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Routing Trillions of Events Per Day @Twitter
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Routing Trillions of Events Per Day @Twitter
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○
Clients
Aggregated by Category
Storage HDFS
Http
Clients
Clients
Client Daemon
Client Daemon
Client Daemon
Http Endpoint
Across millions of
clients
Incoming
uncompressed
Collocated with
HDFS datanodes
Event groups by
category
Clients
Local log collection daemon
Clients
Aggregate log events grouped
by Category
Storage (HDFS)
HTTP
Remote
Clients
Log
Processor
Storage (HDFS)
Storage (HDFS)
Log
ReplicatorStorage (HDFS)
Inside
DataCenter
Storage
(Streaming)
Clients
Local log collection daemon
Clients
Aggregate log events grouped
by Category
Storage (HDFS)
HTTP
Remote
Clients
Log
Processor
Storage (HDFS)
Storage (HDFS)
Log
ReplicatorStorage (HDFS)
Inside
DataCenter
Storage
(Streaming)
Clients
Local log collection daemon
Clients
Aggregate log events grouped
by Category
Storage (HDFS)
HTTP
Remote
Clients
Log
Processor
Storage (HDFS)
Storage (HDFS)
Log
ReplicatorStorage (HDFS)
Inside
DataCenter
Storage
(Streaming)
Clients
Local log collection daemon
Clients
Aggregate log events grouped
by Category
Storage (HDFS)
HTTP
Remote
Clients
Log
Processor
Storage (HDFS)
Storage (HDFS)
Log
ReplicatorStorage (HDFS)
Inside
DataCenter
Storage
(Streaming)
Clients
Local log collection daemon
Clients
Aggregate log events grouped
by Category
Storage (HDFS)
HTTP
Remote
Clients
Log
Processor
Storage (HDFS)
Storage (HDFS)
Log
ReplicatorStorage (HDFS)
Inside
DataCenter
Storage
(Streaming)
Events Events
RT Storage (HDFS)
Inside
DC1
Events Events
RT Storage (HDFS)
Inside
DCN
DW Storage (HDFS)
Prod Storage (HDFS)
DW Storage (HDFS)
Cold Storage (HDFS)
Prod Storage (HDFS)
Events Events
RT Storage (HDFS)
Inside
DC1
Events Events
RT Storage (HDFS)
Inside
DCN
DW Storage (HDFS)
Prod Storage (HDFS)
DW Storage (HDFS)
Cold Storage (HDFS)
Prod Storage (HDFS)
Routing Trillions of Events Per Day @Twitter
Clients
Local log collection daemon
Clients
Aggregate log events grouped
by Category
Storage (HDFS)
HTTP
Remote
Clients
Log
Processor
Storage (HDFS)
Storage (HDFS)
Log
ReplicatorStorage (HDFS)
Inside
DataCenter
Storage
(Streaming)
Scribe
Client
Daemon
Scribe
Aggregator
Daemons
Scribe
Client
Daemon
Flume
Aggregator
Daemon
Flume
Aggregator
Daemon
Flume
Client
Daemon
●
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●
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●
●
●
Source Sink
Channel
Client HDFS
Flume Agent
●
●
●
Agent 1 Agent 2 Agent 3
Category 1 Category 3Category 2
Category Group
Group 1
Category Groups
Aggregator Group 1 Aggregator Group 2●
●
Agent 1 Agent 2 Agent 3 Agent 8
Group 2
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●
●
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●
●
●
Routing Trillions of Events Per Day @Twitter
Clients
Local log collection daemon
Clients
Aggregate log events grouped
by Category
Storage (HDFS)
HTTP
Remote
Clients
Log
Processor
Storage (HDFS)
Storage (HDFS)
Log
ReplicatorStorage (HDFS)
Inside
DataCenter
Storage
(Streaming)
To process one
day of data Output of cleaned,
compressed,
consolidated, and
converted
Saved by
processing Flume
sequence files
●
●
●
●
Datacenter 1
ads_group/yyyy/mm/dd/hh
ads_click/yyyy/mm/dd/hh
ads_view/yyyy/mm/dd/hh
