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Scalable Online Analytics for Monitoring
LISA15, Nov. 13, 2015, Washington, D.C.
Heinrich Hartmann, PhD, Chief Data Scientist, Circonus
I’m Heinrich
· From Mainz, Germany
· Studied Math. in Mainz, Bonn, Oxford
· PhD in Algebraic Geometry
· Sensor Data Mining at Univ. Koblenz
· Freelance IT consultant
· Chief Data Scientist at Circonus
Heinrich.Hartmann@Circonus.com
@HeinrichHartman(n)
Circonus is ...
@HeinrichHartman(n)
· monitoring and telemetry analysis platform
· scalable to millions of incoming metrics
· available as public and private SaaS
· built-in histograms, forecasting, anomaly-detection, ...
@HeinrichHartman(n)
This talk is about...
I. The Future of Monitoring
II. Patterns of Telemetry Analysis
III. Design of the Online Analytics Engine ‘Beaker’
Part I - The Future of Monitoring
Monitor this:
· 100 containers in one fleet
· 200 metrics per-container
· 50 code pushes a day
· For each push all containers are recreated
The “cloud monitoring challenge” 2015
@HeinrichHartman(n)
Line-plots work well for small numbers of nodes
@HeinrichHartman(n)
Node 1
Node 2
Node 3
Node 4
Node 5
Node 6
CPU utilization for a DB cluster. Source: Internal.
... but can get polluted easily
@HeinrichHartman(n)
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CPU utilization for a DB cluster. Source: Internal.
“Information is not a scarce resource. Attention is.”
Herbert A. Simon
@HeinrichHartman(n) Source: http://www.circonus.com/the-future-of-monitoring-qa-with-john-allspaw/
Store Histograms and Alert on Percentiles
@HeinrichHartman(n)
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90% percentile
CPU Utilization of a db service with a variable number of nodes.
@HeinrichHartman(n)
Anomaly Detection for Surfacing relevant data
Mockup of an metric overview page. Source: Circonus
Part II - Patterns of Telemetry Analysis
@HeinrichHartman(n)
Telemetry analysis comes in two forms
Online Analytics
· Anomaly detection
· Percentile alerting
· Smart alerting rules
· Smart dashboards
Offline Analytics
· Post mortem analysis
· Assisted thresholding
Stream
Processing
‘Big Data’
@HeinrichHartman(n)
New Components in Circonus
BunsenBeaker
@HeinrichHartman(n)
Beaker & Bunsen in the Circonus Architecture
metrics Ernie
Alerting Service
Snowth
Metric Store
alerts
Web-UI
Q
Beaker
Online AE
Bunsen
Offline AE
@HeinrichHartman(n)
Pattern 1: Windowing
Examples
· Supervised Machine Learning
· Fourier Transformation
· Anomaly Detection (etsy)
Remarks
· Tradeoff: window size
rich features vs. memory
· Tradeoff: overlap
latency vs. CPU
windows = window(y_stream)
def results():
for w in windows:
yield z = method(w)
@HeinrichHartman(n)
Pattern 3: Processing Units
processing_unit = {
state = ...
update = function(self, y) ... end
}
Example
· Exponential Smoothing
· Holt Winters forecasting
· Anomaly Detection (Circonus)
Remarks
· Fast updates
· Fully general
· Cost: maintains state
Circonus Anomaly Detection
@HeinrichHartman(n)
local q = 0.9
exponential_smoothing = {
s = 0,
update = function(self, y)
self.s = (y * q) + (self.s * (1 - q))
return self.s
end
}
For
A Processing Unit for Exponential Smoothing
Exponential smoothing applied to a dns-duration metric
More examples on http://heinrichhartmann.com/.../Generative-Models-for-Time-Series
Processing units are convenient
· Primitive transformation are readily implemented:
Arithmetic, Smoothing, Forecasting, ...
· Fully general. Allow window-based processing as well
· Composable. Compose several PUs to get a new one!
