Treasure Data is a data analytics service company that makes heavy use of Ruby in its platform and services. It uses Ruby for components like Fluentd (log collection), Embulk (data loading), scheduling, and its Rails-based API and console. Java and JRuby are also used for components involving Hadoop and Presto processing. The company's architecture includes collectors that ingest data, a PlazmaDB for storage, workers that process jobs on Hadoop and Presto clusters, and schedulers that queue and schedule those jobs using technologies like PerfectSched and PerfectQueue which are written in Ruby. Hive jobs are built programmatically using Ruby to generate configurations and submit the jobs to underlying Hadoop clusters.

1. Data Analytics Service Company
and Its Ruby Usage
RubyKaigi 2015 (Dec 12, 2015)
Satoshi Tagomori (@tagomoris)

2. Satoshi "Moris" Tagomori
(@tagomoris)
Fluentd, MessagePack-Ruby, Norikra, ...
Treasure Data, Inc.

4. http://www.treasuredata.com/

5. http://www.fluentd.org/
Fluentd
Unified Logging Layer
For Stream Data
Written in CRuby
http://www.slideshare.net/treasure-data/the-basics-of-fluentd-35681111

6. Bulk Data Loader
High Throughput&Reliability
Embulk
Written in Java/JRuby
http://www.slideshare.net/frsyuki/embuk-making-data-integration-works-relaxed
http://www.embulk.org/

7. Data Analytics Platform
Data Analytics Service

10. Services

11. Services
JVM

12. Services
JVMC++

13. Data Analytics Flow
Collect Store Process Visualize
Data source
Reporting
Monitoring

14. Data Analytics Flow
Collect Store Process Visualize
Data source
Reporting
Monitoring

15. Data Analytics Platform
• Data collection, storage
• Console & API endpoints
• Schema management
• Processing (batch, query, ...)
• Queuing & Scheduling
• Data connector/exporter

16. Treasure Data Internals

17. Data Analytics Platform
• Data collection, storage: Ruby(OSS), Java/JRuby(OSS)
• Console & API endpoints: Ruby(RoR)
• Schema management: Ruby/Java (MessagePack)
• Processing (batch, query, ...): Java(Hadoop,Presto)
• Queuing & Scheduling: Ruby(OSS)
• Data connector/exporter: Java, Java/JRuby(OSS)

18. Treasure Data Architecture: Overview
http://www.slideshare.net/tagomoris/data-analytics-service-company-and-its-ruby-usage
Console
API
EventCollector
PlazmaDB
Worker
Hadoop
Cluster
Presto
Cluster
USERS
TD SDKs
SERVERS
DataConnector
CUSTOMER's
SYSTEMS
Scheduler

19. Console
API
EventCollector
PlazmaDB
Worker
Scheduler
Hadoop
Cluster
Presto
Cluster
USERS
TD SDKs
SERVERS
DataConnector
CUSTOMER's
SYSTEMS

20. Console
API
EventCollector
PlazmaDB
Worker
Scheduler
Hadoop
Cluster
Presto
Cluster
USERS
TD SDKs
SERVERS
DataConnector
CUSTOMER's
SYSTEMS
50k/day
200k/day

21. Console
API
EventCollector
PlazmaDB
Worker
Scheduler
Hadoop
Cluster
Presto
Cluster
USERS
TD SDKs
SERVERS
DataConnector
CUSTOMER's
SYSTEMS
50k/day
200k/day
12M/day
(138/sec)

22. Queue/Worker, Scheduler
• Treasure Data: multi-tenant data analytics service
• executes many jobs in shared clusters (queries,
imports, ...)
• CORE: queues-workers & schedulers
• Clusters have queues/scheduler... it's not enough
• resource limitations for each price plans
• priority queues for job types
• and many others

23. PerfectSched
https://github.com/treasure-data/perfectsched

24. PerfectSched
• Provides periodical/scheduled queries for customers
• it's like reliable "cron"
• Highly available distributed scheduler using RDBMS
• Written in CRuby
• At-least-once semantics
• PerfectSched enqueues jobs into PerfectQueue

25. Jobs in TD
LOST Duplicated
Retried
for
Errors
Throughput
Execution
time
DATA
import/
export
NG NG
OK
or
NG
HIGH SHORT
(secs-mins)
QUERY NG OK OK LOW
SHORT
(secs)
or
LONG
(mins-hours)

26. PerfectQueue
https://github.com/treasure-data/perfectqueue

27. Worker
Worker
Worker
Worker
PerfectQueue overview
Worker
MySQL
1 table for 1 queue
workers for queues
hypervisor process
worker processes
worker processes
worker processes

