XLDB 2013

XLDB 2013 Tutorial Session
Hand On With Druid

Transcript

1. HANDS ON WITH DRUID REAL²TIME EXPLORATORY ANALYTICS ON LARGE DATASETS FANGJIN YANG · XAVIER LÉAUTÉ wifi XLDB2013 · StanfordXLDB links bit.ly/druid-xldb-2013 Monday, September 9, 13
2. DEMO SEE SOME NEAT THINGS MOTIVATION WHY DRUID? ARCHITECTURE SETUP AND USE A DRUID CLUSTER BUILDING APPLICATIONS WITH DRUID OVERVIEW Monday, September 9, 13
3. DEMO IN CASE THE INTERNET DIDN’T WORK PRETEND YOU SAW SOMETHING COOL Monday, September 9, 13
4. PROBLEMS Monday, September 9, 13
5. 2013 DATA INGESTION Monday, September 9, 13
6. 2013 DATA EXPLORATION Monday, September 9, 13
7. 2013 AVAILABILITY Monday, September 9, 13
8. WHAT WE TRIED Monday, September 9, 13
9. 2013 I. RDBMS - Relational Database WHAT WE TRIED Monday, September 9, 13
10. 2013 ‣ Star Schema ‣ Aggregate Tables ‣ Query Caching I. RDBMS - THE SETUP Monday, September 9, 13
11. 2013 ‣ Queries that were cached • fast ‣ Queries against aggregate tables • fast to acceptable ‣ Queries against base fact table • generally unacceptable I. RDBMS - THE RESULTS Monday, September 9, 13
12. 2013 I. RDBMS - PERFORMANCE Naive benchmark scan rate ~5.5M rows / second / core 1 day of summarized aggregates 60M+ rows 1 query over 1 week, 16 cores ~5 seconds Page load with 20 queries over a week of data long time Monday, September 9, 13
13. 2013 I. RDBMS - Relational Database WHAT WE TRIED Monday, September 9, 13
14. 2013 I. RDMBS - Relational Database II.NoSQL - Key/Value Store WHAT WE TRIED Monday, September 9, 13
15. 2013 ‣ Pre-aggregate all dimensional combinations ‣ Store results in a NoSQL store II. NOSQL - THE SETUP ts gender age revenue 1 M 18 $0.15 1 F 25 $1.03 1 F 18 $0.01 Key Value 1 revenue=$1.19 1,M revenue=$0.15 1,F revenue=$1.04 1,18 revenue=$0.16 1,25 revenue=$1.03 1,M,18 revenue=$0.15 1,F,18 revenue=$0.01 1,F,25 revenue=$1.03 Monday, September 9, 13
16. 2013 ‣ Queries were fast • range scan on primary key ‣ Inflexible • not aggregated, not available ‣ Not continuously updated • aggregate first, then display ‣ Processing scales exponentially II. NOSQL - THE RESULTS Monday, September 9, 13
17. 2013 ‣ Dimensional combinations => exponential increase ‣ Tried limiting dimensional depth • still expands exponentially ‣ Example: ~500k records • 11 dimensions, 5-deep • 4.5 hours on a 15-node Hadoop cluster • 14 dimensions, 5-deep • 9 hours on a 25-node Hadoop cluster II. NOSQL - PERFORMANCE Monday, September 9, 13
18. 2013 I. RDMBS - Relational Database II.NoSQL - Key/Value Store WHAT WE TRIED Monday, September 9, 13
19. 2013 I. RDMBS - Relational Database II.NoSQL - Key/Value Store III. ??? WHAT WE TRIED Monday, September 9, 13
20. 2013 ‣ Problem with RDBMS: scans are slow ‣ Problem with NoSQL: computationally intractable WHAT WE LEARNED Monday, September 9, 13
21. 2013 ‣ Problem with RDBMS: scans are slow ‣ Problem with NoSQL: computationally intractable ‣ Tackling the RDBMS issue seems easier WHAT WE LEARNED Monday, September 9, 13
22. “ ” INTRODUCING DRUID Monday, September 9, 13
23. Monday, September 9, 13
24. 2013 KEY FEATURES FAST AGGREGATIONS ARBITRARY SLICE-N-DICE DATA HIGHLY AVAILABLE “REAL-TIME” DRUID Monday, September 9, 13
25. REAL-TIME · WHAT DOES IT MEAN? Monday, September 9, 13
26. “ ” “REAL-TIME” IS THE NEW “CLOUD” THE NEW ADJECTIVE THAT EVERYONE WANTS BUT NO ONE KNOWS WHAT IT MEANS Monday, September 9, 13