@HeinrichHartman(n)
Circonus Analytics Query Language
· Create your own customized processing unit from primitives
· UNIX-inspired syntax with pipes ‘|’
· Native support for histogram metrics
@HeinrichHartman(n)
@HeinrichHartman(n)
CAQL: Example 1 - Low frequency AD
Pre-process a metric before feeding into anomaly detection
metric:average(<>) | rolling:mean(30m) | anomaly_detection()
@HeinrichHartman(n)
CAQL: Example 2 - Histogram Aggregation
Histograms are first-class citizens
metric:average(<uuid>) | window:histogram(1h) | histogram:percentile(95)
Part III - The Design of the Online Analytics
Engine ‘Beaker’
1. Read messages from a message queue
2. Execute a processing units over incoming metrics
3. Publish computed values to message queue
Beaker v. 0.1: Simple stream processing
Beaker Service
output
Q
input
Q
Beaker: Basic Data Flow
Challenge 1: Rollup metrics by the minute
@HeinrichHartman(n)
· Metrics arrive asynchronously on the input queue
· PUs expect exactly one sample per minute
· Rollup logic needs to allow for
· late arrival, e.g. when broker is behind
· out of order arrival
· errors in system time (time-zones, drifts)
Consequence: No real-time processing in Beaker
· Rolling up data in 1m periods causes an average delay
of 30sec for data processing.
· Real-time threshold-based alerting still available.
· Approach: Avoid rolling up data for stateless PUs.
@HeinrichHartman(n)
Challenge 2: Multiple input slots
input metric
input metric
input metric
input metric
input metric
processing unit
processing unit
processing unit output metric
output metric
output metric
Beaker: Logical Data Flow
@HeinrichHartman(n)
Challenge 3:
Synchronize roll-ups for multiple inputs is tricky
@HeinrichHartman(n)
Challenge 4: Fault Tolerance
Definition (Birman): A service is fault tolerant if it
tolerates the failure of nodes without producing wrong
output messages.
Failed nodes must be able to recover from a crash and
rejoin the cluster.
The time to recovery should be as low as possible.
@HeinrichHartman(n) Source: K. Birman - Reliable Distributed Systems, Springer, 2005
Beaker v0.5:
· Automated restarts
a. Service Management Facilities (svcadm) in OmniOS
b. systemd or watchdogs in Linux
· But, recovery can take a long time
State has to be rebuilt from input stream.
· Need a way to recover faster from errors:
a. Persist processing unit state (software updates!)
b. Access persisted metric data
@HeinrichHartman(n)
Beaker v. 0.5: Use db to rebuild state on startup
@HeinrichHartman(n)
Beaker Service
output
Q
input
Q
Snowth db
Challenge 5: High Availability
Definition (Birman): A service is highly available it
continues to publish valid messages during a node failure
after a small reconfiguration period.
For Beaker we require a reconfiguration period of less
than 1 minute. In this time messages may be delayed (e.g.
30sec) and duplicated messages may be published.
@HeinrichHartman(n) Source: K. Birman - Reliable Distributed Systems, Springer, 2005
Beaker v. 0.5 -- HA Cluster
Beaker HA Cluster
Beaker Master
output
Q
input
Q
Beaker
Slave
Beaker
Slave On
Failover:
Master
election
@HeinrichHartman(n)
Snowth db
@HeinrichHartman(n)
Challenge 6: Scalability
Beaker needs to scale in the following dimensions
· Number of processing units up to an unlimited amount.
· In the number of incoming metrics up to ~100M
metrics.
Beaker v.0.6 -- Multiple - HA Clusters
Beaker HA Cluster II (3 slaves)
Beaker HA Cluster I (5 slaves)
..
.
..
Beaker HA Cluster III (1 slave)
BI-M BI-S1 BI-S5
BII-M BII-S1 BII-S3
BIII-M BIII-S1
Meta Service
Msg
Broker
Msg
Broker
BIII-S2
@HeinrichHartman(n)
Snowth db
..