28. Features
• Priorities for query types
• Resource limits per accounts
• Graceful restarts
• Queries must run long time (<= 1d)
• New worker code should be loaded, besides
running job with older code

29. PerfectQueue
• Highly available distributed queue using RDBMS
• Enqueue by INSERT INTO
• Dequeue/Commit by UPDATE
• Using transactions
• Flexible scheduling rather than scalability
• Workers does many things
• Plazmadb operations (including importing data)
• Building job parameters
• Handling results of jobs + kicking other jobs
• Using Amazon RDS (MySQL) internally (+ Workers on EC2)

30. Builing Jobs/Parameters
• Parameters
• for job types, accounts, price plans and clusters
• to control performance/parallelism, permissions
and data types
• ex: Java properties
• Jobs
• to prepare for customers' queries
• to make queries safer/faster
• ex: Hive Queries (HiveQL, a variety of SQL)

31. Example: Hive job
env HADOOP_CLASSPATH=test.jar:td-hadoop-1.0.jar
HADOOP_OPTS="-Xmx738m -Duser.name=221"
hive --service jar td-hadoop-1.0.jar
com.treasure_data.hadoop.hive.runner.QueryRunner
-hiveconf td.jar.version=
-hiveconf plazma.metadb.config={}
-hiveconf plazma.storage.config={}
-hiveconf td.worker.database.config={}
-hiveconf mapreduce.job.priority=HIGH
-hiveconf mapreduce.job.queuename=root.q221.high
-hiveconf mapreduce.job.name=HiveJob379515
-hiveconf td.query.mergeThreshold=1333382400
-hiveconf td.query.apikey=12345
-hiveconf td.scheduled.time=1342449253
-hiveconf td.outdir=./jobs/379515
-hiveconf hive.metastore.warehouse.dir=/user/hive/221/warehouse
-hiveconf hive.auto.convert.join.noconditionaltask=false
-hiveconf hive.mapjoin.localtask.max.memory.usage=0.7
-hiveconf hive.mapjoin.smalltable.filesize=25000000
-hiveconf hive.resultset.use.unique.column.names=false
-hiveconf hive.auto.convert.join=false
-hiveconf hive.optimize.sort.dynamic.partition=false
-hiveconf mapreduce.job.reduces=-1
-hiveconf hive.vectorized.execution.enabled=false
-hiveconf mapreduce.job.ubertask.enable=true
-hiveconf yarn.app.mapreduce.am.resource.mb=2048

32. env HADOOP_CLASSPATH=test.jar:td-hadoop-1.0.jar
HADOOP_OPTS="-Xmx738m -Duser.name=221"
hive --service jar td-hadoop-1.0.jar
com.treasure_data.hadoop.hive.runner.QueryRunner
-hiveconf td.jar.version=
-hiveconf plazma.metadb.config={}
-hiveconf plazma.storage.config={}
-hiveconf td.worker.database.config={}
-hiveconf mapreduce.job.priority=HIGH
-hiveconf mapreduce.job.queuename=root.q221.high
-hiveconf mapreduce.job.name=HiveJob379515
-hiveconf td.query.mergeThreshold=1333382400
-hiveconf td.query.apikey=12345
-hiveconf td.scheduled.time=1342449253
-hiveconf td.outdir=./jobs/379515
-hiveconf hive.metastore.warehouse.dir=/user/hive/221/warehouse
-hiveconf hive.auto.convert.join.noconditionaltask=false
-hiveconf hive.mapjoin.localtask.max.memory.usage=0.7
-hiveconf hive.mapjoin.smalltable.filesize=25000000
-hiveconf hive.resultset.use.unique.column.names=false
-hiveconf hive.auto.convert.join=false
-hiveconf hive.optimize.sort.dynamic.partition=false
-hiveconf mapreduce.job.reduces=-1
-hiveconf hive.vectorized.execution.enabled=false
-hiveconf mapreduce.job.ubertask.enable=true
-hiveconf yarn.app.mapreduce.am.resource.mb=2048
-hiveconf mapreduce.job.ubertask.maxmaps=1
-hiveconf mapreduce.job.ubertask.maxreduces=1
-hiveconf mapreduce.job.ubertask.maxbytes=536870912
-hiveconf td.hive.insertInto.dynamic.partitioning=false
-outdir ./jobs/379515

