The document is a presentation from the Presto Conference Tokyo 2019 by Kai Sasaki and Taro L. Saito from Arm Treasure Data, highlighting the advancements and architecture of their unified data platform, Treasure Data. It discusses the use of Presto for data processing, the integration of various storage architectures, and techniques for optimizing queries and resource management. Additionally, it covers the development of plugins, extensions, and challenges faced in data optimization and processing at scale.

1. Copyright 1995-2019 Arm Limited (or its affiliates). All rights reserved.
Kai Sasaki, Taro L. Saito
Arm Treasure Data
June 11th, 2019
Presto Conference Tokyo 2019
Presto At Arm Treasure Data
2019 Updates
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2. Copyright 1995-2019 Arm Limited (or its affiliates). All rights reserved.
About Me: Kai Sasaki
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● Kai Sasaki (@Lewuathe)
● https://www.lewuathe.com/
● Software Engineer in Arm
● MPP team to maintain Presto cluster
and around ecosystems
● OSS Contributor
○ Presto, Hadoop, Spark, TensorFlow

3. Copyright 1995-2018 Arm Limited (or its affiliates). All rights reserved.
About Us

4. 400+
Customers
Founded in
2011
Raised
$54M
Security
Acquired by Arm / Softbank
2018
Arm Treasure Data

5. Copyright 1995-2019 Arm Limited (or its affiliates). All rights reserved.
Treasure Data = Unified Data Platform
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6. Copyright 1995-2019 Arm Limited (or its affiliates). All rights reserved.
The Architecture of Treasure Data
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DataLogs
Device
Data
Batch
Data
PlazmaDB
Table Schema
Data Collection Cloud Storage Distributed Data Processing
2 million records / sec. 130 trillion records 1 billion rows processed / sec.
Jobs
Job Management
SQL Editor
Scheduler
Workflows
Machine
Learning
Treasure Data OSS
Third Party OSS

7. Copyright 1995-2018 Arm Limited (or its affiliates). All rights reserved.
How We Use Presto

8. Copyright 1995-2019 Arm Limited (or its affiliates). All rights reserved.
Presto Usage (2019)
● 3x more usage since 2017
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3,500 ~ users
(400+ customer accounts)
600,000~ Queries / Day
100 Trillion Rows Processed / Day
(= 1.2 billion rows processed / sec.)

9. Copyright 1995-2019 Arm Limited (or its affiliates). All rights reserved.
PlazmaDB: MessagePack DBMS
● Fluentd -> MessagePack -> Arm Treasure Data
● Generating table schema from the input MessagePack data
■ No need to worry about changing schema as adding columns or escalating column types are
managed by the service
● Apply schema–on-read for providing table data for Presto/Hive/Spark, etc.
● Storage Format:
● Our internal MessagePack Columnar Format (MPC1) for schema-on-read
Table Schema
Int Column Reader
String Column Reader
Update
Schema
Generate
Reader Set
Table Reader
Schema-free Data
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Data Collection Distributed Data Processing

10. Copyright 1995-2018 Arm Limited (or its affiliates). All rights reserved.
Treasure Data Storage Architecture
● Real-Time vs Archive Storages
● Provide an access to the recent data in real-time storage
● Store optimized partitions into Archive Storage by using MapReduce jobs
(LogMergeJob)
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11. Copyright 1995-2018 Arm Limited (or its affiliates). All rights reserved.
TD_INTERVAL UDF
● Support human-friendly time window support
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12. Copyright 1995-2018 Arm Limited (or its affiliates). All rights reserved.
PlazmaDB Partition Indexes
● Q: How can we get a list of partition files?
● Limitations of the S3 API:
■ LIST operation of S3 files is quite slow
■ No range filtering of S3 files
○ Time range queries are not supported
● PlazmaDB Partition Indexes
● Manages indexes to partition files on S3
● Implemented on top of PostgreSQL
■ SQL + PL/Python functions
● Use GiST indexes (B-tree) to support time range filtering
■ dataset id, (partition start_time, end_time)
● Support transactional partition insertion + deletion for a single table
■ INSERT INTO, DELETE are atomic operations in TD
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13. Copyright 1995-2019 Arm Limited (or its affiliates). All rights reserved.
Extension to Presto
● No major change has been made to Presto master branch
● Fork: https://github.com/treasure-data/presto (almost no diff)
● This is a strategy for catching up with the latest master.
● Adding extension modules in a different internal repository (td-presto)
● td-presto-server
■ Extending presto-server main to inject our own modules
■ Adding a split-resource manager for throttling query resource usage
● td-presto-plugin
■ Metadata Management
○ A bridge to TD API (table metadata API)
○ PlazmaDB: Partition indexes
■ MPC1 file reader
○ S3 I/O request manager for pipelining a lot of S3 GET requests
■ Treasure Data specific UDFs
○ https://support.treasuredata.com/hc/en-us/articles/360001450828-Supported-Presto-and-TD-Functions
● td-presto-stella
■ Partition maintenance module implemented as Presto connectors 13

14. Copyright 1995-2019 Arm Limited (or its affiliates). All rights reserved.
Stella Plugin
● A Presto plugin for maintaining
fragmented partitions
● Too small partitions
● Too large partitions
● Use Presto to merge/split partitions
● Guidelines
■ less than 1M records /
partition
■ 250MB / partition
● Using CTAS statement for merging
partitions:
■ CREATE TABLE stella (account_id =
xxx, database = xxx, table = xxx,
max_file_size=xxx,
max_time_range=xxx)
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15. Copyright 1995-2018 Arm Limited (or its affiliates). All rights reserved.
Ecosystems Around
Presto

