21 seconds HiveQL Query MapReduce Job submitted Results returned
- 18. But Hive is too slow for ad-hoc queries
- 19. [email protected] www.rittmanmead.com @rittmanmead 19 •Cloudera’s answer to Hive query response time issues •MPP SQL query engine running on Hadoop, bypasses MapReduce for direct data access •Mostly in-memory, but spills to disk if required •Uses Hive metastore to access Hive table metadata •Similar SQL dialect to Hive - not as rich though and no support for Hive SerDes, storage handlers etc Cloudera Impala - Fast, MPP-style Access to Hadoop Data
- 20. [email protected] www.rittmanmead.com @rittmanmead •Apache Drill is another SQL-on-Hadoop project that focus on schema-free data discovery •Inspired by Google Dremel, innovation is querying raw data with schema optional •Automatically infers and detects schema from semi-structured datasets and NoSQL DBs •Join across different silos of data e.g. JSON records, Hive tables and HBase database •Aimed at different use-cases than Hive - low-latency queries, discovery (think Endeca vs OBIEE) Apache Drill - SQL for Schema-Free Data Discovery
- 21. [email protected] www.rittmanmead.com @rittmanmead 21 •A replacement for Hive, but uses Hive concepts and data dictionary (metastore) •MPP (Massively Parallel Processing) query engine that runs within Hadoop ‣Uses same file formats, security, resource management as Hadoop •Processes queries in-memory •Accesses standard HDFS file data •Option to use Apache AVRO, RCFile, LZO or Parquet (column-store) •Designed for interactive, real-time SQL-like access to Hadoop How Impala Works Impala Hadoop HDFS etc BI Server Presentation Svr Cloudera Impala ODBC Driver Impala Hadoop HDFS etc Impala Hadoop HDFS etc Impala Hadoop HDFS etc Impala Hadoop HDFS etc Multi-Node Hadoop Cluster
- 22. but sometimes,
- 23. but sometimes, you need the real thing
- 24. [email protected] www.rittmanmead.com @rittmanmead •Originally part of Oracle Big Data 4.0 (BDA-only) ‣Also required Oracle Database 12c, Oracle Exadata Database Machine •Extends Oracle Data Dictionary to cover Hive •Extends Oracle SQL and SmartScan to Hadoop •Extends Oracle Security Model over Hadoop ‣Fine-grained access control ‣Data redaction, data masking ‣Uses fast c-based readers where possible (vs. Hive MapReduce generation) ‣Map Hadoop parallelism to Oracle PQ ‣Big Data SQL engine works on top of YARN ‣Like Spark, Tez, MR2 Oracle Big Data SQL Exadata Storage Servers Hadoop Cluster Exadata Database Server Oracle Big Data SQL SQL Queries SmartScan SmartScan
- 25. [email protected] www.rittmanmead.com @rittmanmead •As with other next-gen SQL access layers, uses common Hive metastore table metadata •leverages Hadoop standard APIs for HDFS file access, metadata integration etc Leverages Hive Metastore and Hadoop file access APIs
- 26. [email protected] www.rittmanmead.com @rittmanmead •Brings query-offloading features of Exadata to Oracle Big Data Appliance •Query across both Oracle and Hadoop sources •Intelligent query optimisation applies SmartScan close to ALL data •Use same SQL dialect across both sources •Apply same security rules, policies, user access rights across both sources Extending SmartScan, and Oracle SQL, Across All Data
- 27. [email protected] www.rittmanmead.com @rittmanmead •Read data from HDFS Data Node ‣Direct-path reads ‣C-based readers when possible ‣Use native Hadoop classes otherwise •Translate bytes to Oracle •Apply SmartScan to Oracle bytes ‣Apply filters ‣Project columns ‣Parse JSON/XML ‣Score models How Big Data SQL Accesses Hadoop (HDFS) Data Disks% Data$Node$ Big$Data$SQL$Server$ External$Table$Services$ Smart$Scan$ RecordReader% SerDe% 10110010%10110010%10110010% 1% 2% 3% 1 2 3
- 28. [email protected] www.rittmanmead.com @rittmanmead •“Query Franchising – dispatch of query processing to self-similar compute agents on disparate systems without loss of operational fidelity” •Contrast with OBIEE which provides a query federation capability over Hadoop •Sends sub-queries to each data source •Relies on each data source’s native query engine, and resource management •Query franchising using Big Data SQL ensures consistent resource management •And contrast with SQL translation tools (i.e. Oracle SQL to Impala) •Either limits Oracle SQL to the subset that Hive, Impala supports •Or translation engine has to transform each Oracle feature into Hive, Impala SQL Query Franchising vs. SQL Translation / Federation