27. 2013 WHAT IT MEANS TO US RATE OF QUERY RETURN QUERY TIME < 5S INGESTION LATENCY EVENT OCCURRENCE TO VISIBILITY < 10S Monday, September 9, 13
28. 2013 REAL-TIME CHALLENGES ‣ Availability ‣ Failure Recovery ‣ Increasing data volumes ‣ Consistency Monday, September 9, 13
29. HOW IS DRUID REALLY REAL-TIME FOR REAL? Monday, September 9, 13
30. 2013 ARCHITECTURE Monday, September 9, 13
31. 2013 ARCHITECTURE Monday, September 9, 13
32. 2013 ARCHITECTURE Realtime Nodes Query API Monday, September 9, 13
33. 2013 ‣ Ingest Data streams ‣ Query data as soon as it is ingested ‣ Buffer data in memory ‣ Persist to local disk at configurable time window ‣ Merge and persist to deep storage at (wider) time window REAL-TIME NODES Monday, September 9, 13
34. 2013 DRUID REALTIME INGESTION Real-time Node data stream Monday, September 9, 13
35. 2013 DRUID REALTIME INGESTION Real-time Node data stream Disk Monday, September 9, 13
36. 2013 DRUID REALTIME INGESTION Real-time Node data stream Disk Deep Storage Monday, September 9, 13
37. bit.ly/your-first-druid YOUR FIRST DRUID Monday, September 9, 13
38. 2013 1. wget http://static.druid.io/artifacts/releases/ druid-services-0.5.54-bin.tar.gz 2. tar -zxvf druid-services-*-bin.tar.gz 3. cd druid-services-0.5.54 INSTALLATION Getting the tarball Monday, September 9, 13
39. 2013 1. > ./run_example_server.sh 2. > ./run_example_client.sh EXAMPLES Example Scripts Start a mini-druid | Run some queries Monday, September 9, 13
40. 2013 { "queryType" : "timeBoundary", "dataSource": "wikipedia" } curl -X POST 'http://localhost:8083/druid/v2/?pretty' \ -H 'content-type: application/json' \ -d @time_boundary_query.body QUERYING TimeBoundary Queries Monday, September 9, 13
41. 2013 { "queryType": "timeseries", "dataSource": "wikipedia", "intervals": ["2010-01-01/2020-01-01"], "granularity": "minute", "aggregations": [{"type": "longSum", "fieldName": "count", "name": "edit_count"}, {"type": "doubleSum", "fieldName": "added", "name": "chars_added"}] } curl -X POST 'http://localhost:8083/druid/v2/?pretty' -H 'content- type: application/json' -d @timeseries_query.body QUERYING Timeseries Queries Monday, September 9, 13
42. 2013 { "queryType" : "groupBy", "dataSource" : "wikipedia", "granularity": "all", "dimensions" : ["page"], "orderBy" : { "type": "default", "columns": [{"dimension": "edit_count", "direction": "DESCENDING"}], "limit" : 10 }, "aggregations": [{"type": "longSum", "fieldName": "count", "name": "edit_count"}], "filter" : {"type": "selector", "dimension": "country", "value": "United States"}, "intervals": ["2012-10-01T00:00/2020-01-01T00"] } curl -X POST 'http://localhost:8083/druid/v2/?pretty' -H 'content-type: application/ json' -d @group_by_query.body QUERYING GroupBy Queries Monday, September 9, 13
43. 2013 DRUID INGESTION LATENCY: BEST PRACTICES Real-time Node Message bus Data stream Monday, September 9, 13
44. 2013 DRUID INGESTION LATENCY: BEST PRACTICES Real-time Nodes Message bus Monday, September 9, 13
45. 2013 DRUID INGESTION LATENCY: BEST PRACTICES Real-time Nodes Message bus Monday, September 9, 13
46. 2013 ‣ Intermediate message bus • Track event ingestion • Persist events and update message stream offsets • Replay events • Recover from lost process by replaying events that haven’t persisted • Consume same feed multiple times • Replicate by having multiple real-time nodes process the same stream DRUID INGESTION LATENCY: BEST PRACTICES Monday, September 9, 13
