Gluent New World #02 : SQL-on-Hadoop with Mark Rittman

Hadoop and NoSQL platforms initially focused on Java developers and slow but massively-scalable MapReduce jobs as an alternative to high-end but limited-scale analytics RDBMS engines. Apache Hive opened-up Hadoop to non-programmers by adding a SQL query engine and relational-style metadata layered over raw HDFS storage, and since then open-source initiatives such as Hive Stinger, Cloudera Impala and Apache Drill along with proprietary solutions from closed-source vendors have extended SQL-on-Hadoop’s capabilities into areas such as low-latency ad-hoc queries, ACID-compliant transactions and schema-less data discovery – at massive scale and with compelling economics.

In this session we’ll focus on technical foundations around SQL-on-Hadoop, first reviewing the basic platform Apache Hive provides and then looking in more detail at how ad-hoc querying, ACID-compliant transactions and data discovery engines work along with more specialised underlying storage that each now work best with – and we’ll take a look to the future to see how SQL querying, data integration and analytics are likely to come together in the next five years to make Hadoop the default platform running mixed old-world/new-world analytics workloads.

Transcript

1. info@rittmanmead.com www.rittmanmead.com @rittmanmead Gluent New World #02: SQL-on-Hadoop with Mark Rittman Mark Rittman, CTO, Rittman Mead April 2016
2. info@rittmanmead.com www.rittmanmead.com @rittmanmead 2 •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 : mark.rittman@rittmanmead.com •Twitter : @markrittman About the Speaker
3. info@rittmanmead.com www.rittmanmead.com @rittmanmead 3 •Why Hadoop? And what are the key Hadoop platform features? •Introducing SQL-on-Hadoop, and Apache Hive •How Hive works, and how it’s not just about SELECTing data •Solving Hive’s ad-hoc query performance problem •So what’s all this about Apache Drill? •…. and Oracle Big Data SQL, IBM Big SQL? •Apache Spark, and Spark SQL •Security, Hadoop and SQL-on-Hadoop •Selecting a SQL-on-Hadoop query engine Agenda
4. info@rittmanmead.com www.rittmanmead.com @rittmanmead •Everyone’s talking about Hadoop and “Big Data” Hadoop is the Big Hot Topic In IT / Analytics
5. info@rittmanmead.com www.rittmanmead.com @rittmanmead Highly Scalable (and Affordable) Cluster Computing •Enterprise High-End RDBMSs such as Oracle can scale into the petabytes, using clustering ‣Sharded databases (e.g. Netezza) can scale further but with complexity / single workload trade-offs •Hadoop was designed from outside for massive horizontal scalability - using cheap hardware •Anticipates hardware failure and makes multiple copies of data as protection •More nodes you add, more stable it becomes •And at a fraction of the cost of traditional RDBMS platforms
6. info@rittmanmead.com www.rittmanmead.com @rittmanmead •Store and analyze huge volumes of structured and unstructured data •In the past, we had to throw away the detail •No need to define a data model during ingest •Supports multiple, flexible schemas •Separation of storage from compute engine •Allows multiple query engines and frameworks to work on the same raw datasets 6 Store Everything Forever - And Process in Many Ways Hadoop Data Lake Webserver Log Files (txt) Social Media Logs (JSON) DB Archives (CSV) Sensor Data (XML) `Spatial & Graph (XML, txt) IoT Logs (JSON, txt) Chat Transcripts (Txt) DB Transactions (CSV, XML) Blogs, Articles (TXT, HTML) Raw Data Processed Data NoSQL Key-Value Store DB Tabular Data (Hive Tables) Aggregates (Impala Tables) NoSQL Document Store DB
7. info@rittmanmead.com www.rittmanmead.com @rittmanmead 7 •Data for customer 360 system typically landed into a Hadoop & NoSQL-based •Applies aggregation, joining and machine-learning processes to extract insights Design Pattern : “Data Lake” or “Data Reservoir”
8. info@rittmanmead.com www.rittmanmead.com @rittmanmead 8 •Combine with a traditional data warehouse to add storage, support for new datatypes •Land raw data in real-time into Hadoop, then process and store Combine with Traditional Data Warehouse
9. info@rittmanmead.com www.rittmanmead.com @rittmanmead 9 •Hadoop is the overall framework for enabling low-cost, scalable cluster computing ‣HDFS cluster filesystem stores the data, in a process/query neutral form (files) ‣YARN resource manager allocates resources to Hadoop jobs ‣MapReduce and other processing frameworks then work on that data •Data is decoupled from the engine that processes it •Layers can be swapped out (Mesos for YARN etc) •Hadoop takes care of the overall cluster framework Key Hadoop Platform Technologies Hadoop Distributed Filesystem (HDFS) YARN Resource Manager Query and Processing Engines Batch (MapReduce) In-Memory (Spark) Streaming (Spark, Storm) Graph + Search (Solr, Giraph) Unstructured / Semi-Structured Log Data Offloaded Archive Data Social Graphs & Networks Smart Meter & Sensor Data
10. sounds good
11. but I’m a DBA
12. it’s all files
14. I don’t know MapReduce
15. or Scala
16. or
17. or whatever the latest made-up Hadoop language is
18. or whatever the latest made-up Hadoop language is or whatever the latest made-up Hadoop language is
19. info@rittmanmead.com www.rittmanmead.com @rittmanmead Introducing SQL-on-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 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 19 Hadoop Distributed Filesystem (HDFS) YARN Resource Manager Query and Processing Engines Batch (MapReduce) In-Memory (Spark) Streaming (Spark, Storm) Graph + Search (Solr, Giraph) Unstructured / Semi-Structured Log Data Offloaded Archive Data Social Graphs & Networks Smart Meter & Sensor Data SQL Engine SQL Engine
20. info@rittmanmead.com www.rittmanmead.com @rittmanmead 20 •Modern SQL-on-Hadoop engines often provide connectivity to data sources outside of the Hadoop cluster ‣Traditional DW platforms ‣No-SQL databases e.g. MongoDB ‣Files, JDBC etc •Provide a framework for data integration and data federation, using JDBC drivers Enables Integration with External (And Internal) Data Hadoop Distributed Filesystem (HDFS) YARN Resource Manager Query and Processing Engines In-Memory (Spark) Unstructured / Semi-Structured Log Data Offloaded Archive Data Social Graphs & Networks Smart Meter & Sensor Data SQL Engine 20 NoSQL Key-Value Store DB
21. info@rittmanmead.com www.rittmanmead.com @rittmanmead 21 •Most Traditional data warehousing vendors offer a Hadoop integration option •Oracle Big Data SQL •IBM Big SQL etc •Leverage lower-level SQL-on-Hadoop metadata but use own server process •Allows DBAs to write SQL using RDBMS SQL dialect, run across relational, Hadoop and NoSQL servers Hadoop Distributed Filesystem (HDFS) YARN Resource Manager Query and Processing Engines Oracle Big Data SQL Server Unstructured / Semi-Structured Log Data Offloaded Archive Data Social Graphs & Networks Smart Meter & Sensor Data 21 NoSQL Key-Value Store DB Platform for Traditional DW Integration with Hadoop Oracle RDBMS