33. Example: Hive job (cont)
ADD JAR 'td-hadoop-1.0.jar';
CREATE DATABASE IF NOT EXISTS `db`;
USE `db`;
CREATE TABLE tagomoris (`v` MAP<STRING,STRING>, `time` INT)
STORED BY 'com.treasure_data.hadoop.hive.mapred.TDStorageHandler'
WITH SERDEPROPERTIES ('msgpack.columns.mapping'='*,time')
TBLPROPERTIES (
'td.storage.user'='221',
'td.storage.database'='dfc',
'td.storage.table'='users_20100604_080812_ce9203d0',
'td.storage.path'='221/dfc/users_20100604_080812_ce9203d0',
'td.table_id'='2',
'td.modifiable'='true',
'plazma.data_set.name'='221/dfc/users_20100604_080812_ce9203d0'
);
CREATE TABLE tbl1 (
`uid` INT,
`key` STRING,
`time` INT
)
STORED BY 'com.treasure_data.hadoop.hive.mapred.TDStorageHandler'
WITH SERDEPROPERTIES ('msgpack.columns.mapping'='uid,key,time')
TBLPROPERTIES (
'td.storage.user'='221',
'td.storage.database'='dfc',

34. ADD JAR 'td-hadoop-1.0.jar';
CREATE DATABASE IF NOT EXISTS `db`;
USE `db`;
CREATE TABLE tagomoris (`v` MAP<STRING,STRING>, `time` INT)
STORED BY 'com.treasure_data.hadoop.hive.mapred.TDStorageHandler'
WITH SERDEPROPERTIES ('msgpack.columns.mapping'='*,time')
TBLPROPERTIES (
'td.storage.user'='221',
'td.storage.database'='dfc',
'td.storage.table'='users_20100604_080812_ce9203d0',
'td.storage.path'='221/dfc/users_20100604_080812_ce9203d0',
'td.table_id'='2',
'td.modifiable'='true',
'plazma.data_set.name'='221/dfc/users_20100604_080812_ce9203d0'
);
CREATE TABLE tbl1 (
`uid` INT,
`key` STRING,
`time` INT
)
STORED BY 'com.treasure_data.hadoop.hive.mapred.TDStorageHandler'
WITH SERDEPROPERTIES ('msgpack.columns.mapping'='uid,key,time')
TBLPROPERTIES (
'td.storage.user'='221',
'td.storage.database'='dfc',
'td.storage.table'='contests_20100606_120720_96abe81a',
'td.storage.path'='221/dfc/contests_20100606_120720_96abe81a',
'td.table_id'='4',
'td.modifiable'='true',
'plazma.data_set.name'='221/dfc/contests_20100606_120720_96abe81a'
);
USE `db`;

35. USE `db`;
CREATE TEMPORARY FUNCTION MSGPACK_SERIALIZE AS
'com.treasure_data.hadoop.hive.udf.MessagePackSerialize';
CREATE TEMPORARY FUNCTION TD_TIME_RANGE AS
'com.treasure_data.hadoop.hive.udf.GenericUDFTimeRange';
CREATE TEMPORARY FUNCTION TD_TIME_ADD AS
'com.treasure_data.hadoop.hive.udf.UDFTimeAdd';
CREATE TEMPORARY FUNCTION TD_TIME_FORMAT AS
'com.treasure_data.hadoop.hive.udf.UDFTimeFormat';
CREATE TEMPORARY FUNCTION TD_TIME_PARSE AS
'com.treasure_data.hadoop.hive.udf.UDFTimeParse';
CREATE TEMPORARY FUNCTION TD_SCHEDULED_TIME AS
'com.treasure_data.hadoop.hive.udf.GenericUDFScheduledTime';
CREATE TEMPORARY FUNCTION TD_X_RANK AS
'com.treasure_data.hadoop.hive.udf.Rank';
CREATE TEMPORARY FUNCTION TD_FIRST AS
'com.treasure_data.hadoop.hive.udf.GenericUDAFFirst';
CREATE TEMPORARY FUNCTION TD_LAST AS
'com.treasure_data.hadoop.hive.udf.GenericUDAFLast';
CREATE TEMPORARY FUNCTION TD_SESSIONIZE AS
'com.treasure_data.hadoop.hive.udf.UDFSessionize';
CREATE TEMPORARY FUNCTION TD_PARSE_USER_AGENT AS
'com.treasure_data.hadoop.hive.udf.GenericUDFParseUserAgent';
CREATE TEMPORARY FUNCTION TD_HEX2NUM AS
'com.treasure_data.hadoop.hive.udf.UDFHex2num';
CREATE TEMPORARY FUNCTION TD_MD5 AS
'com.treasure_data.hadoop.hive.udf.UDFmd5';
CREATE TEMPORARY FUNCTION TD_RANK_SEQUENCE AS
'com.treasure_data.hadoop.hive.udf.UDFRankSequence';
CREATE TEMPORARY FUNCTION TD_STRING_EXPLODER AS
'com.treasure_data.hadoop.hive.udf.GenericUDTFStringExploder';
CREATE TEMPORARY FUNCTION TD_URL_DECODE AS

