Treasure Data Tech Talk 2015-03-11, 2015-03-12

1. Sadayuki Furuhashi
Founder & Software Architect
Treasure Data, inc.
PlazmaTreasure Data’s distributed analytical database
growing 40,000,000,000 records/day.

2. Plazma - Treasure Data’s distributed
analytical database

3. Plazma by the numbers
> Data importing
> 450,000 records/sec
≒ 40 billion records/day
> Query processing using Hive
> 2 trillion records/day
> 2,828 TB/day

4. Today’s talk
1. Data importing
> Realtime Storage & Archive Storage
> Deduplication
2. Data processing
> Column-oriented IO
> Schmema-on-read
> Schema auto detection
3. Transaction & Metadata
> Schema auto detection
> INSERT INTO

5. 1. Data Importing

6. Import
Queue
td-agent
/ fluentd
Import
Worker
✓ Buffering for
1 minute
✓ Retrying
(at-least once)
✓ On-disk buffering
on failure
✓ Unique ID for
each chunk
API
Server
It’s like JSON.
but fast and small.
unique_id=375828ce5510cadb
{“time”:1426047906,”uid”:1,…}
{“time”:1426047912,”uid”:9,…}
{“time”:1426047939,”uid”:3,…}
{“time”:1426047951,”uid”:2,…}
…
MySQL
(PerfectQueue)

7. Import
Queue
td-agent
/ fluentd
Import
Worker
✓ Buffering for
1 minute
✓ Retrying
(at-least once)
✓ On-disk buffering
on failure
✓ Unique ID for
each chunk
API
Server
It’s like JSON.
but fast and small.
MySQL
(PerfectQueue)
unique_id time
375828ce5510cadb 2015-12-01 10:47
2024cffb9510cadc 2015-12-01 11:09
1b8d6a600510cadd 2015-12-01 11:21
1f06c0aa510caddb 2015-12-01 11:38

8. Import
Queue
td-agent
/ fluentd
Import
Worker
✓ Buffering for
1 minute
✓ Retrying
(at-least once)
✓ On-disk buffering
on failure
✓ Unique ID for
each chunk
API
Server
It’s like JSON.
but fast and small.
MySQL
(PerfectQueue)
unique_id time
375828ce5510cadb 2015-12-01 10:47
2024cffb9510cadc 2015-12-01 11:09
1b8d6a600510cadd 2015-12-01 11:21
1f06c0aa510caddb 2015-12-01 11:38UNIQUE
(at-most once)

9. Import
Queue
Import
Worker
Import
Worker
Import
Worker
✓ HA
✓ Load balancing

10. Realtime
Storage
PostgreSQL
Amazon S3 /
Basho Riak CS
Metadata
Import
Queue
Import
Worker
Import
Worker
Import
Worker
Archive
Storage

11. Realtime
Storage
PostgreSQL
Amazon S3 /
Basho Riak CS
Metadata
Import
Queue
Import
Worker
Import
Worker
Import
Worker
uploaded time file index range records
2015-03-08 10:47
[2015-12-01 10:47:11,
2015-12-01 10:48:13]
3
2015-03-08 11:09
[2015-12-01 11:09:32,
2015-12-01 11:10:35]
25
2015-03-08 11:38
[2015-12-01 11:38:43,
2015-12-01 11:40:49]
14
… … … …
Archive
Storage
Metadata of the
records in a file
(stored on
PostgreSQL)

12. Amazon S3 /
Basho Riak CS
Metadata
Merge Worker
(MapReduce)
uploaded time file index range records
2015-03-08 10:47
[2015-12-01 10:47:11,
2015-12-01 10:48:13]
3
2015-03-08 11:09
[2015-12-01 11:09:32,
2015-12-01 11:10:35]
25
2015-03-08 11:38
[2015-12-01 11:38:43,
2015-12-01 11:40:49]
14
… … … …
file index range records
[2015-12-01 10:00:00,
2015-12-01 11:00:00]
3,312
[2015-12-01 11:00:00,
2015-12-01 12:00:00]
2,143
… … …
Realtime
Storage
Archive
Storage
PostgreSQL
Merge every 1 hourRetrying + Unique
(at-least-once + at-most-once)

13. Amazon S3 /
Basho Riak CS
Metadata
uploaded time file index range records
2015-03-08 10:47
[2015-12-01 10:47:11,
2015-12-01 10:48:13]
3
2015-03-08 11:09
[2015-12-01 11:09:32,
2015-12-01 11:10:35]
25
2015-03-08 11:38
[2015-12-01 11:38:43,
2015-12-01 11:40:49]
14
… … … …
file index range records
[2015-12-01 10:00:00,
2015-12-01 11:00:00]
3,312
[2015-12-01 11:00:00,
2015-12-01 12:00:00]
2,143
… … …
Realtime
Storage
Archive
Storage
PostgreSQL
GiST (R-tree) Index
on“time” column on the files
Read from Archive Storage if merged.
Otherwise, from Realtime Storage

14. Data Importing
> Scalable & Reliable importing
> Fluentd buffers data on a disk
> Import queue deduplicates uploaded chunks
> Workers take the chunks and put to Realtime Storage
> Instant visibility
> Imported data is immediately visible by query engines.
> Background workers merges the files every 1 hour.
> Metadata
> Index is built on PostgreSQL using RANGE type and
GiST index