22. So how do they work?
23. info@rittmanmead.com www.rittmanmead.com @rittmanmead •Original SQL-on-Hadoop engine developed at Facebook, now 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 23 Apache Hive : SQL Metadata + Engine over Hadoop YARN Resource Manager Hadoop Distributed Filesystem (HDFS) Unstructured / Semi-Structured Log Data Offloaded Archive Data Social Graphs & Networks Smart Meter & Sensor Data 23 23 MapReduce Processing Framework Apache Hive SQL Processing Engine HiveQL SQL Commands Java JARs Submitted Jobs
24. info@rittmanmead.com www.rittmanmead.com @rittmanmead •Queries come in via JDBC/ODBC, the Hive Thrift Server, from the CLI or via Hue (for example) •The Hive Metastore (data dictionary) maps files and other Hadoop data structures onto tables and columns •The Hive SQL engine parses, plans and then executes the query, using an execution plan similar to Oracle, SQL Server and other RBDMS engines •MapReduce code is then auto-generated, and submitted to YARN, and then run on the Hadoop cluster 24 Apache Hive Logical Architecture Hive Thrift Server JDBC / ODBC Parser Planner Execution Engine Metastore Hue CLI MapReduce HDFS 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
25. info@rittmanmead.com www.rittmanmead.com @rittmanmead •Data integration tools such as Oracle Data Integrator can load and process Hadoop data •BI tools such as Oracle Business Intelligence 12c can report on Hadoop data •Generally use MapReduce and Hive to access data ‣ODBC and JDBC access to Hive tabular data ‣Allows Hadoop unstructured/semi-structured data on HDFS to be accessed like RDBMS Provides a SQL Interface for BI + ETL Tools Access direct Hive or extract using ODI12c for structured OBIEE dashboard analysis What pages are people visiting? Who is referring to us on Twitter? What content has the most reach?
26. T : +44 (0) 1273 911 268 (UK) or (888) 631-1410 (USA) or +61 3 9596 7186 (Australia & New Zealand) or +91 997 256 7970 (India) E : info@rittmanmead.com W : www.rittmanmead.com Connecting to Hive using Beeline CLI •From the command-line, either use Hive CLI, or beeline CLI ‣HUE (“Hadoop User Experience”) provides Web interface into Hive (think Oracle Apex) [iot@cdh-node1 ~]$ beeline -u jdbc:hive2://cdh-node1:10000 -n iot -p welcome1 -d org.apache.hive.jdbc.HiveDriver Connecting to jdbc:hive2://cdh-node1:10000 Connected to: Apache Hive (version 1.1.0-cdh5.5.1) Driver: Hive JDBC (version 1.1.0-cdh5.5.1) Transaction isolation: TRANSACTION_REPEATABLE_READ Beeline version 1.1.0-cdh5.5.1 by Apache Hive 0: jdbc:hive2://cdh-node1:10000> show tables; +-----------------------------------+--+ | tab_name | +-----------------------------------+--+ | flight_delays | | my_second_table | | oracle_analytics_tweets | +-----------------------------------+--+ 8 rows selected (0.137 seconds) 0: jdbc:hive2://cdh-node1:10000>
27. info@rittmanmead.com www.rittmanmead.com @rittmanmead •Hive is extensible in three major ways that help with accessing and integrating new data sets •SerDes : Serializer-Deserializers that interpret semi-structured sources + make tabular •UDFs + Hive Streaming : Add user-defined functions and whole-row external processing •File Formats : make use of compressed and/or optimised file storage •Storage Handlers : use storage other than HDFS (e.g. MongoDB) as data source Hive Extensibility - The “Swiss Army Knife” of Hadoop Client Client HDFS Fileformats JDBC / ODBC Metastore MapReduce UDF/UDAFs SerDe Scripts HBase MongoDB Parser Execution Engine HiveQL Planner Storage Hdlrs TextFile Parquet
28. info@rittmanmead.com www.rittmanmead.com @rittmanmead •Extend Hive by adding new computation and aggregation capabilities •UDFs (row-based), UDAFs (aggregation) and UDTFs (table functions) Hive Extensibility through UDFs and UDAFs add jar target/JsonSplit-1.0-SNAPSHOT.jar; create temporary function json_split as 'com.pythian.hive.udf.JsonSplitUDF'; create table json_example (json string); load data local inpath 'split_example.json' into table json_example; SELECT ex.* FROM json_example LATERAL VIEW explode(json_split(json_example.json)) ex; ADD JAR ./ext.jar; CREATE TEMPORARY FUNCTION process_names as 'com.matthewrathbone.example.NameParserGenericUDTF'; SELECT adTable.name, adTable.surname FROM people lateral view process_names(name) adTable as name, surname;
29. info@rittmanmead.com www.rittmanmead.com @rittmanmead •Allows data to be stored in optimised storage format ‣Column-store for analytics ‣Self-describing, splittable storage for general-purpose use ‣Compressed data ‣Semi-structured (e.g. log) data 29 SerDes & Storage Handlers Further Decouple Storage Hadoop Distributed Filesystem (HDFS) Query and Processing Engines MapReduce Unstructured / Semi-Structured Log Data Offloaded Archive Data Social Graphs & Networks Smart Meter & Sensor Data SQL Engine NoSQL Key-Value Store DB RegEx Serde Parquet SerDe JSON SerDe NoSQL Key-Value Store DB MongoDB Store Handler MongoDB Store Handler
