The document discusses building a system for processing machine and event-oriented data in real-time. It describes the high-level architecture which involves data acquisition, processing, storage and querying. Events are modeled and transformed through stream processing jobs. Metrics and time series data are aggregated. Challenges include dealing with distributed systems issues, data quality, and immaturity of stream processing technologies.

1. building a system for machine and
event-oriented data
e. sammer | @esammer
big data day la 2016

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context: it’s important

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what we do
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• we build a system for the operation of modern data centers
• triage and diagnostics, exploration, trends, advanced analytics of complex
systems
• our data: logs, metrics, human activity, anything that occurs in the data center
• “enterprise software” (i.e. we build for others.)
• today: how we built what we built

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our typical customer use cases
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• millions of events / sec, sub-second end to end latency, full fidelity retention,
critical use cases
• quality of service - “are credit card transactions happening fast enough?”
• fraud detection - “detect, investigate, prosecute, and learn from fraud.”
• forensic diagnostics - “what really caused the outage last friday?”
• security - “who’s doing what, where, when, why, and how, and is that ok?”
• user behavior - ”capture and correlate user behavior with system performance,
then feed it to downstream systems in realtime.”

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depth: 3 meters

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high level architecture – data acquisition
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high level architecture – processing, storage, query
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guarantees
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• no single point of failure exists
• all components scale horizontally[1]
• data retention and latency is a function of cost, not tech[1]
• every event is delivered provided no more than N - 1 failures occur (where N is
the kafka replication level)
• all operations, including upgrade, are online[2]
• every event is (or appears to be) delivered exactly once[3]
[1] we’re positive there’s a limit, but thus far it has been cost.
[2] from the user’s perspective, at a system level.
[3] when queried via our UI. lots of details here.

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events

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modeling our world
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• everything is an event
• each event contains a timestamp, type, location, host, service, body, and type-
specific attributes (k/v pairs)
• build specialized aggregates as necessary - just optimized views of the data

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event schema
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{
id: string,
ts: long,
event_type_id: int,
location: string,
host: string,
service: string,
body: [ null, string ],
attributes: map<string>
}

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event types
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• some event types are standard
– syslog, http, log4j, generic text record, …
• users define custom event types
• producers populate event type
• transformations can turn an event of type A into B
• event type metadata tells downstream systems how to interpret body and
attributes

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ex: generic syslog event
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event_type_id: 100, // rfc3164, rfc5424 (syslog)
body: … // raw syslog message bytes
attributes: { // extracted fields from body
syslog_message: “DHCPACK from 10.10.0.1 (xid=0x45b63bdc)”,
syslog_severity: “6”, // info severity
syslog_facility: “3”, // daemon facility
syslog_process: “dhclient”,
syslog_pid: “668”,
…
}

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ex: generic http event
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event_type_id: 102, // generic http event
body: … // raw http log message bytes
attributes: {
http_req_method: “GET”,
http_req_vhost: “w2a-demo-02”,
http_req_path: “/api/v1/search?q=service%3Asshd&p=1&s=200”,
http_req_query: “q=service%3Asshd&p=1&s=200”,
http_resp_code: “200”,
…
}

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stream processing

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a reminder…
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data processing
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• each processing job gets a full stream of the fire hose, decides what it wants to
consider or operate on
• output of “non-terminal” jobs always just events
• result: all processing jobs are composable
• many jobs take user rules or configuration from our ui

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the jobs
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• transformation engine: configuration-based data transformation
• metric aggregation: olap cube construction of time series data (e.g. host 17
user cpu time)
• model build/eval: train/evaluate various kinds of models (e.g. anomaly
detection)
• trigger engine: detect complex patterns in the stream, emit events on match
(e.g. complex event processing, automated workflow, alerting)
• action engine: perform some action upon receiving a specific event type (e.g.
email notification, 3rd party api invocation)
• storage: write all the things to hdfs

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transformation use cases
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event feedback loops
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metrics and time series

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aggregation
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• used for host/service metrics
• two halves: on write and on query
• data model: (dimensions) => (aggregates)
• on write
– reduce(a: A, b: A) => A over window
– store “base” aggregates, all associative and commutative
• on query
– perform same aggregate or derivative aggregates
– group by the same dimensions
– we use SQL (impala+parquet+hdfs)

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aside: late arriving data (it’s a thing)
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• never trust a (wall) clock
• producer determines event time, rest of the system uses this always
• data that shows up late always processed according to event time
• apache beam describes these issues perfectly
• this is real and you must deal with it

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extension, pain, and advice

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extending the system
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• custom producers
• custom consumers
• event types
• parser / transformation plugins
• custom metric definition and aggregate functions
• custom processing jobs on landed data

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pain (aka: the struggle is real)
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• lots of tradeoffs when picking a stream processing solution
– samza: right features, but low level programming model, not supported by vendors.
missing security features.
– storm: too rigid, too slow. not supported by all Hadoop vendors.
– flink: relatively new, fledgling community. growing.
– spark streaming: tons of issues initially, but lots of community energy. improving.
• stack complexity, (relative im)maturity
• beam-style retractions required for correct, timely, efficient aggregates of
complex metrics (non-assoc/commutative)

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if you’re going to try this…
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• read all the literature on stream processing[1]
• treat it like the distributed systems problem it is
• understand, make, and make good on guarantees
• find the right abstractions
• never trust the hand waving or “hello worlds”
• fully evaluate the projects/products in this space
• understand it’s not just about search
[1] wait, like all of it? yea, like all of it.

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things I didn’t talk about
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• reprocessing data when bad code / transformations are detected
• dealing with data quality issues (“the struggle is real” part 2)
• the user interface and all the fancy analytics
– data visualization and exploration
– event search
– anomalous trend and event detection
– metric, source, and event correlation
– motif finding
– noise reduction and dithering
• event delivery semantics (e.g. at least/most/exactly once, etc.)

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questions?
thank you.
@esammer | esammer@rocana.com