15. 2. Data processing

16. time code method
2015-12-01 10:02:36 200 GET
2015-12-01 10:22:09 404 GET
2015-12-01 10:36:45 200 GET
2015-12-01 10:49:21 200 POST
… … …
time code method
2015-12-01 11:10:09 200 GET
2015-12-01 11:21:45 200 GET
2015-12-01 11:38:59 200 GET
2015-12-01 11:43:37 200 GET
2015-12-01 11:54:52 “200” GET
… … …
Archive
Storage
Files on Amazon S3 / Basho Riak CS
Metadata on PostgreSQL
path index range records
[2015-12-01 10:00:00,
2015-12-01 11:00:00]
3,312
[2015-12-01 11:00:00,
2015-12-01 12:00:00]
2,143
… … …
MessagePack Columnar
File Format

17. time code method
2015-12-01 10:02:36 200 GET
2015-12-01 10:22:09 404 GET
2015-12-01 10:36:45 200 GET
2015-12-01 10:49:21 200 POST
… … …
time code method
2015-12-01 11:10:09 200 GET
2015-12-01 11:21:45 200 GET
2015-12-01 11:38:59 200 GET
2015-12-01 11:43:37 200 GET
2015-12-01 11:54:52 “200” GET
… … …
Archive
Storage
path index range records
[2015-12-01 10:00:00,
2015-12-01 11:00:00]
3,312
[2015-12-01 11:00:00,
2015-12-01 12:00:00]
2,143
… … …
column-based partitioning
time-based partitioning
Files on Amazon S3 / Basho Riak CS
Metadata on PostgreSQL

18. time code method
2015-12-01 10:02:36 200 GET
2015-12-01 10:22:09 404 GET
2015-12-01 10:36:45 200 GET
2015-12-01 10:49:21 200 POST
… … …
time code method
2015-12-01 11:10:09 200 GET
2015-12-01 11:21:45 200 GET
2015-12-01 11:38:59 200 GET
2015-12-01 11:43:37 200 GET
2015-12-01 11:54:52 “200” GET
… … …
Archive
Storage
path index range records
[2015-12-01 10:00:00,
2015-12-01 11:00:00]
3,312
[2015-12-01 11:00:00,
2015-12-01 12:00:00]
2,143
… … …
column-based partitioning
time-based partitioning
Files on Amazon S3 / Basho Riak CS
Metadata on PostgreSQL
SELECT code, COUNT(1) FROM logs
WHERE time >= 2015-12-01 11:00:00
GROUP BY code

19. time code method
2015-12-01 10:02:36 200 GET
2015-12-01 10:22:09 404 GET
2015-12-01 10:36:45 200 GET
2015-12-01 10:49:21 200 POST
… … …
user time code method
391 2015-12-01 11:10:09 200 GET
482 2015-12-01 11:21:45 200 GET
573 2015-12-01 11:38:59 200 GET
664 2015-12-01 11:43:37 200 GET
755 2015-12-01 11:54:52 “200” GET
… … …
MessagePack Columnar
File Format is schema-less
✓ Instant schema change
SQL is schema-full
✓ SQL doesn’t work
without schema
Schema-on-Read

20. Realtime
Storage
Query Engine
Hive, Pig, Presto
Archive
Storage
Schema-full
Schema-less
Schema
{“user”:54, “name”:”plazma”, “value”:”120”, “host”:”local”}
CREATE TABLE events (
user INT, name STRING, value INT, host INT
);
| user
| 54
| name
| “plazma”
| value
| 120
| host
| NULL
|
|
Schema-on-Read

21. Realtime
Storage
Query Engine
Hive, Pig, Presto
Archive
Storage
Schema-full
Schema-less
Schema
{“user”:54, “name”:”plazma”, “value”:”120”, “host”:”local”}
CREATE TABLE events (
user INT, name STRING, value INT, host INT
);
| user
| 54
| name
| “plazma”
| value
| 120
| host
| NULL
|
|
Schema-on-Read

22. 2. Transaction & Metadata

23. Plazma’s Transaction API
> getOrCreateMetadataTransaction(uniqueName)
> start a named transaction.
> if already started, abort the previous one and restart.
> putOrOverwriteTransactoinPartition(name)
> insert a file to the transaction.
> if the file already exists, overwrite it.
> commitMetadataTransaction(uniqueName)
> make the inserted files visible.
> If the transaction is already committed before, do nothing.

24. Presto
worker
Presto
coordinator
Presto
worker
Example: INSERT INTO impl. to Presto
Metadata
Archive
Storage
Plazma
1. getOrCreateMetadataTransaction
3. commitMetadataTransaction
2. putOrOverwriteTransactoinPartition(name)
Retrying + Unique
(at-least-once + at-most-once)

25. Reducer
Hive
QueryRunner
Reducer
Example: INSERT INTO impl. to Hive
Metadata
Archive
Storage
Plazma
1. getOrCreateMetadataTransaction
3. commitMetadataTransaction
2. putOrOverwriteTransactoinPartition(name)
Retrying + Unique
(at-least-once + at-most-once)

26. Hive
QueryRunner
Example: INSERT INTO impl. to Hive, rewriting query plan
Hive
QueryRunner
Reducer
Reducer
Mapper
Mapper
Reducer
Reducer
Mapper
Mapper
Reducer
Reducer
Mapper
Mapper
Rewrite query plan Partitioning by time
Files are not partitioned by time

27. Why not MySQL? - benchmark
0
45
90
135
180
INSERT 50,000 rows SELECT sum(id) SELECT sum(file_size) WHERE index range
0.656.578.79
168
3.66
17.2
MySQL PostgreSQL
(seconds)
Index-only scan
GiST index +
range type

28. Metadata optimization
> Partitioning & TRUNCATE
> DELETE produces many garbage rows and large WAL
> TRUNCATE doesn’t
> PostgreSQL parameters
> random_page_cost == seq_page_cost
> statement_timeout = 60 sec
> hot_standby_feedback = 1

29. 1. Backend Engineer
2. Support Engineer
3. OSS Engineer
（日本,東京,丸の内）
We’re hiring!