Embulk is an open-source tool for bulk loading data between various data sources. It provides parallel execution, data validation, error recovery, and other features. Customers use Embulk to upload various file formats and data sources to Treasure Data. The architecture includes a data connector that submits Embulk jobs to connector workers, which generate Embulk configurations and load data directly into Treasure Data using a private output plugin. To scale to large data loads, an optional MapReduce executor can run Embulk tasks on Hadoop clusters in parallel.

1. Muga Nishizawa (西澤 無我)
Using Embulk at Treasure Data

2. Today’s talk
> What’s Embulk?
> Why our customers use Embulk?
> Embulk
> Data Connector
> Data Connector
> The architecture
> The use case
> with MapReduce Executor
> How we configure MapReduce Executor?
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3. What’s Embulk?
> An open-source parallel bulk data loader
> loads records from “A” to “B”
> using plugins
> for various kinds of “A” and “B”
> to make data integration easy.
> which was very painful…
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Storage, RDBMS,
NoSQL, Cloud Service,
etc.
broken records,
transactions (idempotency),
performance, …

4. HDFS
MySQL
Amazon S3
Embulk
CSV Files
SequenceFile
Salesforce.com
Elasticsearch
Cassandra
Hive
Redis
✓ Parallel execution
✓ Data validation
✓ Error recovery
✓ Deterministic behavior
✓ Resuming
Plugins Plugins
bulk load

5. Why our customers use Embulk?
> Upload various types of their data to TD with Embulk
> Various file formats
> CSV, TSV, JSON, XML,..
> Various data source
> Local disk, RDBMS, SFTP,..
> Various network environments
> embulk-output-td
> https://github.com/treasure-data/embulk-output-td
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6. Out of scope for Embulk
> They develop scripts for
> generating Embulk configs
> changing schema on a regular basis
> logic to select some files but not others
> managing cron settings
> e.g. some users want to upload yesterday’s data
as daily batch
> Embulk is just “bulk loader”
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7. Best practice to manage Embulk!!
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http://www.slideshare.net/GONNakaTaka/embulk5

8. Yes, yes,..
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9. Data Connector
Users/Customers
PlazmaDBConnector Worker
submit
connector jobs
see loaded data
on Console
Guess/Preview API

10. Data Connector
Users/Customers
PlazmaDBConnector Worker
submit
connector jobs
see loaded data
on Console
Guess/Preview API

11. 2 types of hosted Embulk service
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Import
(Data Connector)
Export
(Result Output)
MySQL
PostgreSQL
Redshift
AWS S3
Google Cloud Storage
SalesForce
Marketo
…etc
MySQL
PostgreSQL
Redshift
BigQuery
…etc

12. Guess/Preview API
Users/Customers
PlazmaDB
Connector Worker
submit
connector jobs
see loaded data
on Console
Guess/Preview API

13. Guess/Preview API
> Guesses Embulk config based on sample data
> Creates parser config
> Adds schema, escape char, quote char, etc..
> Creates rename filter config
> TD requires uncapitalized column names
> Preview data before uploading
> Ensures quick response
> Embulk performs this functionality running
on our web application servers
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14. Connector Worker
Users/Customers
PlazmaDB
Connector Worker
submit
connector jobs
see loaded data
on Console
Guess/Preview API

15. Connector Worker
> Generates Embulk config and executes Embulk
> Uses private output plugin instead of embulk-output-td
to upload users’ data to PlazmaDB directly
> Appropriate retry mechanism
> Embulk runs on our Job Queue clients
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16. Timestamp parsing
Users/Customers
PlazmaDB
Connector Worker
submit
connector jobs
see loaded data
on Console
Guess/Preview API

17. Timestamp parsing
> Implement strptime in Java
> Ported from CRuby implementation
> Can precompile the format
> Faster than JRuby’s strptime
> Has been maintained in Embulk repo obscurely..
> It will be merged into JRuby
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18. How we use Data Connector at TD
> a. Monitoring our S3 buckets access
> e.g. “IAM users who accessed our S3 buckets?”
“Access frequency”
> {in: {type: s3}} and {parser: {type: csv}}
> b. Measuring KPIs for development process
> e.g. “phases that we took a long time on the process”
> {in: {type: jira}}
> c. Measuring Business & Support Performance
> {in: {type: Salesforce, Marketo, ZenDesk, …}}
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19. Scaling Embulk
> Requests for massive data loading from users
> e.g. “Upload 150GB data by hourly batch”
“Start PoC and upload 500GB data today”
> Local Executor can not handle this scale
> MapReduce Executor enables us to scale
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20. W/ MapReduce
Users/Customers
PlazmaDB
Connector Worker
submit
connector jobs
see loaded data
on Console
Guess/Preview API
Hadoop Clusters

21. What’s MapReduce Executor?
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Task
Task
Task
Task
Map tasks
Task queue
run tasks on Hadoop

22. MapReduce Executor
with TimestampPartitioning
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Task
Map tasks
Task queue
run tasks on Hadoop
Reduce tasksShuffle

23. built Embulk configs
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exec:
type: mapreduce
job_name: embulk.100000
config_files:
- /etc/hadoop/conf/core-site.xml
- /etc/hadoop/conf/hdfs-site.xml
- /etc/hadoop/conf/mapred-site.xml
config:
fs.defaultFS: “hdfs://my-hdfs.example.net:8020”
yarn.resourcemanager.hostname: "my-yarn.example.net"
dfs.replication: 1
mapreduce.client.submit.file.replication: 1
state_path: /mnt/xxx/embulk/
partitioning:
type: timestamp
unit: hour
column: time
unix_timestamp_unit: hour
map_side_partition_split: 3
reducers: 3
in:
...
Connector Workers (single-machine workers)
are still able to generate config

24. Different sized files
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Map tasks Reduce tasksShuffle

25. Same time range data
25
Map tasks Reduce tasksShuffle

26. Grouping input files
- {in: {min_task_size}}
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Map tasks Reduce tasksShuffle
Task
Task
Task
It also can reduce mapper’s launch cost.

27. One partition into multi-reducers
- {exec: {partitioning: {map_side_split}}}
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Map tasks Reduce tasksShuffle

28. Conclusion
> What’s Embulk?
> Why we use Embulk?
> Embulk
> Data Connector
> Data Connector
> The architecture of Data Connector
> The use case
> with MapReduce Executor
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