@HeinrichHartman(n)
Done! Great. This works...
Can we do better?
@HeinrichHartman(n)
· Divide Beaker into multiple services
· Avoid master election in processing service, by allowing duplicates
· Upside: Only the processing service needs to scale out
Beaker Service
Beaker v. 1.0 -- Divide and Conquer
@HeinrichHartman(n)
Q Q
Processing
Service
Dedup
Service
Rollup
Service
Scaling the Processing Service is simplified
· No rollup logic in workers
· All workers publish messages
· No failover logic necessary
· Replicate each PUs on multiple
workers
· No crosstalk between nodes
( = 0 in USL).
@HeinrichHartman(n)
Q Q
...
snowth db
Worker
Worker
Worker
Meta Service
Processing Service:
Data Flow
Benchmark results on prototype
· PU type anomaly_detection
PU count 100
Throughput 15 kHz
· PU type anomaly_detection
PU count 10k
Throughput 4.2 kHz
Machine: 6-core Xeon E5 2630 v2 at 2.6 GHz
@HeinrichHartman(n)
Conclusion
· Stateful processing units allow implementation of next
generation of monitoring analytics
· Use CAQL to build your own processing units
· Service orientation facilitates scaling
· Beaker will be out soon
@HeinrichHartman(n)
Credits
Joint work with:
· Jonas Kunze
· Theo Schlossnagle
Image Credits:
Example of Kummer Surface, Claudio Rocchini, CC-BY-SA, https://en.wikipedia.org/wiki/File:Kummer_surface.png
Indian truck, by strudelt, CC-BY, https://commons.wikimedia.org/wiki/File:Truck_in_India_-_overloaded.jpg
Kafka, Public Domain, https://en.wikipedia.org/wiki/File:Kafka_portrait.jpg
Thinker, Public Domain, https://www.flickr.com/photos/mustangjoe/5966894496
@HeinrichHartman(n)

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Scalable Online Analytics for Monitoring

  • 1. Scalable Online Analytics for Monitoring LISA15, Nov. 13, 2015, Washington, D.C. Heinrich Hartmann, PhD, Chief Data Scientist, Circonus
  • 2. I’m Heinrich · From Mainz, Germany · Studied Math. in Mainz, Bonn, Oxford · PhD in Algebraic Geometry · Sensor Data Mining at Univ. Koblenz · Freelance IT consultant · Chief Data Scientist at Circonus Heinrich.Hartmann@Circonus.com @HeinrichHartman(n)
  • 3. Circonus is ... @HeinrichHartman(n) · monitoring and telemetry analysis platform · scalable to millions of incoming metrics · available as public and private SaaS · built-in histograms, forecasting, anomaly-detection, ...
  • 4. @HeinrichHartman(n) This talk is about... I. The Future of Monitoring II. Patterns of Telemetry Analysis III. Design of the Online Analytics Engine ‘Beaker’
  • 5. Part I - The Future of Monitoring
  • 6. Monitor this: · 100 containers in one fleet · 200 metrics per-container · 50 code pushes a day · For each push all containers are recreated The “cloud monitoring challenge” 2015 @HeinrichHartman(n)
  • 7. Line-plots work well for small numbers of nodes @HeinrichHartman(n) Node 1 Node 2 Node 3 Node 4 Node 5 Node 6 CPU utilization for a DB cluster. Source: Internal.
  • 8. ... but can get polluted easily @HeinrichHartman(n) N N N N N N N N N N N N N N N N N N N N CPU utilization for a DB cluster. Source: Internal.
  • 9. “Information is not a scarce resource. Attention is.” Herbert A. Simon @HeinrichHartman(n) Source: http://www.circonus.com/the-future-of-monitoring-qa-with-john-allspaw/
  • 10. Store Histograms and Alert on Percentiles @HeinrichHartman(n) N N N N N N N N N N N N N N N N N N N N N N N 90% percentile CPU Utilization of a db service with a variable number of nodes.