30. info@rittmanmead.com www.rittmanmead.com @rittmanmead 30 •Splittability - can the file be split into blocks and processed in parallel ‣CSV files can be split by file line; XML files can’t because of opening and closing tags •Ability to compress - CSV files can’t be block compressed, impact on space / performance •Support for schema evolution - does the file contain in-built schema information that self-describes the data? File Formats in Hadoop Are Important 2016-01-28T09:30:28Z,2016-01-28T11:56:24Z,145.933 2016-01-29T00:19:35Z,2016-01-29T01:36:49Z,77.233 2016-01-29T02:10:35Z,2016-01-29T02:32:18Z,21.717 2016-01-29T03:08:07Z,2016-01-29T03:16:11Z,8.067 2016-01-29T03:51:24Z,2016-01-29T06:57:44Z,186.333 2016-01-29T07:05:50Z,2016-01-29T07:13:21Z,7.517 2016-01-29T07:25:53Z,2016-01-29T07:30:23Z,4.5 2016-01-29T23:30:00Z,2016-01-30T07:00:30Z,450.5 2016-01-31T23:30:00Z,2016-02-01T07:30:00Z,480 2016-02-02T00:35:54Z,2016-02-02T02:10:54Z,95 CSV Extract from Apple Health • Human readable, splittable • No ability to block compress • No in-built self-describing metadata • Timestamps will need special processing • Store final data in parquet format to address some of these concerns {"entities": {"user_mentions": [], "media": [], "hashtags": [], "urls": []}, "text": "Off to visit our office in Bangalore in 15 mins. It'll be good to meet up with Venkat again, plus his team of Ram and Jay.", "created_at": "2010-09-01 00:00:00 +0000", "source": "<a href=\"http://twitter.com\" rel=\"nofollow\">Twitter Web Client</a>", "id_str": "22684302309", "geo": {}, "id": 22684302309, "user": {"verified": false, "name": "Mark Rittman", "profile_image_url_https": "https://pbs.twimg.com/profile_images/7025371008900 87425/rAlqgrGX_normal.jpg", "protected": false, "id_str": "14716125", "id": 14716125, "screen_name": "markrittman"}} JSON Records from Twitter • Human readable, splittable • No ability to block compress (+verbose) • Built self-describing metadata • Less mature SerDe support
31. info@rittmanmead.com www.rittmanmead.com @rittmanmead 31 •Beginners usually store data in HDFS using text file formats (CSV) but these have limitations •Apache AVRO often used for general-purpose processing ‣Splitability, schema evolution, in-built metadata, support for block compression •Parquet now commonly used with Impala due to column-orientated storage ‣Mirrors work in RDBMS world around column-store ‣Only return (project) the columns you require across a wide table Specialised File Formats - Parquet and AVRO
32. info@rittmanmead.com www.rittmanmead.com @rittmanmead 32 Example HiveQL Commands to Create + Populate Table create table health_sleep_analysis_tmp ( asleep_start_ts timestamp, asleep_end_ts timestamp, mins_asleep float) ROW FORMAT SERDE 'org.apache.hadoop.hive.serde2.OpenCSVSerde' WITH SERDEPROPERTIES ( "separatorChar" = “,", "quoteChar" = "'", "escapeChar" = "\\" ) STORED AS TEXTFILE; create table health_sleep_analysis stored as parquet as select from_unixtime(unix_timestamp(asleep_start, "yyyy-MM-dd'T'hh:mm:ss'Z'")) asleep_start_ts, from_unixtime(unix_timestamp(asleep_end, "yyyy-MM-dd'T'hh:mm:ss'Z'")) end_start_ts, mins_asleep from health_sleep_analysis_tmp; • Define temporary Hive table to store start and end times/dates as strings, as we can’t do the string>timestamp conversion using the LOAD DATA command • Use the OpenCSVSerde file format so that we can specify delimiters, quote chars and escape chars for file data • Store as regular uncompressed human-readable text file LOAD DATA INPATH '/user/iot/Health/apple_health_sleep_analysis_noheader.csv' OVERWRITE INTO TABLE health_sleep_analysis_tmp; • Load the data file into that temporary Hive table • Now re-load that temporary data into more optimised Parquet format files, suitable for ad-hoc analytic querying • Convert the timestamps currently held in generic string datatype fields into more optimal TIMESTAMP datatypes using a Hive UDF
33. info@rittmanmead.com www.rittmanmead.com @rittmanmead •One of several third-party SerDes available to download from Github Use of Third-Party (Community) Serde - JSONSerde CREATE EXTERNAL TABLE tweets( id string, created_at string, source string, favorited boolean, retweeted_status struct<text:string, user:struct<screen_name:string,name:string>, retweet_count:int>, entities struct<urls:array <struct<expanded_url:string>>, user_mentions:array<struct<screen_name:string,name:string>>, hashtags:array<struct<text:string>>>, text string, user struct<screen_name:string,name:string,friends_count:int,followers_count:int, statuses_count:int,verified:boolean,utc_of in_reply_to_screen_name string ) ROW FORMAT SERDE 'com.cloudera.hive.serde.JSONSerDe' STORED AS TEXTFILE LOCATION '/user/iot/tweets/'; • Note the use of STRUCT and ARRAY datatypes • Used to handle arrays of hashtags, URLs etc in tweets Just select the JSON elements that we want from the overall schema in JSON records Created as an external Hive table, so overlays schema on existing directory of files
34. info@rittmanmead.com www.rittmanmead.com @rittmanmead 34 •Hive SELECT statement against nested columns returns data as arrays •Can parse programatically, or create further views or CTAS tables to split out array Support for Nested (Array)-Type Structures hive> select entities, user from tweets > limit 3; OK {"urls":[{"expanded_url":"http://www.rittmanmead.com/biforum2013"}],"user_mentio ns":[],"hashtags":[]} {"screen_name":"markrittman","name":"Mark Rittman","friends_count":null,"followers_count":null,"statuses_count":null,"veri fied":false,"utc_offset":null,"time_zone":null} {"urls":[{"expanded_url":"http://www.bbc.co.uk/news/technology- 22299503"}],"user_mentions":[],"hashtags":[]} {"screen_name":"markrittman","name":"Mark Rittman","friends_count":null,"followers_count":null,"statuses_count":null,"veri fied":false,"utc_offset":null,"time_zone":null} {"urls":[{"expanded_url":"http://pocket.co/seb2e"}],"user_mentions":[{"screen_na me":"ArtOfBI","name":"Christian Screen"},{"screen_name":"wiseanalytics","name":"Lyndsay