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Efficient and Invincible
Big Data Platform in LINE
Neil Tu (杜佐民)
● Data architect and engineer
● Expert on Hadoop distributed system and its ecosystems
● 5+ years of experience in image processing, computer vision,
and pattern recognition
About Me
Agenda • Data Platforms Within LINE
• Pipeline Platform
• Analysis Platform
• Ecosystem
Data Platforms Within LINE
Data Platforms
Data Platforms Big Data
Data Analysis
Mathematic Modeling
Pipeline
Machine Learning
Deep Learning
Etc.
Protocolized Model
System Integrated
Streaming
Tracking Service

Platform
Pipeline

Platform
Analysis

Platform
Data Platforms Within LINE
Pipeline Platform
Types
30
PB
6.5M
msg/sec
652
Service

System
ETL
Protocol definition
Data flow definition
Protocolized Data Model
message ApiAccessLog {
string request_id = 1;
string method = 2;
string path = 3;
string request_ip = 4 [(EsMapping.type) = "ip"];
int32 status = 5;
string contents = 6 [(EsMapping.index) = false];
string result = 7;
int64 event_time = 8 [(use_as_timestamp) = true];
int64 injest_time = 9;
}
Analysis Platform
Analysis Platform
Tables
25
PB
550
Users
1668
Data Infrastructure
BI tool
Event

log
RDBMS

dump
Other

storages
Data hub
Data Flow
RDBMS
ETL
Service data
Other
storages
Real-time Query
180,000 data / sec
● UI
● Security
● Local backup
Nifi
Ecosystem
etc.
Oasis
https://github.com/yanagishima/yanagishima
Yanagishima
LINE Analytics
Reporting Tool
Interactive Data Analytics Tool
Oasis
Data Catalog Tool
Aquarium
Data Catalog Tool
Aquarium
Aquarium
Data Catalog Tool
Security
Office authentication Private authentication and authorization
Gateway server Client server
Sign-up web UI
HDFS

user home
directory
Registration
WF
Monitoring
● JVM
● Net traffic
● Disk capacity
● etc.
Basic Monitoring
● Small files
● Cluster usage per user
● Disk usage
● Blocks
● Empty files
● etc.
Cluster Monitoring
Third Namenode
NN1 NN2 NN3
JN1 JN2 JN3
Always on standby
Real-time metadata
Tuning
YARN
● yarn.log-aggregation.retain-check-interval-seconds=86400
● yarn.log-aggregation.retain-seconds=172800
Basic Tuning
Spark
● spark.history.fs.cleaner.enabled=true
● spark.history.fs.cleaner.interval=1d
● spark.history.fs.cleaner.maxAge=2d
Hive
● hive.merge.mapredfiles=true
● hive.merge.smallfiles.avgsize=128000000
● mapreduce.input.fileinputformat.split.maxsize=2147483648
● mapreduce.input.fileinputformat.split.minsize=134217728
● mapreduce.input.fileinputformat.split.minsize.per.node=134217728
● mapreduce.input.fileinputformat.split.minsize.per.rack=134217728
hive> ALTER TABLE xxx PARTITION (dt='19840312') CONCATENATE;
Basic Tuning
Conclusion
What is required?
● Be patient
How to achieve results?
● Trial and error
● Never give up
Running a Platform
THANK YOU

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