login_event/yyyy/mm/dd/hhlogin_group/yyyy/mm/dd/hh
Category Groups CategoriesDemux Jobs
ads_group_demuxer
login_group_demuxer
Datacenter 1
ads_group/yyyy/mm/dd/hh
ads_click/yyyy/mm/dd/hh
ads_view/yyyy/mm/dd/hh
login_event/yyyy/mm/dd/hhlogin_group/yyyy/mm/dd/hh
Category Groups CategoriesDemux Jobs
ads_group_demuxer
login_group_demuxer
Datacenter 1
ads_group/yyyy/mm/dd/hh
ads_click/yyyy/mm/dd/hh
ads_view/yyyy/mm/dd/hh
login_event/yyyy/mm/dd/hhlogin_group/yyyy/mm/dd/hh
Category Groups CategoriesDemux Jobs
ads_group_demuxer
login_group_demuxer
Decode
Demux
Clean
[Convert]
● Scribe’s contract amounts to sending a binary blob to a port
● Scribe used new line characters to delimit records in a binary blob batch
of records
● Valid records may include newline characters
● Scribe base64 encoded received binary blobs to avoid confusion with
record delimiter
● Base 64 encoding is no longer necessary because we have moved to one
serialized Thrift object per binary blob
/logs/ads_click/yyyy/mm/dd/hh/1.lzo
/logs/ads_view/yyyy/mm/dd/hh/1.lzo
/raw/ads_group/yyyy/mm/dd/hh/ads_group_1.seq
DEMUX
/logs/ads_view/yyyy/mm/dd/hh/1.lzo
/logs/ads_click/yyyy/mm/dd/hh/1.lzo
/logs/ads_view/yyyy/mm/dd/hh/1.lzo
/raw/ads_group/yyyy/mm/dd/hh/ads_group_1.seq
DEMUX
/logs/ads_view/yyyy/mm/dd/hh/1.lzo
/logs/ads_click/yyyy/mm/dd/hh/1.lzo
/logs/ads_view/yyyy/mm/dd/hh/2.lzo
/raw/ads_group/yyyy/mm/dd/hh/ads_group_1.seq
DEMUX
/logs/ads_view/yyyy/mm/dd/hh/1.lzo
●
●
●
●
●
● Some categories are significantly larger than other categories (KBs v TBs)
● MapReduce demux? Each reducer handles a single category
● Streaming demux? Each spout or channel handles a single category
● Massive skew in partitioning by category causes long running tasks which
slows down job completion time
● Relatively well understood fault tolerance semantics similar to
MapReduce, Spark, etc
● Tez’s dynamic hash partitioner adjusts partitions at runtime if necessary,
allowing large partitions to be further partitioned so multiple tasks process
events for a single category one task
○ More info at TEZ-3209.
○ Thanks to team member Ming Ma for the contribution!
● Easier horizontal scaling simultaneously providing more predictable
processing times
Task 3
Task 2
Input File 1
Task 1
Task 3
Task 2
Input File 1
Task 1
Task 4
Task 5
Routing Trillions of Events Per Day @Twitter
Clients
Local log collection daemon
Clients
Aggregate log events grouped
by Category
Storage (HDFS)
HTTP
Remote
Clients
Log
Processor
Storage (HDFS)
Storage (HDFS)
Log
ReplicatorStorage (HDFS)
Inside
DataCenter
Storage
(Streaming)
Across all analytics clusters Replicated to analytics clusters
●
○
○
●
●
●
Datacenter N
Datacenter 1
ads_click/yyyy/mm/dd/hh
ads_view/yyyy/mm/dd/hh
login_event/yyyy/mm/dd/hh
ads_click/yyyy/mm/dd/hh
ads_view/yyyy/mm/dd/hh
login_event/yyyy/mm/dd/hh
ads_click/yyyy/mm/dd/hh
ads_view/yyyy/mm/dd/hh
login_event/yyyy/mm/dd/hh
ads_click/yyyy/mm/dd/hh
Replication Jobs
ads_click_repl
ads_view_repl
ads_click_repl
login_event_repl
Datacenter N
Datacenter 1
ads_click/yyyy/mm/dd/hh
ads_view/yyyy/mm/dd/hh
login_event/yyyy/mm/dd/hh
ads_click/yyyy/mm/dd/hh
ads_view/yyyy/mm/dd/hh
login_event/yyyy/mm/dd/hh
ads_click/yyyy/mm/dd/hh
ads_view/yyyy/mm/dd/hh
login_event/yyyy/mm/dd/hh
ads_click/yyyy/mm/dd/hh
Replication Jobs
ads_click_repl
ads_view_repl
ads_click_repl
login_event_repl
Datacenter N
Datacenter 1