36. CREATE TEMPORARY FUNCTION TD_URL_DECODE AS
'com.treasure_data.hadoop.hive.udf.UDFUrlDecode';
CREATE TEMPORARY FUNCTION TD_DATE_TRUNC AS
'com.treasure_data.hadoop.hive.udf.UDFDateTrunc';
CREATE TEMPORARY FUNCTION TD_LAT_LONG_TO_COUNTRY AS
'com.treasure_data.hadoop.hive.udf.UDFLatLongToCountry';
CREATE TEMPORARY FUNCTION TD_SUBSTRING_INENCODING AS
'com.treasure_data.hadoop.hive.udf.GenericUDFSubstringInEncoding';
CREATE TEMPORARY FUNCTION TD_DIVIDE AS
'com.treasure_data.hadoop.hive.udf.GenericUDFDivide';
CREATE TEMPORARY FUNCTION TD_SUMIF AS
'com.treasure_data.hadoop.hive.udf.GenericUDAFSumIf';
CREATE TEMPORARY FUNCTION TD_AVGIF AS
'com.treasure_data.hadoop.hive.udf.GenericUDAFAvgIf';
CREATE TEMPORARY FUNCTION hivemall_version AS
'hivemall.HivemallVersionUDF';
CREATE TEMPORARY FUNCTION perceptron AS
'hivemall.classifier.PerceptronUDTF';
CREATE TEMPORARY FUNCTION train_perceptron AS
'hivemall.classifier.PerceptronUDTF';
CREATE TEMPORARY FUNCTION train_pa AS
'hivemall.classifier.PassiveAggressiveUDTF';
CREATE TEMPORARY FUNCTION train_pa1 AS
'hivemall.classifier.PassiveAggressiveUDTF';
CREATE TEMPORARY FUNCTION train_pa2 AS
'hivemall.classifier.PassiveAggressiveUDTF';
CREATE TEMPORARY FUNCTION train_cw AS
'hivemall.classifier.ConfidenceWeightedUDTF';
CREATE TEMPORARY FUNCTION train_arow AS
'hivemall.classifier.AROWClassifierUDTF';
CREATE TEMPORARY FUNCTION train_arowh AS
'hivemall.classifier.AROWClassifierUDTF';

37. CREATE TEMPORARY FUNCTION train_arowh AS
'hivemall.classifier.AROWClassifierUDTF';
CREATE TEMPORARY FUNCTION train_scw AS
'hivemall.classifier.SoftConfideceWeightedUDTF';
CREATE TEMPORARY FUNCTION train_scw2 AS
'hivemall.classifier.SoftConfideceWeightedUDTF';
CREATE TEMPORARY FUNCTION adagrad_rda AS
'hivemall.classifier.AdaGradRDAUDTF';
CREATE TEMPORARY FUNCTION train_adagrad_rda AS
'hivemall.classifier.AdaGradRDAUDTF';
CREATE TEMPORARY FUNCTION train_multiclass_perceptron AS
'hivemall.classifier.multiclass.MulticlassPerceptronUDTF';
CREATE TEMPORARY FUNCTION train_multiclass_pa AS
'hivemall.classifier.multiclass.MulticlassPassiveAggressiveUDTF';
CREATE TEMPORARY FUNCTION train_multiclass_pa1 AS
'hivemall.classifier.multiclass.MulticlassPassiveAggressiveUDTF';
CREATE TEMPORARY FUNCTION train_multiclass_pa2 AS
'hivemall.classifier.multiclass.MulticlassPassiveAggressiveUDTF';
CREATE TEMPORARY FUNCTION train_multiclass_cw AS
'hivemall.classifier.multiclass.MulticlassConfidenceWeightedUDTF';
CREATE TEMPORARY FUNCTION train_multiclass_arow AS
'hivemall.classifier.multiclass.MulticlassAROWClassifierUDTF';
CREATE TEMPORARY FUNCTION train_multiclass_scw AS
'hivemall.classifier.multiclass.MulticlassSoftConfidenceWeightedUDTF';
CREATE TEMPORARY FUNCTION train_multiclass_scw2 AS
'hivemall.classifier.multiclass.MulticlassSoftConfidenceWeightedUDTF';
CREATE TEMPORARY FUNCTION cosine_similarity AS
'hivemall.knn.similarity.CosineSimilarityUDF';
CREATE TEMPORARY FUNCTION cosine_sim AS
'hivemall.knn.similarity.CosineSimilarityUDF';
CREATE TEMPORARY FUNCTION jaccard AS
'hivemall.knn.similarity.JaccardIndexUDF';

