筑波大学 集中講義資料 2014/01/31

1. Retrospection / prospection
and schema
TAGOMORI Satoshi (@tagomoris)
LINE Corp.
2014/01/31 (Fri) at University of Tsukuba
the 1st half
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2. TAGOMORI Satoshi (@tagomoris)
LINE Corp.
Development Support Team
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5. Logs
Service metrics (Users, PageViews, ...)
UX/UI metrics (Access path, Taps/views, ...)
Monitoring metrics (Traffic Gbps, TBytes/day, ...)
System monitoring (Error rates, Response time, ...)
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6. Software for Logging
Collection: Fluentd, Scribed, Flume, LogStash, ...
Storage: RDBMS, Hadoop HDFS, NoSQLs, Elasticsearch, ....
Processing: SQL, Hadoop MapReduce(Hive), Presto, Impala, ...
Stream-Processing: Storm, Kafka, Norikra, ...
Visualization: Kibana, Tableau Fnordmetric, GrowthForecast, Focuslight, ...
Appliance: DHW + BI Tools
Services: Google BigQuery, Treasure Data, ...
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7. How inspect logs
Retrospection (reactive search)
Store data, and search
Prospection (proactive search)
Define what should be processed, and store data
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8. What logs inspected
Schema-full data:
strict schema: pre defined fields w/ types (or reject)
schema on read: try to read known fields (or ignore)
Schema-less data:
any fields (or ignore), any types (implicit/explicit
conversion)
fit for services in-development (all internet services!)
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9. How/what
HowWhat
Schema-full
Schema-less
Retrospect
RDBMS,
Hive, BigQuery,
Cassandra, HBase, ...
MongoDB,
Hive(SerDe), TD,
Plain text file, ...
Prospect
Esper,
many of stream CEPs,
...
Norikra, ...
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10. Data size: schema & index
Logs: size is always important (xTB - xPB)
Schema:
size optimization
access optimization on memory/disk
Index:
access optimization on memory/disk
more memory/disk required
hard to distribute
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11. Query response improvements
of retrospection
Schema-full + indexed (RDBMS)
Query plan optimization
Schema on read
I/O and Task size optimization & scale out
Schema-less + indexed (Mongo)
mmap-ed index & data (!)
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12. Query response improvements
of prospection
Time window + incremental calculation
Stream processing engines
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13. Stream processing
and data size
No disks: reduction of failure points
Less memory:
size of just processing and I/O buffers
aggregation results
Easy to distribute:
stream duplication
stream splitting by aggregation key
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14. Stream processing and schema
Stream processing: query -> data
Prospective schema by queries:
Queries know required fields and its types
Unused fields can be ignored
Implicit type conversion available
Schema-less data + schema-full queries
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15. My goal:
Schema-less data stream
+ schema-full queries
It’s Norikra!
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