Wise"}],"hashtags":[]} {"screen_name":"markrittman","name":"Mark Rittman","friends_count":null,"followers_count":null,"statuses_count":null,"veri fied":false,"utc_offset":null,"time_zone":null} How to you work with these values? CREATE TABLE tweets_expanded stored as parquet AS select tweets.id, tweets.created_at, tweets.user.screen_name as user_screen_name, tweets.user.friends_count as user_friends_count, tweets.user.followers_count as user_followers_count, tweets.user.statuses_count as user_tweets_count, tweets.text, tweets.in_reply_to_screen_name, tweets.retweeted_status.user.screen_name as retweet_user_screen_name, tweets.retweeted_status.retweet_count as retweet_count, tweets.entities.urls[0].expanded_url as url1, tweets.entities.urls[1].expanded_url as url2, tweets.entities.hashtags[0].text as hashtag1, tweets.entities.hashtags[1].text as hashtag2, tweets.entities.hashtags[2].text as hashtag3, tweets.entities.hashtags[3].text as hashtag4 from tweets; Create a copy of the table in Parquet storage format “Denormalize” the array by selecting individual elements CREATE view tweets_expanded_view AS select tweets.id, tweets.created_at, tweets.user.screen_name as user_screen_name, tweets.user.friends_count as user_friends_count, tweets.user.followers_count as user_followers_count, tweets.user.statuses_count as user_tweets_count, tweets.text, tweets.in_reply_to_screen_name, tweets.retweeted_status.user.screen_name as retweet_user_screen_name, tweets.retweeted_status.retweet_count as retweet_count, tweets.entities.urls[0].expanded_url as url1, tweets.entities.urls[1].expanded_url as url2, tweets.entities.hashtags[0].text as hashtag1, tweets.entities.hashtags[1].text as hashtag2, tweets.entities.hashtags[2].text as hashtag3, tweets.entities.hashtags[3].text as hashtag4 from tweets; … or create as a view (not all BI tools support views though)
35. info@rittmanmead.com www.rittmanmead.com @rittmanmead •Use HiveQL to create aggregations, select individual columns (JSON elements) from data •Use WHERE clause to limit data returned & ORDER BY to sort - as per normal SQL 35 Calculating Aggregations, Filtering Tweet Data select text, hashtag1, hashtag2 from tweets_expanded where hashtag1 = ‘obiee’; Column selection only = just MAP task select in_reply_to_screen_name, count(*) as total_replies_to from tweets_expanded group by in_reply_to_screen_name order by total_replies_to desc limit 10; Selection and aggregation = MAP() and REDUCE task
36. info@rittmanmead.com www.rittmanmead.com @rittmanmead •Hive MR jobs can have multiple stages •MapReduce Stages, Metastore operations •File Move / Rename etc Multi-Stage MapReduce Jobs SELECT LOWER(hashtags.text), COUNT(*) AS total_count FROM ( SELECT * FROM tweets WHERE regexp_extract(created_at,"(2015)*",1) = "2015" ) tweets LATERAL VIEW EXPLODE(entities.hashtags) t1 AS hashtags GROUP BY LOWER(hashtags.text) ORDER BY total_count DESC LIMIT 15 1 2
37. info@rittmanmead.com www.rittmanmead.com @rittmanmead Multi-Step HiveQL Transforms - Tweet Sentiment create external table load_tweets(id string,text STRING) ROW FORMAT SERDE 'com.cloudera.hive.serde.JSONSerDe' LOCATION '/user/iot/tweets'; create table split_words as select id as id,split(text,' ') as words from load_tweets; create table tweet_word as select id as id,word from split_words LATERAL VIEW explode(words) w as word; create table dictionary (word string,rating int) ROW FORMAT DELIMITED FIELDS TERMINATED BY ‘\t'; create table word_join as select tweet_word.id,tweet_word.word,dictionary.rating from tweet_word LEFT OUTER JOIN dictionary ON(tweet_word.word =dictionary.word); select t.text, r.rating from tweets_expanded t join (select id,AVG(rating) as rating from word_join GROUP BY word_join.id) r on t.id = r.id order by r.rating; LOAD DATA INPATH 'afinn.txt' into TABLE dictionary; 1 2 3 4 5 6 7 Take all the text within a set of tweets, and explode-out all the words into a table, one row per word Load in a dictionary file that we’ll use to determine the sentiment of words in these tweets Join the words and the dictionary sentiment scores together, so every word used with any of the tweets has a sentiment score we can use Now average-out the sentiment scores for each word within a tweet, and return the tweet text and those averages listed in descending sentiment order
38. info@rittmanmead.com www.rittmanmead.com @rittmanmead 38 •Not all join types are available in Hive - joins must be equality joins •No sequences, no primary keys on tables •Generally need to stage Oracle or other external data into Hive before joining to it •Hive latency - not good for small microbatch-type work ‣But other alternatives exist - Spark, Impala etc •Don’t assume that HiveQL == Oracle SQL ‣Test assumptions before committing to platform •Hive is INSERT / APPEND only - no updates, deletes etc ‣But HBase may be suitable for CRUD-type loading SQL Considerations : Using Hive vs. Regular Oracle SQL vs.
39. info@rittmanmead.com www.rittmanmead.com @rittmanmead 39 •Based on BigTable paper from Google, 2006, Dean et al. ‣“Bigtable is a sparse, distributed, persistent multi-dimensional sorted map.”Key Features: ‣Distributed storage across cluster of machines – Random, online read and write data access ‣Schemaless data model (“NoSQL”) ‣Self-managed data partitions •Why would you use it with Hive? ‣Allows you to do update and delete activity rather than just Hive append-only ‣Very fast for incremental loading ‣Can define Hive tables over HBase ones, allowing OBIEE to then access them What is HBase?