38. CREATE TEMPORARY FUNCTION jaccard AS
'hivemall.knn.similarity.JaccardIndexUDF';
CREATE TEMPORARY FUNCTION jaccard_similarity AS
'hivemall.knn.similarity.JaccardIndexUDF';
CREATE TEMPORARY FUNCTION angular_similarity AS
'hivemall.knn.similarity.AngularSimilarityUDF';
CREATE TEMPORARY FUNCTION euclid_similarity AS
'hivemall.knn.similarity.EuclidSimilarity';
CREATE TEMPORARY FUNCTION distance2similarity AS
'hivemall.knn.similarity.Distance2SimilarityUDF';
CREATE TEMPORARY FUNCTION hamming_distance AS
'hivemall.knn.distance.HammingDistanceUDF';
CREATE TEMPORARY FUNCTION popcnt AS 'hivemall.knn.distance.PopcountUDF';
CREATE TEMPORARY FUNCTION kld AS 'hivemall.knn.distance.KLDivergenceUDF';
CREATE TEMPORARY FUNCTION euclid_distance AS
'hivemall.knn.distance.EuclidDistanceUDF';
CREATE TEMPORARY FUNCTION cosine_distance AS
'hivemall.knn.distance.CosineDistanceUDF';
CREATE TEMPORARY FUNCTION angular_distance AS
'hivemall.knn.distance.AngularDistanceUDF';
CREATE TEMPORARY FUNCTION jaccard_distance AS
'hivemall.knn.distance.JaccardDistanceUDF';
CREATE TEMPORARY FUNCTION manhattan_distance AS
'hivemall.knn.distance.ManhattanDistanceUDF';
CREATE TEMPORARY FUNCTION minkowski_distance AS
'hivemall.knn.distance.MinkowskiDistanceUDF';
CREATE TEMPORARY FUNCTION minhashes AS 'hivemall.knn.lsh.MinHashesUDF';
CREATE TEMPORARY FUNCTION minhash AS 'hivemall.knn.lsh.MinHashUDTF';
CREATE TEMPORARY FUNCTION bbit_minhash AS
'hivemall.knn.lsh.bBitMinHashUDF';
CREATE TEMPORARY FUNCTION voted_avg AS
'hivemall.ensemble.bagging.VotedAvgUDAF';

39. CREATE TEMPORARY FUNCTION voted_avg AS
'hivemall.ensemble.bagging.VotedAvgUDAF';
CREATE TEMPORARY FUNCTION weight_voted_avg AS
'hivemall.ensemble.bagging.WeightVotedAvgUDAF';
CREATE TEMPORARY FUNCTION wvoted_avg AS
'hivemall.ensemble.bagging.WeightVotedAvgUDAF';
CREATE TEMPORARY FUNCTION max_label AS
'hivemall.ensemble.MaxValueLabelUDAF';
CREATE TEMPORARY FUNCTION maxrow AS 'hivemall.ensemble.MaxRowUDAF';
CREATE TEMPORARY FUNCTION argmin_kld AS
'hivemall.ensemble.ArgminKLDistanceUDAF';
CREATE TEMPORARY FUNCTION mhash AS
'hivemall.ftvec.hashing.MurmurHash3UDF';
CREATE TEMPORARY FUNCTION sha1 AS 'hivemall.ftvec.hashing.Sha1UDF';
CREATE TEMPORARY FUNCTION array_hash_values AS
'hivemall.ftvec.hashing.ArrayHashValuesUDF';
CREATE TEMPORARY FUNCTION prefixed_hash_values AS
'hivemall.ftvec.hashing.ArrayPrefixedHashValuesUDF';
CREATE TEMPORARY FUNCTION polynomial_features AS
'hivemall.ftvec.pairing.PolynomialFeaturesUDF';
CREATE TEMPORARY FUNCTION powered_features AS
'hivemall.ftvec.pairing.PoweredFeaturesUDF';
CREATE TEMPORARY FUNCTION rescale AS 'hivemall.ftvec.scaling.RescaleUDF';
CREATE TEMPORARY FUNCTION rescale_fv AS
'hivemall.ftvec.scaling.RescaleUDF';
CREATE TEMPORARY FUNCTION zscore AS 'hivemall.ftvec.scaling.ZScoreUDF';
CREATE TEMPORARY FUNCTION normalize AS
'hivemall.ftvec.scaling.L2NormalizationUDF';
CREATE TEMPORARY FUNCTION conv2dense AS
'hivemall.ftvec.conv.ConvertToDenseModelUDAF';
CREATE TEMPORARY FUNCTION to_dense_features AS
'hivemall.ftvec.conv.ToDenseFeaturesUDF';