ads_click/yyyy/mm/dd/hh
ads_view/yyyy/mm/dd/hh
login_event/yyyy/mm/dd/hh
ads_click/yyyy/mm/dd/hh
ads_view/yyyy/mm/dd/hh
login_event/yyyy/mm/dd/hh
ads_click/yyyy/mm/dd/hh
ads_view/yyyy/mm/dd/hh
login_event/yyyy/mm/dd/hh
ads_click/yyyy/mm/dd/hh
Replication Jobs
ads_click_repl
ads_view_repl
ads_click_repl
login_event_repl
Datacenter N
Datacenter 1
ads_click/yyyy/mm/dd/hh
ads_view/yyyy/mm/dd/hh
login_event/yyyy/mm/dd/hh
ads_click/yyyy/mm/dd/hh
ads_view/yyyy/mm/dd/hh
login_event/yyyy/mm/dd/hh
ads_click/yyyy/mm/dd/hh
ads_view/yyyy/mm/dd/hh
login_event/yyyy/mm/dd/hh
ads_click/yyyy/mm/dd/hh
Replication Jobs
ads_click_repl
ads_view_repl
ads_click_repl
login_event_repl
Datacenter N
Datacenter 1
ads_click/yyyy/mm/dd/hh
ads_view/yyyy/mm/dd/hh
login_event/yyyy/mm/dd/hh
ads_click/yyyy/mm/dd/hh
ads_view/yyyy/mm/dd/hh
login_event/yyyy/mm/dd/hh
ads_click/yyyy/mm/dd/hh
ads_view/yyyy/mm/dd/hh
login_event/yyyy/mm/dd/hh
ads_click/yyyy/mm/dd/hh
Replication Jobs
ads_click_repl
ads_view_repl
ads_click_repl
login_event_repl
Datacenter N
Datacenter 1
ads_click/yyyy/mm/dd/hh
ads_view/yyyy/mm/dd/hh
login_event/yyyy/mm/dd/hh
ads_click/yyyy/mm/dd/hh
ads_view/yyyy/mm/dd/hh
login_event/yyyy/mm/dd/hh
ads_click/yyyy/mm/dd/hh
ads_view/yyyy/mm/dd/hh
login_event/yyyy/mm/dd/hh
ads_click/yyyy/mm/dd/hh
Replication Jobs
ads_click_repl
ads_view_repl
ads_click_repl
login_event_repl
Datacenter N
Datacenter 1
ads_click/yyyy/mm/dd/hh
ads_view/yyyy/mm/dd/hh
login_event/yyyy/mm/dd/hh
ads_click/yyyy/mm/dd/hh
ads_view/yyyy/mm/dd/hh
login_event/yyyy/mm/dd/hh
ads_click/yyyy/mm/dd/hh
ads_view/yyyy/mm/dd/hh
login_event/yyyy/mm/dd/hh
ads_click/yyyy/mm/dd/hh
Replication Jobs
ads_click_repl
ads_view_repl
ads_click_repl
login_event_repl
Datacenter N
Datacenter 1
ads_click/yyyy/mm/dd/hh
ads_view/yyyy/mm/dd/hh
login_event/yyyy/mm/dd/hh
ads_click/yyyy/mm/dd/hh
ads_view/yyyy/mm/dd/hh
login_event/yyyy/mm/dd/hh
ads_click/yyyy/mm/dd/hh
ads_view/yyyy/mm/dd/hh
login_event/yyyy/mm/dd/hh
ads_click/yyyy/mm/dd/hh
Replication Jobs
ads_click_repl
ads_view_repl
ads_click_repl
login_event_repl
Datacenter N
Datacenter 1
ads_click/yyyy/mm/dd/hh
ads_view/yyyy/mm/dd/hh
login_event/yyyy/mm/dd/hh
ads_click/yyyy/mm/dd/hh
ads_view/yyyy/mm/dd/hh
login_event/yyyy/mm/dd/hh
ads_click/yyyy/mm/dd/hh
ads_view/yyyy/mm/dd/hh
login_event/yyyy/mm/dd/hh
ads_click/yyyy/mm/dd/hh
Replication Jobs
ads_click_repl
ads_view_repl
ads_click_repl
login_event_repl
Datacenter N
Datacenter 1
ads_click/yyyy/mm/dd/hh
ads_view/yyyy/mm/dd/hh
login_event/yyyy/mm/dd/hh
ads_click/yyyy/mm/dd/hh
ads_view/yyyy/mm/dd/hh
login_event/yyyy/mm/dd/hh
ads_click/yyyy/mm/dd/hh
ads_view/yyyy/mm/dd/hh
login_event/yyyy/mm/dd/hh
ads_click/yyyy/mm/dd/hh
Replication Jobs
ads_click_repl
ads_view_repl
ads_click_repl
login_event_repl
Datacenter N
Datacenter 1
ads_click/yyyy/mm/dd/hh
ads_view/yyyy/mm/dd/hh
login_event/yyyy/mm/dd/hh
ads_click/yyyy/mm/dd/hh
ads_view/yyyy/mm/dd/hh
login_event/yyyy/mm/dd/hh
ads_click/yyyy/mm/dd/hh
ads_view/yyyy/mm/dd/hh
login_event/yyyy/mm/dd/hh
ads_click/yyyy/mm/dd/hh
Replication Jobs
ads_click_repl
ads_view_repl
ads_click_repl
login_event_repl
Copy
Merge
Present
Publish
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Routing Trillions of Events Per Day @Twitter
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Routing Trillions of Events Per Day @Twitter

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