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Prestobase: Presto Gateway (api-presto)
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● Prestobase is a proxy gateway to Presto clusters to support standard presto
clients (e.g., presto-cli, jdbc, odbc, etc.)
● Written in Scala

17. Copyright 1995-2019 Arm Limited (or its affiliates). All rights reserved.
td-spark: Apache Spark Driver for Treasure Data
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● td-spark provides a way to use TD table as a datasource of Spark application
● Supporting both read/write mode makes TD extended to further use cases
Python
$ pip install td-pyspark
Or
$ docker pull armtd/td-spark-pyspark
Scala
Add td-spark-assembly.jar in the Spark class path.
https://support.treasuredata.com/hc/en-us/articles/360000716627-Apache-Spark-Driver-td-sp
ark-Release

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Internal Optimization

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Data-Driven System Optimization
● TD is one of the biggest users of TD
● Query logs
● Collecting all Presto query logs since 2015
● Query statements, performance statistics, logs, etc.
● Logs are our valuable assets
● To understand user activities and enable data-driven decisions
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Logs
User
Query
Collect Query Logs
Analyze Query Logs
Machine
Learning
Query
Optimization
Optimize System

20. Copyright 1995-2019 Arm Limited (or its affiliates). All rights reserved.
Checking Query Correctness And Performance
● Upgrading Query Engine Versions
● Need to check customer query compatibilities, performance degradation, etc.
● Testing all 500,000 query / day = 15M queries / month is impractical
● Use ML techniques to effectively reduce the problem size
● Simulate all possible customer query patterns to check the compatibility
● Compute checksums of queries, record performance results to TD
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User
QueryUser
QueryUser
QueryUser
QueryUser
QueryUser
Query
15,000,000
queries
clustering
Query
SigQuery
SigQuery
SigQuery
Sig
minimize
Small
QuerySmall
QuerySmall
QuerySmall
Query
100,000 query
patterns
100,000 small
queries
simulate
queries
simulation
results and
stats

21. Copyright 1995-2019 Arm Limited (or its affiliates). All rights reserved.
Query Metric Analysis
● Resource Usage Prediction
○ Based on the historical metric data
● Further optimization leveraged by prediction result
● Working with Internship Student from UCB
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Presto Resource Manager
● Collecting the system metric in robust manner is challenging
● Unified metric collector of Presto
○ Cluster metric management
○ Query routing optimization
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Challenges

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Challenge: Optimizing Query Workload As A Whole
● 2000 query patterns (5000 queries in a day)
● A real example in our production workload
● How can we improve the entire data processing?
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Detecting Redundant Data Processing
● Redundancy In Queries
○ Same table scans, joins,
aggregations, UDF processing, etc.
● Related work:
○ Selecting Subexpressions to
Materialize At Datacenter Scale
(Microsoft, VLDB 2018)
■ Extract best common sub-
expressions from query graphs
■ Linear programming
● Challenges
● Updating sub-expression caches
■ Cache invalidation
● Combine cached results + query
results for time series data
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26. Copyright 1995-2019 Arm Limited (or its affiliates). All rights reserved.
Challenge: Maximizing Machine Resource Utilization
● Uneven CPU usage due to regional
differences
● US (upper)
■ Global customers
● Tokyo (lower)
■ Only Japan customers
● Semi-scheduled auto-scaling
● Using past stats + runtime
metrics
● Distributing workloads to off-peak
times
● Early data processing
● Optimizing query scheduling
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27. Copyright 1995-2019 Arm Limited (or its affiliates). All rights reserved.
Idea: Using Presto As A Backend of Other Query Engines
● Presto is efficient for table scans, filter, aggregations
● Can’t we use the power of Presto for accelerating other query engines?
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Primary Query EngineSecondary Query Engine

28. Copyright 1995-2019 Arm Limited (or its affiliates). All rights reserved.
Idea: Presto-Presto Connector
● Launch multiple Presto clusters with different configurations
● Presto 1
● Caching common sub-expressions (e.g., Materialized Views or in-memory
storage)
● Presto 2
● Delegate sub-expression processing to the upstream Presto cluster
● Challenge:
● Extracting appropriate sub-queries to run at the upstream Presto
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With Sub-Query CacheReuse Pre-Computed Results

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Missing: Binary Protocol
● Current Presto sends query results in JSON format (v1 protocol)
● Need a faster data transfer method
○ Idea: Using MessagePack for binary data representation
○ Support parallel query results transfer
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v1 JSON
(slow)
Binary Data Transfer

30. Copyright 1995-2019 Arm Limited (or its affiliates). All rights reserved.
Join Arm Treasure Data Team!
● Solving Challenges In The Real World Data Processing
● Building a scalable data processing platform on the cloud
■ Enabled 400+ companies to use Presto for their data processing
● Stream data ingestion systems
■ PlazmaDB indexes, physical storage optimization
● Advanced analytics with Presto, Hive, Spark, and their workflows.
● CDP (Customer Data Platform)
● Building data platform for managing our customers’ customer data
● Supporting non-engineers (e.g., marketers, executives) to manage their own data
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31. Confidential © Arm 2017Confidential © Arm 2017Confidential © Arm 2017
Thank You!
Danke!
Merci!
谢谢!
ありがとう!
Gracias!
Kiitos!
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