40. CREATE TEMPORARY FUNCTION to_dense_features AS
'hivemall.ftvec.conv.ToDenseFeaturesUDF';
CREATE TEMPORARY FUNCTION to_dense AS
'hivemall.ftvec.conv.ToDenseFeaturesUDF';
CREATE TEMPORARY FUNCTION to_sparse_features AS
'hivemall.ftvec.conv.ToSparseFeaturesUDF';
CREATE TEMPORARY FUNCTION to_sparse AS
'hivemall.ftvec.conv.ToSparseFeaturesUDF';
CREATE TEMPORARY FUNCTION quantify AS
'hivemall.ftvec.conv.QuantifyColumnsUDTF';
CREATE TEMPORARY FUNCTION vectorize_features AS
'hivemall.ftvec.trans.VectorizeFeaturesUDF';
CREATE TEMPORARY FUNCTION categorical_features AS
'hivemall.ftvec.trans.CategoricalFeaturesUDF';
CREATE TEMPORARY FUNCTION indexed_features AS
'hivemall.ftvec.trans.IndexedFeatures';
CREATE TEMPORARY FUNCTION quantified_features AS
'hivemall.ftvec.trans.QuantifiedFeaturesUDTF';
CREATE TEMPORARY FUNCTION quantitative_features AS
'hivemall.ftvec.trans.QuantitativeFeaturesUDF';
CREATE TEMPORARY FUNCTION amplify AS
'hivemall.ftvec.amplify.AmplifierUDTF';
CREATE TEMPORARY FUNCTION rand_amplify AS
'hivemall.ftvec.amplify.RandomAmplifierUDTF';
CREATE TEMPORARY FUNCTION addBias AS 'hivemall.ftvec.AddBiasUDF';
CREATE TEMPORARY FUNCTION add_bias AS 'hivemall.ftvec.AddBiasUDF';
CREATE TEMPORARY FUNCTION sortByFeature AS
'hivemall.ftvec.SortByFeatureUDF';
CREATE TEMPORARY FUNCTION sort_by_feature AS
'hivemall.ftvec.SortByFeatureUDF';
CREATE TEMPORARY FUNCTION extract_feature AS
'hivemall.ftvec.ExtractFeatureUDF';

41. CREATE TEMPORARY FUNCTION extract_feature AS
'hivemall.ftvec.ExtractFeatureUDF';
CREATE TEMPORARY FUNCTION extract_weight AS
'hivemall.ftvec.ExtractWeightUDF';
CREATE TEMPORARY FUNCTION add_feature_index AS
'hivemall.ftvec.AddFeatureIndexUDF';
CREATE TEMPORARY FUNCTION feature AS 'hivemall.ftvec.FeatureUDF';
CREATE TEMPORARY FUNCTION feature_index AS
'hivemall.ftvec.FeatureIndexUDF';
CREATE TEMPORARY FUNCTION tf AS 'hivemall.ftvec.text.TermFrequencyUDAF';
CREATE TEMPORARY FUNCTION train_logregr AS
'hivemall.regression.LogressUDTF';
CREATE TEMPORARY FUNCTION train_pa1_regr AS
'hivemall.regression.PassiveAggressiveRegressionUDTF';
CREATE TEMPORARY FUNCTION train_pa1a_regr AS
'hivemall.regression.PassiveAggressiveRegressionUDTF';
CREATE TEMPORARY FUNCTION train_pa2_regr AS
'hivemall.regression.PassiveAggressiveRegressionUDTF';
CREATE TEMPORARY FUNCTION train_pa2a_regr AS
'hivemall.regression.PassiveAggressiveRegressionUDTF';
CREATE TEMPORARY FUNCTION train_arow_regr AS
'hivemall.regression.AROWRegressionUDTF';
CREATE TEMPORARY FUNCTION train_arowe_regr AS
'hivemall.regression.AROWRegressionUDTF';
CREATE TEMPORARY FUNCTION train_arowe2_regr AS
'hivemall.regression.AROWRegressionUDTF';
CREATE TEMPORARY FUNCTION train_adagrad_regr AS
'hivemall.regression.AdaGradUDTF';
CREATE TEMPORARY FUNCTION train_adadelta_regr AS
'hivemall.regression.AdaDeltaUDTF';
CREATE TEMPORARY FUNCTION train_adagrad AS
'hivemall.regression.AdaGradUDTF';