  • 11. @HeinrichHartman(n) Anomaly Detection for Surfacing relevant data Mockup of an metric overview page. Source: Circonus
  • 12. Part II - Patterns of Telemetry Analysis
  • 13. @HeinrichHartman(n) Telemetry analysis comes in two forms Online Analytics · Anomaly detection · Percentile alerting · Smart alerting rules · Smart dashboards Offline Analytics · Post mortem analysis · Assisted thresholding Stream Processing ‘Big Data’
  • 14. @HeinrichHartman(n) New Components in Circonus BunsenBeaker
  • 15. @HeinrichHartman(n) Beaker & Bunsen in the Circonus Architecture metrics Ernie Alerting Service Snowth Metric Store alerts Web-UI Q Beaker Online AE Bunsen Offline AE
  • 16. @HeinrichHartman(n) Pattern 1: Windowing Examples · Supervised Machine Learning · Fourier Transformation · Anomaly Detection (etsy) Remarks · Tradeoff: window size rich features vs. memory · Tradeoff: overlap latency vs. CPU windows = window(y_stream) def results(): for w in windows: yield z = method(w)
  • 17. @HeinrichHartman(n) Pattern 3: Processing Units processing_unit = { state = ... update = function(self, y) ... end } Example · Exponential Smoothing · Holt Winters forecasting · Anomaly Detection (Circonus) Remarks · Fast updates · Fully general · Cost: maintains state Circonus Anomaly Detection
  • 18. @HeinrichHartman(n) local q = 0.9 exponential_smoothing = { s = 0, update = function(self, y) self.s = (y * q) + (self.s * (1 - q)) return self.s end } For A Processing Unit for Exponential Smoothing Exponential smoothing applied to a dns-duration metric More examples on http://heinrichhartmann.com/.../Generative-Models-for-Time-Series
  • 19. Processing units are convenient · Primitive transformation are readily implemented: Arithmetic, Smoothing, Forecasting, ... · Fully general. Allow window-based processing as well · Composable. Compose several PUs to get a new one! @HeinrichHartman(n)
  • 20. Circonus Analytics Query Language · Create your own customized processing unit from primitives · UNIX-inspired syntax with pipes ‘|’ · Native support for histogram metrics @HeinrichHartman(n)
  • 21. @HeinrichHartman(n) CAQL: Example 1 - Low frequency AD Pre-process a metric before feeding into anomaly detection metric:average(<>) | rolling:mean(30m) | anomaly_detection()
  • 22. @HeinrichHartman(n) CAQL: Example 2 - Histogram Aggregation Histograms are first-class citizens metric:average(<uuid>) | window:histogram(1h) | histogram:percentile(95)
  • 23. Part III - The Design of the Online Analytics Engine ‘Beaker’
  • 24. 1. Read messages from a message queue 2. Execute a processing units over incoming metrics 3. Publish computed values to message queue Beaker v. 0.1: Simple stream processing Beaker Service output Q input Q Beaker: Basic Data Flow
  • 25. Challenge 1: Rollup metrics by the minute @HeinrichHartman(n) · Metrics arrive asynchronously on the input queue · PUs expect exactly one sample per minute · Rollup logic needs to allow for · late arrival, e.g. when broker is behind · out of order arrival · errors in system time (time-zones, drifts)
  • 26. Consequence: No real-time processing in Beaker · Rolling up data in 1m periods causes an average delay of 30sec for data processing. · Real-time threshold-based alerting still available. · Approach: Avoid rolling up data for stateless PUs. @HeinrichHartman(n)
  • 27. Challenge 2: Multiple input slots input metric input metric input metric input metric input metric processing unit processing unit processing unit output metric output metric output metric Beaker: Logical Data Flow @HeinrichHartman(n)
