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Log Events
Apache Kafka
Real-time
Personalization Pipeline
Apache Storm
User Profile
Store
Apache Cassandra
Entity Metadata
Store
Apache Cassandra
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Build
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Storm
Cluster
Running
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t1 t2
Storm
Cluster
t3 t4
Running
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Running
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Deactivate
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Submit
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Storm
Cluster
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t5 t6 t7 t8
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Pagerduty Inhouse Solution
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Log Events
Apache Kafka
Real-time
Personalization Pipeline
Apache Storm
User Profile
Store
Apache Cassandra
Entity Metadata
Store
Apache Cassandra
● Different tables for different TTLs and set
gc_grace_period=0. Read repairs are disabled.
● Used DateTieredCompactionStrategy for short lived
data.
● Control the number of open connections from Storm
topology to Cassandra
● Configure Snitch to ensure proper call routing
● 1 worker per node per topology
● 1 executor per core for CPU bound tasks
● 1-10 executors per core for IO bound tasks
● Compute total parallelism possible and distribute it
amongst slow and fast tasks. High parallelism for
slow tasks, low for fast tasks.
* Parallelism tuning inspired by P Taylor Goetz’s Strata 2014 talk
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● Think about constraints in external vs in-process caching
○ External Caching
■ Network IO
■ Latency
■ Another point of failure
○ In-process
■ Limited memory
■ No persistence
How Spotify scales Apache Storm Pipelines
How Spotify scales Apache Storm Pipelines

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