42. CREATE TEMPORARY FUNCTION train_adagrad AS
'hivemall.regression.AdaGradUDTF';
CREATE TEMPORARY FUNCTION train_adadelta AS
'hivemall.regression.AdaDeltaUDTF';
CREATE TEMPORARY FUNCTION logress AS 'hivemall.regression.LogressUDTF';
CREATE TEMPORARY FUNCTION pa1_regress AS
'hivemall.regression.PassiveAggressiveRegressionUDTF';
CREATE TEMPORARY FUNCTION pa1a_regress AS
'hivemall.regression.PassiveAggressiveRegressionUDTF';
CREATE TEMPORARY FUNCTION pa2_regress AS
'hivemall.regression.PassiveAggressiveRegressionUDTF';
CREATE TEMPORARY FUNCTION pa2a_regress AS
'hivemall.regression.PassiveAggressiveRegressionUDTF';
CREATE TEMPORARY FUNCTION arow_regress AS
'hivemall.regression.AROWRegressionUDTF';
CREATE TEMPORARY FUNCTION arowe_regress AS
'hivemall.regression.AROWRegressionUDTF';
CREATE TEMPORARY FUNCTION arowe2_regress AS
'hivemall.regression.AROWRegressionUDTF';
CREATE TEMPORARY FUNCTION adagrad AS 'hivemall.regression.AdaGradUDTF';
CREATE TEMPORARY FUNCTION adadelta AS 'hivemall.regression.AdaDeltaUDTF';
CREATE TEMPORARY FUNCTION float_array AS
'hivemall.tools.array.AllocFloatArrayUDF';
CREATE TEMPORARY FUNCTION array_remove AS
'hivemall.tools.array.ArrayRemoveUDF';
CREATE TEMPORARY FUNCTION sort_and_uniq_array AS
'hivemall.tools.array.SortAndUniqArrayUDF';
CREATE TEMPORARY FUNCTION subarray_endwith AS
'hivemall.tools.array.SubarrayEndWithUDF';
CREATE TEMPORARY FUNCTION subarray_startwith AS
'hivemall.tools.array.SubarrayStartWithUDF';
CREATE TEMPORARY FUNCTION collect_all AS

43. CREATE TEMPORARY FUNCTION collect_all AS
'hivemall.tools.array.CollectAllUDAF';
CREATE TEMPORARY FUNCTION concat_array AS
'hivemall.tools.array.ConcatArrayUDF';
CREATE TEMPORARY FUNCTION subarray AS 'hivemall.tools.array.SubarrayUDF';
CREATE TEMPORARY FUNCTION array_avg AS
'hivemall.tools.array.ArrayAvgGenericUDAF';
CREATE TEMPORARY FUNCTION array_sum AS
'hivemall.tools.array.ArraySumUDAF';
CREATE TEMPORARY FUNCTION to_string_array AS
'hivemall.tools.array.ToStringArrayUDF';
CREATE TEMPORARY FUNCTION map_get_sum AS
'hivemall.tools.map.MapGetSumUDF';
CREATE TEMPORARY FUNCTION map_tail_n AS 'hivemall.tools.map.MapTailNUDF';
CREATE TEMPORARY FUNCTION to_map AS 'hivemall.tools.map.UDAFToMap';
CREATE TEMPORARY FUNCTION to_ordered_map AS
'hivemall.tools.map.UDAFToOrderedMap';
CREATE TEMPORARY FUNCTION sigmoid AS
'hivemall.tools.math.SigmoidGenericUDF';
CREATE TEMPORARY FUNCTION taskid AS 'hivemall.tools.mapred.TaskIdUDF';
CREATE TEMPORARY FUNCTION jobid AS 'hivemall.tools.mapred.JobIdUDF';
CREATE TEMPORARY FUNCTION rowid AS 'hivemall.tools.mapred.RowIdUDF';
CREATE TEMPORARY FUNCTION generate_series AS
'hivemall.tools.GenerateSeriesUDTF';
CREATE TEMPORARY FUNCTION convert_label AS
'hivemall.tools.ConvertLabelUDF';
CREATE TEMPORARY FUNCTION x_rank AS 'hivemall.tools.RankSequenceUDF';
CREATE TEMPORARY FUNCTION each_top_k AS 'hivemall.tools.EachTopKUDTF';
CREATE TEMPORARY FUNCTION tokenize AS 'hivemall.tools.text.TokenizeUDF';
CREATE TEMPORARY FUNCTION is_stopword AS
'hivemall.tools.text.StopwordUDF';
CREATE TEMPORARY FUNCTION split_words AS