- 29. [email protected] www.rittmanmead.com @rittmanmead •Oracle Database 12c 12.1.0.2.0 with Big Data SQL option can view Hive table metadata ‣Linked by Exadata configuration steps to one or more BDA clusters •DBA_HIVE_TABLES and USER_HIVE_TABLES exposes Hive metadata •Oracle SQL*Developer 4.0.3, with Cloudera Hive drivers, can connect to Hive metastore View Hive Table Metadata in the Oracle Data Dictionary SQL> col database_name for a30 SQL> col table_name for a30 SQL> select database_name, table_name 2 from dba_hive_tables; DATABASE_NAME TABLE_NAME ------------------------------ ------------------------------ default access_per_post default access_per_post_categories default access_per_post_full default apachelog default categories default countries default cust default hive_raw_apache_access_log
- 30. [email protected] www.rittmanmead.com @rittmanmead •Big Data SQL accesses Hive tables through external table mechanism ‣ORACLE_HIVE external table type imports Hive metastore metadata ‣ORACLE_HDFS requires metadata to be specified •Access parameters cluster and tablename specify Hive table source and BDA cluster Hive Access through Oracle External Tables + Hive Driver CREATE TABLE access_per_post_categories( hostname varchar2(100), request_date varchar2(100), post_id varchar2(10), title varchar2(200), author varchar2(100), category varchar2(100), ip_integer number) organization external (type oracle_hive default directory default_dir access parameters(com.oracle.bigdata.tablename=default.access_per_post_categories));
- 31. [email protected] www.rittmanmead.com @rittmanmead •Run normal Oracle SQL from the Oracle Database server •Big Data SQL query franchising then uses agents on Hadoop nodes to query and return data independent of YARN scheduling; Oracle Database combines and returns full results Running Oracle SQL on Hadoop Data Nodes SELECT w.sess_id,w.cust_id,c.name FROM web_logs w, customers c WHERE w.source_country = ‘Brazil’ AND c.customer_id = w.cust_id
- 32. [email protected] www.rittmanmead.com @rittmanmead •OBIEE can access Hadoop data via Hive, but it’s slow •(Impala only has subset of Oracle SQL capabilities) •Big Data SQL presents all data to OBIEE as Oracle data, with full advanced analytic capabilities across both platforms Example : Combining Hadoop + Oracle Data for BI Hive Weblog Activity table Oracle Dimension lookup tables Combined output in report form
- 33. [email protected] www.rittmanmead.com @rittmanmead •Not all functions can be offloaded to Hadoop tier •Even for non-offloadable operations Big Data SQL will perform column pruning and datatype conversion (which saves a lot of resources) •Other operations (non-offloadable) will be done on the database side •Requires Oracle Database 12.1.0.2 + patchset, and per-disk licensing for Big Data SQL •You need and Oracle Big Data Appliance, and Oracle Exadata, to use Big Data SQL Restrictions when using Oracle Big Data SQL SELECT NAME FROM v$sqlfn_metadata WHERE offloadable ='YES'
- 34. [email protected] www.rittmanmead.com @rittmanmead •From Big Data SQL 3.0, commodity hardware can be used instead of BDA and Exadata •Oracle Database 12.1.0.2 on x86_64 with Jan/Apr Proactive Bundle Patches •Cloudera CDH 5.5 or Hortonworks HDP 2.3 on RHEL/OEL6 •See MOS Doc ID 2119369.1 - note cannot mix Engineered/Non-Engineered platforms Running Big Data SQL on Commodity Hardware
- 35. [email protected] www.rittmanmead.com @rittmanmead •No functional differences when running Big Data SQL on commodity hardware •External table capability lives with the database, and the performance functionality with the BDS cell software. •All BDS features (SmartScan, offloading, storage indexes etc still available) •But hardware can be a factor now, as we’re pushing processing down and data up the wire •1GB ethernet can be too slow, 10Gb is a minimum (i.e. no InfiniBand) •If you run on an undersized system you may see bottlenecks on the DB side. Big Data SQL on Commodity Hardware Considerations