40. info@rittmanmead.com www.rittmanmead.com @rittmanmead 40 •HBase Shell CLI allows you to create HBase tables •GET and PUT commands can then be used to add/update cells, query cells etc Creating HBase Tables using HBase Shell hbase shell create 'carriers','details' create 'geog_origin','origin' create 'geog_dest','dest' create 'flight_delays','dims','measures' put 'geog_dest','LAX','dest:airport_name','Los Angeles, CA: Los Angeles' put 'geog_dest','LAX','dest:city','Los Angeles, CA' put 'geog_dest','LAX','dest:state','California' put 'geog_dest','LAX','dest:id','12892' hbase(main):015:0> scan 'geog_dest' ROW COLUMN+CELL LAX column=dest:airport_name, timestamp=1432067861347, value=Los Angeles, CA: Los Angeles LAX column=dest:city, timestamp=1432067861375, value=Los Angeles,CA LAX column=dest:id, timestamp=1432067862018,value=12892 LAX column=dest:state, timestamp=1432067861404,value=California 1 row(s) in 0.0240 seconds
41. info@rittmanmead.com www.rittmanmead.com @rittmanmead 41 •Direct extract from salesforce.com into HBase using Python and add-in packages ‣Python packages extend functionality by adding APIs, integration etc ‣Happybase, Beatbox and Pyhs2 packages installed along with Python •All free and open-source Programmatically Loading HBase Tables using Python import pyhs2 import happybase connection = happybase.Connection('bigdatalite') flight_delays_hbase_table = connection.table('test1_flight_delays') b = flight_delays_hbase_table.batch(batch_size=10000) with pyhs2.connect(host='bigdatalite', port=10000, authMechanism="PLAIN", user='oracle', password='welcome1', database='default') as conn: with conn.cursor() as cur: #Execute query cur.execute("select * from flight_delays_initial_load") #Fetch table results for i in cur.fetch(): b.put(str(i[0]),{'dims:year': i[1], 'dims:carrier': i[2], 'dims:orig': i[3], 'dims:dest': i[4], 'measures:flights': i[5], 'measures:late': i[6], 'measures:cancelled': i[7], 'measures:distance': i[8]}) b.send()
42. info@rittmanmead.com www.rittmanmead.com @rittmanmead 42 •Create Hive tables over the HBase ones to provide SQL load/query capabilities ‣Uses HBaseStorageHandler Storage Handler for HBAse ‣HBase columns mapped to Hive columns using SERDEPROPERTIES Create Hive Table Metadata over HBase Tables CREATE EXTERNAL TABLE hbase_flight_delays (key string, year string, carrier string, orig string, dest string, flights string, late string, cancelled string, distance string ) STORED BY 'org.apache.hadoop.hive.hbase.HBaseStorageHandler' WITH SERDEPROPERTIES ("hbase.columns.mapping" = ":key,dims:year,dims:carrier,dims:orig,dims:dest, measures:flights,measures:late,measures:cancelled,measures:distance") TBLPROPERTIES ("hbase.table.name" = "test1_flight_delays");
43. info@rittmanmead.com www.rittmanmead.com @rittmanmead 43 •Use HiveQL commands INSERT INTO TABLE … SELECT to load (merge) new data •Use HiveQL SELECT query to retrieve data from HBase table Load and Query HBase using HiveQL insert into table hbase_flight_delays select * from flight_delays_initial_load; Total jobs = 1 ... Total MapReduce CPU Time Spent: 11 seconds 870 msec OK Time taken: 40.301 seconds select count(*), min(cast(key as bigint)) as min_key, max(cast(key as bigint)) as max_key from hbase_flight_delays; Total jobs = 1 ... Total MapReduce CPU Time Spent: 14 seconds 660 msec OK 200000 1 200000 Time taken: 53.076 seconds, Fetched: 1 row(s)
44. info@rittmanmead.com www.rittmanmead.com @rittmanmead 44 •But Parquet (and HDFS) have significant limitation for real-time analytics applications ‣Append-only orientation, focus on column-store makes streaming ingestion harder •Cloudera Kudu aims to combine best of HDFS + HBase ‣Real-time analytics-optimised ‣Supports updates to data ‣Fast ingestion of data ‣Accessed using SQL-style tables and get/put/update/delete API Cloudera Kudu - Combining Best of HBase and Column-Store
45. info@rittmanmead.com www.rittmanmead.com @rittmanmead 45 •Kudu storage used with Impala - create tables using Kudu storage handler •Can now UPDATE, DELETE and INSERT into Hadoop tables, not just SELECT and LOAD DATA Example Impala DDL + DML Commands with Kudu CREATE TABLE `my_first_table` ( `id` BIGINT, `name` STRING ) TBLPROPERTIES( 'storage_handler' = 'com.cloudera.kudu.hive.KuduStorageHandler', 'kudu.table_name' = 'my_first_table', 'kudu.master_addresses' = 'kudu-master.example.com:7051', 'kudu.key_columns' = 'id' ); INSERT INTO my_first_table VALUES (99, "sarah"); INSERT IGNORE INTO my_first_table VALUES (99, "sarah"); UPDATE my_first_table SET name="bob" where id = 3; DELETE FROM my_first_table WHERE id < 3; DELETE c FROM my_second_table c, stock_symbols s WHERE c.name = s.symbol;
47. only one problem…
48. Hive is slow
49. too slow for ad-hoc querying