44. CREATE TEMPORARY FUNCTION split_words AS
'hivemall.tools.text.SplitWordsUDF';
CREATE TEMPORARY FUNCTION normalize_unicode AS
'hivemall.tools.text.NormalizeUnicodeUDF';
CREATE TEMPORARY FUNCTION lr_datagen AS
'hivemall.dataset.LogisticRegressionDataGeneratorUDTF';
CREATE TEMPORARY FUNCTION f1score AS 'hivemall.evaluation.FMeasureUDAF';
CREATE TEMPORARY FUNCTION mae AS
'hivemall.evaluation.MeanAbsoluteErrorUDAF';
CREATE TEMPORARY FUNCTION mse AS
'hivemall.evaluation.MeanSquaredErrorUDAF';
CREATE TEMPORARY FUNCTION rmse AS
'hivemall.evaluation.RootMeanSquaredErrorUDAF';
CREATE TEMPORARY FUNCTION mf_predict AS 'hivemall.mf.MFPredictionUDF';
CREATE TEMPORARY FUNCTION train_mf_sgd AS
'hivemall.mf.MatrixFactorizationSGDUDTF';
CREATE TEMPORARY FUNCTION train_mf_adagrad AS
'hivemall.mf.MatrixFactorizationAdaGradUDTF';
CREATE TEMPORARY FUNCTION fm_predict AS
'hivemall.fm.FMPredictGenericUDAF';
CREATE TEMPORARY FUNCTION train_fm AS
'hivemall.fm.FactorizationMachineUDTF';
CREATE TEMPORARY FUNCTION train_randomforest_classifier AS
'hivemall.smile.classification.RandomForestClassifierUDTF';
CREATE TEMPORARY FUNCTION train_rf_classifier AS
'hivemall.smile.classification.RandomForestClassifierUDTF';
CREATE TEMPORARY FUNCTION train_randomforest_regr AS
'hivemall.smile.regression.RandomForestRegressionUDTF';
CREATE TEMPORARY FUNCTION train_rf_regr AS
'hivemall.smile.regression.RandomForestRegressionUDTF';
CREATE TEMPORARY FUNCTION tree_predict AS
'hivemall.smile.tools.TreePredictByStackMachineUDF';

45. CREATE TEMPORARY FUNCTION tree_predict AS
'hivemall.smile.tools.TreePredictByStackMachineUDF';
CREATE TEMPORARY FUNCTION vm_tree_predict AS
'hivemall.smile.tools.TreePredictByStackMachineUDF';
CREATE TEMPORARY FUNCTION rf_ensemble AS
'hivemall.smile.tools.RandomForestEnsembleUDAF';
CREATE TEMPORARY FUNCTION train_gradient_boosting_classifier AS
'hivemall.smile.classification.GradientTreeBoostingClassifierUDTF';
CREATE TEMPORARY FUNCTION guess_attribute_types AS
'hivemall.smile.tools.GuessAttributesUDF';
CREATE TEMPORARY FUNCTION tokenize_ja AS
'hivemall.nlp.tokenizer.KuromojiUDF';
CREATE TEMPORARY MACRO max2(x DOUBLE, y DOUBLE) if(x>y,x,y);
CREATE TEMPORARY MACRO min2(x DOUBLE, y DOUBLE) if(x<y,x,y);
CREATE TEMPORARY MACRO rand_gid(k INT) floor(rand()*k);
CREATE TEMPORARY MACRO rand_gid2(k INT, seed INT) floor(rand(seed)*k);
CREATE TEMPORARY MACRO idf(df_t DOUBLE, n_docs DOUBLE) log(10, n_docs /
max2(1,df_t)) + 1.0;
CREATE TEMPORARY MACRO tfidf(tf FLOAT, df_t DOUBLE, n_docs DOUBLE) tf *
(log(10, n_docs / max2(1,df_t)) + 1.0);
SELECT time, COUNT(1) AS cnt FROM tbl1
WHERE TD_TIME_RANGE(time, '2015-12-11', '2015-12-12', 'JST');

46. Do you still love
Java / SQL ?

47. PQ written in Ruby
• Building jobs/parameters is so complex!
• using data from many configurations (YAML, JSON),
internal APIs and RDBMSs
• with many ext syntaxes/rules to tune performance,
override configurations for tests, ...
• Ruby empower to write fat/complex worker code
• Testing!
• Unit tests using Rspec
• System tests (executing real queries/jobs) using
Rspec

48. For Further improvement
about workers
• More performance for more customers and less
costs
• More scalability for many other kind jobs
• Better and well-controlled tests (indented here
documents!)

49. "Done is better than Perfect."

50. PerfectQueue
DoneQueue
?

51. We'll improve our code step by step,
with improvements of ruby and developer
community <3
Thanks!