  • 28. Challenge 3: Synchronize roll-ups for multiple inputs is tricky @HeinrichHartman(n)
  • 29. Challenge 4: Fault Tolerance Definition (Birman): A service is fault tolerant if it tolerates the failure of nodes without producing wrong output messages. Failed nodes must be able to recover from a crash and rejoin the cluster. The time to recovery should be as low as possible. @HeinrichHartman(n) Source: K. Birman - Reliable Distributed Systems, Springer, 2005
  • 30. Beaker v0.5: · Automated restarts a. Service Management Facilities (svcadm) in OmniOS b. systemd or watchdogs in Linux · But, recovery can take a long time State has to be rebuilt from input stream. · Need a way to recover faster from errors: a. Persist processing unit state (software updates!) b. Access persisted metric data @HeinrichHartman(n)
  • 31. Beaker v. 0.5: Use db to rebuild state on startup @HeinrichHartman(n) Beaker Service output Q input Q Snowth db
  • 32. Challenge 5: High Availability Definition (Birman): A service is highly available it continues to publish valid messages during a node failure after a small reconfiguration period. For Beaker we require a reconfiguration period of less than 1 minute. In this time messages may be delayed (e.g. 30sec) and duplicated messages may be published. @HeinrichHartman(n) Source: K. Birman - Reliable Distributed Systems, Springer, 2005
  • 33. Beaker v. 0.5 -- HA Cluster Beaker HA Cluster Beaker Master output Q input Q Beaker Slave Beaker Slave On Failover: Master election @HeinrichHartman(n) Snowth db
  • 34. @HeinrichHartman(n) Challenge 6: Scalability Beaker needs to scale in the following dimensions · Number of processing units up to an unlimited amount. · In the number of incoming metrics up to ~100M metrics.
  • 35. Beaker v.0.6 -- Multiple - HA Clusters Beaker HA Cluster II (3 slaves) Beaker HA Cluster I (5 slaves) .. . .. Beaker HA Cluster III (1 slave) BI-M BI-S1 BI-S5 BII-M BII-S1 BII-S3 BIII-M BIII-S1 Meta Service Msg Broker Msg Broker BIII-S2 @HeinrichHartman(n) Snowth db ..
  • 37. Can we do better? @HeinrichHartman(n)
  • 38. · Divide Beaker into multiple services · Avoid master election in processing service, by allowing duplicates · Upside: Only the processing service needs to scale out Beaker Service Beaker v. 1.0 -- Divide and Conquer @HeinrichHartman(n) Q Q Processing Service Dedup Service Rollup Service
  • 39. Scaling the Processing Service is simplified · No rollup logic in workers · All workers publish messages · No failover logic necessary · Replicate each PUs on multiple workers · No crosstalk between nodes ( = 0 in USL). @HeinrichHartman(n) Q Q ... snowth db Worker Worker Worker Meta Service Processing Service: Data Flow
  • 40. Benchmark results on prototype · PU type anomaly_detection PU count 100 Throughput 15 kHz · PU type anomaly_detection PU count 10k Throughput 4.2 kHz Machine: 6-core Xeon E5 2630 v2 at 2.6 GHz @HeinrichHartman(n)
  • 41. Conclusion · Stateful processing units allow implementation of next generation of monitoring analytics · Use CAQL to build your own processing units · Service orientation facilitates scaling · Beaker will be out soon @HeinrichHartman(n)
  • 42. Credits Joint work with: · Jonas Kunze · Theo Schlossnagle Image Credits: Example of Kummer Surface, Claudio Rocchini, CC-BY-SA, https://en.wikipedia.org/wiki/File:Kummer_surface.png Indian truck, by strudelt, CC-BY, https://commons.wikimedia.org/wiki/File:Truck_in_India_-_overloaded.jpg Kafka, Public Domain, https://en.wikipedia.org/wiki/File:Kafka_portrait.jpg Thinker, Public Domain, https://www.flickr.com/photos/mustangjoe/5966894496 @HeinrichHartman(n)