50. info@rittmanmead.com www.rittmanmead.com @rittmanmead 50 •MapReduce’s great innovation was to break processing down into distributed jobs •Jobs that have no functional dependency on each other, only upstream tasks •Provides a framework that is infinitely scalable and very fault tolerant •Hadoop handled job scheduling and resource management ‣All MapReduce code had to do was provide the “map” and “reduce” functions ‣Automatic distributed processing ‣Slow but extremely powerful Hadoop 1.0 and MapReduce
51. info@rittmanmead.com www.rittmanmead.com @rittmanmead 51 •A typical Hive or Pig script compiles down into multiple MapReduce jobs •Each job stages its intermediate results to disk •Safe, but slow - write to disk, spin-up separate JVMs for each job MapReduce - Scales By Writing Intermediate Results to Disk SELECT LOWER(hashtags.text), COUNT(*) AS total_count FROM ( SELECT * FROM tweets WHERE regexp_extract(created_at,"(2015)*",1) = "2015" ) tweets LATERAL VIEW EXPLODE(entities.hashtags) t1 AS hashtags GROUP BY LOWER(hashtags.text) ORDER BY total_count DESC LIMIT 15 MapReduce Jobs Launched: Stage-Stage-1: Map: 1 Reduce: 1 Cumulative CPU: 5.34 sec HDFS Read: 10952994 HDFS Write: 5239 SUCCESS Stage-Stage-2: Map: 1 Reduce: 1 Cumulative CPU: 2.1 sec HDFS Read: 9983 HDFS Write: 164 SUCCESS Total MapReduce CPU Time Spent: 7 seconds 440 msec OK 1 2
52. info@rittmanmead.com www.rittmanmead.com @rittmanmead 52 •MapReduce 2 (MR2) splits the functionality of the JobTracker by separating resource management and job scheduling/monitoring •Introduces YARN (Yet Another Resource Manager) •Permits other processing frameworks to MR ‣For example, Apache Spark •Maintains backwards compatibility with MR1 •Introduced with CDH5+ MapReduce 2 and YARN Node Manager Node Manager Node Manager Resource Manager Client Client
53. info@rittmanmead.com www.rittmanmead.com @rittmanmead 53 •Runs on top of YARN, provides a faster execution engine than MapReduce for Hive, Pig etc •Models processing as an entire data flow graph (DAG), rather than separate job steps ‣DAG (Directed Acyclic Graph) is a new programming style for distributed systems ‣Dataflow steps pass data between them as streams, rather than writing/reading from disk •Supports in-memory computation, enables Hive on Tez (Stinger) and Pig on Tez •Favoured In-memory / Hive v2 route by Hortonworks Apache Tez Input Data TEZ DAG Map() Map() Map() Reduce() Output Data Reduce() Reduce() Reduce() Input Data Map() Map() Reduce() Reduce()
54. info@rittmanmead.com www.rittmanmead.com @rittmanmead 54 Tez Advantage - Drop-In Replacement for MR with Hive, Pig set hive.execution.engine=mr set hive.execution.engine=tez 4m 17s 2m 25s
56. info@rittmanmead.com www.rittmanmead.com @rittmanmead 56 •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
57. info@rittmanmead.com www.rittmanmead.com @rittmanmead 57 How Impala Works Impala Daemon HDFS DataNode SQL App ODBC / JDBC HDFS DataNode HDFS DataNode HDFS DataNode Impala Daemon Impala Daemon Impala Daemon Hive MetaStore Impala StateStore •Cloudera-based solution for ad-hoc SQL-on-Hadoop •MPP SQL query engine running on Hadoop, with daemons running on each Hadoop node •In contrast to jobs being submitted via YARN •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
58. info@rittmanmead.com www.rittmanmead.com @rittmanmead 58 •Log into Impala Shell, run INVALIDATE METADATA command to refresh Impala table list •Run SHOW TABLES Impala SQL command to view tables available •Run COUNT(*) on main ACCESS_PER_POST table to see typical response time Enabling Hive Tables for Impala [oracle@bigdatalite ~]$ impala-shell Starting Impala Shell without Kerberos authentication [bigdatalite.localdomain:21000] > invalidate metadata; Query: invalidate metadata Fetched 0 row(s) in 2.18s [bigdatalite.localdomain:21000] > show tables; Query: show tables +-----------------------------------+ | name | +-----------------------------------+ | access_per_post | | access_per_post_cat_author | | … | | posts | |——————————————————————————————————-+ Fetched 45 row(s) in 0.15s [bigdatalite.localdomain:21000] > select count(*) from access_per_post; Query: select count(*) from access_per_post +----------+ | count(*) | +----------+ | 343 | +----------+ Fetched 1 row(s) in 2.76s
59. info@rittmanmead.com www.rittmanmead.com @rittmanmead 59 •Significant improvement over Hive response time •Now makes Hadoop suitable for ad-hoc querying Significantly-Improved Ad-Hoc Query Response Time vs Hive | Logical Query Summary Stats: Elapsed time 2, Response time 1, Compilation time 0 (seconds) Logical Query Summary Stats: Elapsed time 50, Response time 49, Compilation time 0 (seconds) Simple Two-Table Join against Hive Data Only Simple Two-Table Join against Impala Data Only vs