- 36. But Hadoop is more than simple HDFS files
- 38. [email protected] www.rittmanmead.com @rittmanmead •Subsequent releases of Big Data SQL have extended its Hadoop capabilties •Support for Hive storage handlers (HBase, MongoDB etc) •Hive partition elimination •Better, more efficient access to Hadoop data •Storage Indexes •Predicate Push-Down for Parquet, ORC, HBase, Oracle NoSQL •Bloom Filters •Coming with Oracle Database 12.2 •Big Data-aware optimizer •Dense Bloom Filters •Oracle managed Big Data partitions Going beyond Fast Unified Query Access to HDFS Data
- 39. [email protected] www.rittmanmead.com @rittmanmead •Hive Storage handlers give Hive the ability to access data from non-HDFS sources •MongoDB •HBase •Oracle NoSQL database •Run HiveQL queries against NoSQL DBs •From BDS1.1, Hive storage handlers can be used with Big Data SQL •Only MongoDB, HBase and NoSQL currently “supported” •Others should work but not tested Big Data SQL and Hive Storage Handlers
- 40. [email protected] www.rittmanmead.com @rittmanmead •Create Hive table over HBase database as normal •Typically done to add INSERT and DELETE capabilities to Hive, for DW dimension ETL •Create Oracle external table as normal, using ORACLE_HIVE driver Use of Hive Storage Handlers Transparent to BDS CREATE EXTERNAL TABLE tablename colname coltype[, colname coltype,...] ROW FORMAT SERDE 'org.apache.hadoop.hive.hbase.HBaseSerDe' STORED BY 'org.apache.hadoop.hive.hbase.HBaseStorageHandler' WITH SERDEPROPERTIES ( 'serialization.format'='1', 'hbase.columns.mapping'=':key,value:key,value: CREATE TABLE tablename(colname colType[, colname colType...]) ORGANIZATION EXTERNAL (TYPE ORACLE_HIVE DEFAULT DIRECTORY DEFAULT_DIR ACCESS PARAMETERS (access parameters) ) REJECT LIMIT UNLIMITED;
- 41. [email protected] www.rittmanmead.com @rittmanmead •From Big Data SQL 2.0, Storage Indexes are automatically created in Big Data SQL agents •Check index before reading blocks – Skip unnecessary I/Os •An average of 65% faster than BDS 1.x •Up to 100x faster for highly selective queries •Columns in SQL are mapped to fields in the HDFS file via External Table Definitions •Min / max value is recorded for each HDFS Block in a storage index Big Data SQL Storage Indexes
- 42. [email protected] www.rittmanmead.com @rittmanmead •Hadoop supports predicate push-down through several mechanisms (filetypes, Hive partition pruning etc) •Original BDS 1.0 supported Hive predicate push-down as part of SmartScan •BDS 3.0 extends this by pushing SARGable (Search ARGument ABLE) predicates •Into Parquet and ORCFile to reduce I/O when reading files from disk •Into HBAse and Oracle NoSQL database to drive subscans of data from remote DB •Oracle Database 12.2 will add more optimisations •Columnar-caching •Big Data-Aware Query Optimizer, •Managed Hadoop partitions •Dense Bloom Filters Extending Predicate Push-Down Beyond Hive
- 43. [email protected] www.rittmanmead.com @rittmanmead •Typically a one-way street - queries run in Hadoop but results delivered through Oracle •What if you want to load data into Hadoop, update data, do Hadoop>Hadoop transforms? •Still requires formal Hive metadata, whereas direction is towards Drill & schema-free queries •What if you have other RDBMSs as well as Oracle RDBMS? •Trend is towards moving all high-end analytic workloads into Hadoop - BDS is Oracle-only •Requires Oracle 12c database, no 11g support •And cost … BDS is $3k/Hadoop disk drive •Can cost more than an Oracle BDA •High-end, high-cost Oracle-centric solution •of course! … So What’s the Catch?
- 44. [email protected] www.rittmanmead.com @rittmanmead •Rich, comprehensive SQL access to all enterprise data •Extend Oracle security, advanced analytic features and metadata across Hadoop & NoSQL Oracle Big Data SQL Vision : Unified Query
- 46. [email protected] www.rittmanmead.com @rittmanmead Using Oracle Big Data SQL to add Hadoop + NoSQL to your Oracle Data Warehouse Mark Rittman, CTO, Rittman Mead SQL Celebration Day, Netherlands June 2016