47. 2013 ‣ Replication ‣ Minimize reads after failure from message bus ‣ Periodic batch processes to “clean up” data DRUID INGESTION LATENCY: AVAILABILITY Monday, September 9, 13
48. 2013 REALTIME INGESTION 10 – 100K RECORDS / SECOND / NODE REAL-WORLD CLUSTER 150K EVENTS/S (7B EVENTS/DAY) 500MB/S (2 TB/H) DRUID BENCHMARKS Monday, September 9, 13
49. 2013 ARCHITECTURE Realtime Nodes Query API Monday, September 9, 13
50. 2013 ARCHITECTURE Realtime Nodes Query API Query API Compute Nodes Deep Storage Monday, September 9, 13
51. 2013 • Main workhorses of a Druid cluster • Load historical data (most data in a cluster is historical data) from deep storage • Respond to queries COMPUTE NODES Monday, September 9, 13
52. 2013 ARCHITECTURE Query API Compute Nodes Realtime Nodes Query API Deep Storage Monday, September 9, 13
53. 2013 ARCHITECTURE Query API Compute Nodes Realtime Nodes Query API Broker Nodes Query API Query Rewrite Scatter/Gather Deep Storage Monday, September 9, 13
54. 2013 • Knows which nodes hold what data • Query scatter/gather (send requests to nodes and merge results) • Caching BROKER NODES Monday, September 9, 13
55. Compute Nodes Broker Nodes Real-time Nodes Data Stream Master Nodes Queries Deep Storage ARCHITECTURE Monday, September 9, 13
56. 2013 • Distributes data across historical nodes • Assigns historical nodes to load & drop data • Manages replication MASTER NODES Monday, September 9, 13
57. Compute Nodes Broker Nodes Real-time Nodes Zookeeper Queries MySQL Data Stream Master Nodes ARCHITECTURE Deep Storage Monday, September 9, 13
58. 2013 • Deep storage: permanent backup • Apache Zookeeper: coordination • Relational database: Store for metadata and configuration EXTERNAL DEPENDENCIES Monday, September 9, 13
59. BUILDING A CLUSTER bit.ly/build-druid-cluster Monday, September 9, 13
60. 2013 # download http://dev.mysql.com/downloads/mysql/ # or (if you have homebrew) brew install mysql mysql -u root GRANT ALL ON druid.* TO 'druid'@'localhost' IDENTIFIED BY 'diurd'; CREATE database druid; INSTALLING EXTENSIONS MySQL Monday, September 9, 13
61. 2013 curl http://www.motorlogy.com/apache/zookeeper/zookeeper-3.4.5/ {zookeeper-3.4.5.tar.gz} -o “#1” tar xzf zookeeper-3.4.5.tar.gz cd zookeeper-3.4.5 cp conf/zoo_sample.cfg conf/zoo.cfg ./bin/zkServer.sh start cd .. INSTALLING EXTENSIONS Apache Zookeeper Monday, September 9, 13
62. 2013 1. Start a Compute Node 2. Start a Broker Node 3. Start a Master Node 4. Load Data STARTING A FULL DRUID CLUSTER Druid Nodes Monday, September 9, 13
63. 2013 ‣ Whirr • Deploy different node types in an AWS environment • Minimal configuration • bit.ly/boot-druid-cluster ‣ Loading Data? • Real-time data ingestion • Hadoop-based batch ingestion ONE CLICK CLUSTER SETUP Monday, September 9, 13
64. 2013 DATA! timestamp page language city country ... added deleted 2011-01-01T00:01:35Z Justin Bieber en SF USA 10 65 2011-01-01T00:03:63Z Justin Bieber en SF USA 15 62 2011-01-01T00:04:51Z Justin Bieber en SF USA 32 45 2011-01-01T01:00:00Z Ke$ha en Calgary CA 17 87 2011-01-01T02:00:00Z Ke$ha en Calgary CA 43 99 2011-01-01T02:00:00Z Ke$ha en Calgary CA 12 53 ... Monday, September 9, 13