60. What about integration with my Data Warehouse?
61. info@rittmanmead.com www.rittmanmead.com @rittmanmead 61 •Most Traditional data warehousing vendors offer a Hadoop integration option •Oracle Big Data SQL •IBM Big SQL etc •Leverage lower-level SQL-on-Hadoop metadata but use own server process •Allows DBAs to write SQL using RDBMS SQL dialect, run across relational, Hadoop and NoSQL servers Hadoop Distributed Filesystem (HDFS) YARN Resource Manager Query and Processing Engines Oracle Big Data SQL Server Unstructured / Semi-Structured Log Data Offloaded Archive Data Social Graphs & Networks Smart Meter & Sensor Data 61 NoSQL Key-Value Store DB Platform for Traditional DW Integration with Hadoop Oracle RDBMS
62. info@rittmanmead.com www.rittmanmead.com @rittmanmead 62 •Originally Part of Oracle Big Data 4.0 (BDA-only) but now available for commodity Hadoop installs ‣Also requires Oracle Database 12c (no longer dependent on Exadata from Big Data SQL 4.0) ‣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) Oracle Big Data SQL Exadata Storage Servers Hadoop Cluster Exadata Database Server Oracle Big Data SQL SQL Queries SmartScan SmartScan
63. info@rittmanmead.com www.rittmanmead.com @rittmanmead 63 •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
64. info@rittmanmead.com www.rittmanmead.com @rittmanmead 64 •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));
65. info@rittmanmead.com www.rittmanmead.com @rittmanmead 65 •Brings query-offloading features similar to 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
66. hold on…
68. “where we’re going we don’t need roads”
69. “where we’re going we don’t need roads”
70. info@rittmanmead.com 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) Introducing Apache Drill - “We Don’t Need No Roads”
71. info@rittmanmead.com www.rittmanmead.com @rittmanmead •Most modern datasource formats embed their schema in the data (“schema-on-read”) •Apache Drill makes these as easy to join to traditional datasets as “point me at the data” •Cuts out unnecessary work in defining Hive schemas for data that’s self-describing •Supports joining across files, databases, NoSQL etc Self-Describing Data - Parquet, AVRO, JSON etc
72. info@rittmanmead.com www.rittmanmead.com @rittmanmead •Files can exist either on the local filesystem, or on HDFS •Connection to directory or file defined in storage configuration •Can work with CSV, TXT, TSV etc •First row of file can provide schema (column names) Apache Drill and Text Files SELECT * FROM dfs.`/tmp/csv_with_header.csv2`; +-------+------+------+------+ | name | num1 | num2 | num3 | +-------+------+------+------+ | hello | 1 | 2 | 3 | | hello | 1 | 2 | 3 | | hello | 1 | 2 | 3 | | hello | 1 | 2 | 3 | | hello | 1 | 2 | 3 | | hello | 1 | 2 | 3 | | hello | 1 | 2 | 3 | +-------+------+------+------+ 7 rows selected (0.12 seconds) SELECT * FROM dfs.`/tmp/csv_no_header.csv`; +------------------------+ | columns | +------------------------+ | ["hello","1","2","3"] | | ["hello","1","2","3"] | | ["hello","1","2","3"] | | ["hello","1","2","3"] | | ["hello","1","2","3"] | | ["hello","1","2","3"] | | ["hello","1","2","3"] | +------------------------+ 7 rows selected (0.112 seconds)
73. info@rittmanmead.com www.rittmanmead.com @rittmanmead •JSON (Javascript Object Notation) documents are often used for data interchange •Exports from Twitter and other consumer services •Web service responses and other B2B interfaces •A more lightweight form of XML that is “self- describing” •Handles evolving schemas, and optional attributes •Drill treats each document as a row, and has features to •Flatten nested data (extract elements from arrays) •Generate key/value pairs for loosely structured data Apache Drill and JSON Documents use dfs.iot; show files; select in_reply_to_user_id, text from `all_tweets.json` limit 5; +---------------------+------+ | in_reply_to_user_id | text | +---------------------+------+ | null | BI Forum 2013 in Brighton has now sold-out | | null | "Football has become a numbers game | | null | Just bought Lyndsay Wise’s Book | | null | An Oracle BI "Blast from the Past" | | 14716125 | Dilbert on Agile Programming | +---------------------+------+ 5 rows selected (0.229 seconds) select name, flatten(fillings) as f from dfs.users.`/donuts.json` where f.cal < 300;
74. info@rittmanmead.com www.rittmanmead.com @rittmanmead •Drill can connect to Hive to make use of metastore (incl. multiple Hive metastores) •NoSQL databases (HBase etc) •Parquet files (native storage format - columnar + self describing) Apache Drill and Hive, HBase, Parquet Sources etc USE hbase; SELECT * FROM students; +-------------+-----------------------+-----------------------------------------------------+ | row_key | account | address | +-------------+-----------------------+------------------------------------------------------+ | [B@e6d9eb7 | {"name":"QWxpY2U="} | {"state":"Q0E=","street":"MTIzIEJhbGxtZXIgQXY="} | | [B@2823a2b4 | {"name":"Qm9i"} | {"state":"Q0E=","street":"MSBJbmZpbml0ZSBMb29w"} | | [B@3b8eec02 | {"name":"RnJhbms="} | {"state":"Q0E=","street":"NDM1IFdhbGtlciBDdA=="} | | [B@242895da | {"name":"TWFyeQ=="} | {"state":"Q0E=","street":"NTYgU291dGhlcm4gUGt3eQ=="} | +-------------+-----------------------+----------------------------------------------------------------------+ SELECT firstname,lastname FROM hiveremote.`customers` limit 10;` +------------+------------+ | firstname | lastname | +------------+------------+ | Essie | Vaill | | Cruz | Roudabush | | Billie | Tinnes | | Zackary | Mockus | | Rosemarie | Fifield | | Bernard | Laboy | | Marianne | Earman | +------------+------------+ SELECT * FROM dfs.`iot_demo/geodata/region.parquet`; +--------------+--------------+-----------------------+ | R_REGIONKEY | R_NAME | R_COMMENT | +--------------+--------------+-----------------------+ | 0 | AFRICA | lar deposits. blithe | | 1 | AMERICA | hs use ironic, even | | 2 | ASIA | ges. thinly even pin | | 3 | EUROPE | ly final courts cajo | | 4 | MIDDLE EAST | uickly special accou | +--------------+--------------+-----------------------+
75. info@rittmanmead.com www.rittmanmead.com @rittmanmead •Drill developed for real-time, ad-hoc data exploration with schema discovery on-the-fly •Individual analysts exploring new datasets, leveraging corporate metadata/data to help •Hive is more about large-scale, centrally curated set-based big data access •Drill models conceptually as JSON, vs. Hive’s tabular approach •Drill introspects schema from whatever it connects to, vs. formal modeling in Hive Apache Drill vs. Apache Hive Interactive Queries (Data Discovery, Tableau/VA) Reporting Queries (Canned Reports, OBIEE) ETL (ODI, Scripting, Informatica) Apache Drill Apache Hive Interactive Queries 100ms - 3mins Reporting Queries 3mins - 20mins ETL & Batch Queries 20mins - hours
76. but…
77. what’s all this about “Spark”?