65. 2013 COLUMN COMPRESSION · DICTIONARIES ‣ Create ids • Justin Bieber -> 0, Ke$ha -> 1 ‣ Store • page -> [0 0 0 1 1 1] • language -> [0 0 0 0 0 0] timestamp page language city country ... added deleted 2011-01-01T00:01:35Z Justin Bieber en SF USA 10 65 2011-01-01T00:03:63Z Justin Bieber en SF USA 15 62 2011-01-01T00:04:51Z Justin Bieber en SF USA 32 45 2011-01-01T01:00:00Z Ke$ha en Calgary CA 17 87 2011-01-01T02:00:00Z Ke$ha en Calgary CA 43 99 2011-01-01T02:00:00Z Ke$ha en Calgary CA 12 53 ... Monday, September 9, 13
66. 2013 BITMAP INDICES ‣ Justin Bieber -> [0, 1, 2] -> [111000] ‣ Ke$ha -> [3, 4, 5] -> [000111] timestamp page language city country ... added deleted 2011-01-01T00:01:35Z Justin Bieber en SF USA 10 65 2011-01-01T00:03:63Z Justin Bieber en SF USA 15 62 2011-01-01T00:04:51Z Justin Bieber en SF USA 32 45 2011-01-01T01:00:00Z Ke$ha en Calgary CA 17 87 2011-01-01T02:00:00Z Ke$ha en Calgary CA 43 99 2011-01-01T02:00:00Z Ke$ha en Calgary CA 12 53 ... Monday, September 9, 13
67. 2013 BITMAP INDEX COMPRESSION ‣ CONCISE compression • Boolean operations directly on compressed indices • Less memory => faster scan rates ‣ More details • http://ricerca.mat.uniroma3.it/users/colanton/concise.html Monday, September 9, 13
68. 2013 FAST AND FLEXIBLE QUERIES JUSTIN BIEBER [1, 1, 0, 0] KE$HA [0, 0, 1, 1] JUSTIN BIEBER OR KE$HA [1, 1, 1, 1] row page 0 Justin(Bieber 1 Justin(Bieber 2 Ke$ha 3 Ke$ha Monday, September 9, 13
69. 2013 REPLICATION ROLLING DEPLOYMENTS + RESTARTS GROW = START PROCESSES SHRINK = KILL PROCESSES 2 YEARS · NO DOWNTIME FOR SOFTWARE UPDATE AVAILABILITY Monday, September 9, 13
70. 2013 DRUID BENCHMARKS Monday, September 9, 13
71. 2013 SCAN SPEED ~33M ROWS / SECOND / CORE DRUID BENCHMARKS Monday, September 9, 13
72. 2013 SCAN SPEED ~33M ROWS / SECOND / CORE INDEX COMPRESSION CONCISE SETS 70% SMALLER THAN BINARY ARRAYS “OR” OPERATION WITH 1M DIMENSION VALUES IN ~5S DRUID BENCHMARKS Monday, September 9, 13
73. APPLICATION BUILDING WITH DRUID Monday, September 9, 13
74. 2013 install.packages("devtools") library(devtools) source_gist(6430470) DRUID + R prerequisites RDruid ggplot2 shiny Monday, September 9, 13
75. 2013 > ./run_example_server.sh wikipedia DRUID + R start collecting realtime wikipedia data from our realtime example earlier Monday, September 9, 13
76. 2013 library(shiny) shiny::runGist(6491596) # gist.github.com/6491596 TOY APPLICATION A simplistic real-time dashboard Monday, September 9, 13
77. 2013 shinyUI(pageWithSidebar( headerPanel("Druid | Wikipedia"), sidebarPanel( selectInput( "granularity", "Time resolution", choices = c("1 minute" = "PT1M", "5 minutes" = "PT5M", "15 minutes" = "PT15M"), selected = "PT1M" ) ), mainPanel( plotOutput("plot"), tableOutput("table") ) )) TOY APPLICATION User Interface Monday, September 9, 13
78. 2013 shinyUI(pageWithSidebar( headerPanel("Druid | Wikipedia"), sidebarPanel( selectInput( "granularity", "Time resolution", choices = c("1 minute" = "PT1M", "5 minutes" = "PT5M", "15 minutes" = "PT15M"), selected = "PT1M" ) ), mainPanel( plotOutput("plot"), tableOutput("table") ) )) TOY APPLICATION User Interface Monday, September 9, 13
79. 2013 url <- druid.url(host="localhost", port="8083"); datasource <- "wikipedia" timespan <- interval(ymd(20130101), ymd(20200101)) shinyServer(function(input, output, session) { tsdata <- reactive({ invalidateLater(3000, session) druid.query.timeseries(url=url, dataSource=datasource, intervals = timespan, aggregations = sum(metric("count")), granularity = granularity(input$granularity)) }) output$plot <- renderPlot({ print(ggplot(data=tsdata(), aes_string(x="timestamp", y="count")) + geom_line()) }) output$table <- renderTable({ df <- tsdata(); df$timestamp <- as.character(df$timestamp); df }) }) TOY APPLICATION Backend Monday, September 9, 13
80. 2013 url <- druid.url(host="localhost", port="8083"); datasource <- "wikipedia" timespan <- interval(ymd(20130101), ymd(20200101)) shinyServer(function(input, output, session) { tsdata <- reactive({ invalidateLater(3000, session) druid.query.timeseries(url=url, dataSource=datasource, intervals = timespan, aggregations = sum(metric("count")), granularity = granularity(input$granularity)) }) output$plot <- renderPlot({ print(ggplot(data=tsdata(), aes_string(x="timestamp", y="count")) + geom_line()) }) output$table <- renderTable({ df <- tsdata(); df$timestamp <- as.character(df$timestamp); df }) }) TOY APPLICATION Backend Monday, September 9, 13
81. 2013 edits <- druid.query.timeseries( url = druid.url("localhost", 8083), dataSource = "wikipedia", intervals = interval(ymd("2013-01-01"), ymd("2013-10-01")), aggregations = sum(metric("count")), granularity = granularity("PT1M") ) TIMESERIES QUERIES Wikipedia page edits count since January, by minute Monday, September 9, 13
82. 2013 enfr <- druid.query.timeseries( [...] granularity = granularity("PT1M"), filter = dimension("namespace") == "article" & ( dimension("language") == "en" | dimension("language") == "fr" ) ) FILTERS what if I’m only interested in articles in English or French Monday, September 9, 13
83. 2013 enfr <- druid.query.groupBy( [...] filter = dimension("namespace") == "article" & ( dimension("language") == "en" | dimension("language") == "fr" ), dimensions = list("language") ) qplot(data = enfr, x = timestamp, y = count, geom = "line", color = language) GROUP BY break it out by language Monday, September 9, 13
84. 2013 granularity = granularity( period = "PT6H", timeZone = "America/Los_Angeles" ) GRANULARITY slice time arbitrarily any period / any timezone Monday, September 9, 13
85. 2013 library(shiny) shiny::runGist(6427337) # gist.github.com/6427337 BIGGER TOY APPLICATION A little more advanced example Monday, September 9, 13
86. 2013 aggregations = list( rowcount = druid.count(), edits = sum(metric("count")), total = sum(metric("added")), maxdelta = max(metric("delta")) ) AGGREGATIONS sum min max count Monday, September 9, 13
87. 2013 aggregations = list( edits = sum(metric("count")), added = sum(metric("added")), deleted = sum(metric("deleted")) ), postAggregations = list( average = field("added") / field("edits"), pct = field("deleted") / field("added") * -100 ) POST-AGGREGATIONS a.k.a. math + - * / constants Monday, September 9, 13
88. 2013 # regular expression filters filter = dimension("user") %~% "^Bob.*" # JavaScript filters filter = druid.filter.javascript( dimension = "page", predicate = "function(x) { return(x >= 'bar' && x <= 'foo') }" ) ADVANCED FILTERS regular expressions javascript functions Monday, September 9, 13
89. 2013 # JavaScript aggregator javascript( fieldNames = list("page"), fnAggregate = "function(current, page) { return(current + page.length) }", fnCombine = "function(a, b) { return(a+b) }", fnReset = "function() { return(0) }" ) ADVANCED AGGREGATORS arbitrary javascript functions Monday, September 9, 13
90. DRUID IS OPEN SOURCE DRUID.IO twitter @druidio irc.freenode.net #druid-dev Monday, September 9, 13
91. THANK YOU Monday, September 9, 13