78. info@rittmanmead.com www.rittmanmead.com @rittmanmead 78 •Another DAG execution engine running on YARN •More mature than TEZ, with richer API and more vendor support •Uses concept of an RDD (Resilient Distributed Dataset) ‣RDDs like tables or Pig relations, but can be cached in-memory ‣Great for in-memory transformations, or iterative/cyclic processes •Spark jobs comprise of a DAG of tasks operating on RDDs •Access through Scala, Python or Java APIs •Related projects include ‣Spark SQL ‣Spark Streaming Apache Spark
79. info@rittmanmead.com www.rittmanmead.com @rittmanmead 79 •Native support for multiple languages with identical APIs ‣Python - prototyping, data wrangling ‣Scala - functional programming features ‣Java - lower-level, application integration •Use of closures, iterations, and other common language constructs to minimize code •Integrated support for distributed + functional programming •Unified API for batch and streaming Rich Developer Support + Wide Developer Ecosystem scala> val logfile = sc.textFile("logs/access_log") 14/05/12 21:18:59 INFO MemoryStore: ensureFreeSpace(77353) called with curMem=234759, maxMem=309225062 14/05/12 21:18:59 INFO MemoryStore: Block broadcast_2 stored as values to memory (estimated size 75.5 KB, free 294.6 MB) logfile: org.apache.spark.rdd.RDD[String] = MappedRDD[31] at textFile at <console>:15 scala> logfile.count() 14/05/12 21:19:06 INFO FileInputFormat: Total input paths to process : 1 14/05/12 21:19:06 INFO SparkContext: Starting job: count at <console>:1 ... 14/05/12 21:19:06 INFO SparkContext: Job finished: count at <console>:18, took 0.192536694 s res7: Long = 154563 scala> val logfile = sc.textFile("logs/access_log").cache scala> val biapps11g = logfile.filter(line => line.contains("/biapps11g/")) biapps11g: org.apache.spark.rdd.RDD[String] = FilteredRDD[34] at filter at <console>:17 scala> biapps11g.count() ... 14/05/12 21:28:28 INFO SparkContext: Job finished: count at <console>:20, took 0.387960876 s res9: Long = 403
80. info@rittmanmead.com www.rittmanmead.com @rittmanmead 80 •Spark SQL, and Data Frames, allow RDDs in Spark to be processed using SQL queries •Bring in and federate additional data from JDBC sources •Load, read and save data in Hive, Parquet and other structured tabular formats Spark SQL - Adding SQL Processing to Apache Spark val accessLogsFilteredDF = accessLogs .filter( r => ! r.agent.matches(".*(spider|robot|bot|slurp).*")) .filter( r => ! r.endpoint.matches(".*(wp-content|wp-admin).*")).toDF() .registerTempTable("accessLogsFiltered") val topTenPostsLast24Hour = sqlContext.sql("SELECT p.POST_TITLE, p.POST_AUTHOR, COUNT(*) as total FROM accessLogsFiltered a JOIN posts p ON a.endpoint = p.POST_SLUG GROUP BY p.POST_TITLE, p.POST_AUTHOR ORDER BY total DESC LIMIT 10 ") // Persist top ten table for this window to HDFS as parquet file topTenPostsLast24Hour.save("/user/oracle/rm_logs_batch_output/topTenPostsLast24Hour.parquet" , "parquet", SaveMode.Overwrite)
81. Hadoop is insecure and has fragmented security …doesn’t it? but …
82. info@rittmanmead.com www.rittmanmead.com @rittmanmead 82 Consistent Security and Audit Now Emerging on Platform
83. info@rittmanmead.com www.rittmanmead.com @rittmanmead 83 •Clusters by default are unsecured (vunerable to account spoofing) & need Kerberos enabled •Data access controlled by POSIX-style permissions on HDFS files •Hive and Impala can Apache Sentry RBAC ‣Result is data duplication and complexity ‣No consistent API or abstracted security model Hadoop Security Initially Was a Mess /user/mrittman/scratchpad /user/ryeardley/scratchpad /user/mpatel/scratchpad /user/mrittman/scratchpad /user/mrittman/scratchpad /data/rm_website_analysis/logfiles/incoming /data/rm_website_analysis/logfiles/archive /data/rm_website_analysis/tweets/incoming /data/rm_website_analysis/tweets/archive
84. info@rittmanmead.com www.rittmanmead.com @rittmanmead 84 •Use standard Oracle Security over Hadoop & NoSQL ‣Grant & Revoke Privileges ‣Redact Data ‣Apply Virtual Private Database ‣Provides Fine-grain Access Control •Great solution to extend existing Oracle security model over Hadoop datasets Oracle Big Data SQL : Extend Oracle Security to Hadoop DBMS_REDACT.ADD_POLICY( object_schema => 'txadp_hive_01', object_name => 'customer_address_ext', column_name => 'ca_street_name', policy_name => 'customer_address_redaction', function_type => DBMS_REDACT.RANDOM, expression => 'SYS_CONTEXT(''SYS_SESSION_ROLES'', ''REDACTION_TESTER'')=''TRUE''' );
85. info@rittmanmead.com www.rittmanmead.com @rittmanmead 85 •Provides a higher level, logical abstraction for data (ie Tables or Views) ‣Can be used with Spark & Spark SQL, with Predicate pushdown, projection •Returns schemed objects (instead of paths and bytes) in similar way to HCatalog •Unified data access path allows platform-wide performance improvements •Secure service that does not execute arbitrary user code ‣Central location for all authorization checks using Sentry metadata. Cloudera RecordService
87. info@rittmanmead.com www.rittmanmead.com @rittmanmead 87 Choosing a SQL-on-Hadoop Engine The original SQL-on-Hadoop engine Maximum compatibility with Hadoop … but designed for batch processing Plug-in replacement for MapReduce Works via YARN and submitting jobs Speeds-up Hive but long-term future? Daemon-based MPP engines Impala is more mature Drill innovates around data-discovery Adds SQL access and set-based processing to Spark Useful for query federation Vendor-provided RBDMS-Hadoop integration bridges
88. You don’t need to learn Java, or MapReduce, or Scala
89. or Toupee
90. SQL-on-Hadoop isn’t just Hive
91. Check-out these Developer VMs: http://www.cloudera.com/documentation/enterprise/5-3- x/topics/cloudera_quickstart_vm.html http://hortonworks.com/products/sandbox/ http://www.oracle.com/technetwork/database/bigdata-appliance/oracle-bigdatalite- 2104726.html https://www.mapr.com/products/mapr-sandbox-hadoop/download-sandbox-drill
92. http://www.rittmanmead.com
93. info@rittmanmead.com www.rittmanmead.com @rittmanmead Gluent New World #02: SQL-on-Hadoop with Mark Rittman Mark Rittman, CTO, Rittman Mead April 2016