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@rawkode
Good AfternoonDavid McKay@rawkode
Developer Advocate@InfluxDB | #InfluxDB
PHPUK 2020
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@rawkode
Developer Advocateat InfluxData
@rawkode
🏴 Scottish
🐭 Has 9 Pets
💙 Esoteric Programming Languages
☸ Kubernetes Team
🤔 Stoic
David Mckay
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Introduction to Time Series
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@rawkode
Before we begin …
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@rawkode
Pop Quiz“Invented” When?
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@rawkode
EncodingFirst Used … ?First Used 410 BC
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@rawkode
Encoding“Documented” in The Lives of the Noble Grecians and Romans, by Roman historian Plutarch.
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@rawkode
Alcibiades suddenly raised the Athenian ensign in the admiral shop, and fell upon those galleys of the Peloponnesians …
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@rawkode
In the 14th century, things hadn’t actually advanced much more. The Black Book of Admiralty listed 2 signals: 1 flag or 2 flags
Encoding
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@rawkode
By the 15th century there were 15 flags, each with a single meaning.
Encoding
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@rawkode
Finally, in the late 17th century; a French system existed (Mahé de la Bourdonnais) with 10 coloured flags, representing 0-9
Encoding
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@rawkode
ShardingFirst Used … ?First Used 150 BC
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@rawkode
ShardingFirst “documented” example was in ~150 AD, invented and described by Polybius.
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@rawkode
We take the alphabet and divide it into five parts, each consisting of five letters.
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@rawkode
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@rawkode
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History of Time Series
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@rawkode
The earliest form of a company which issued public shares was the case of the publicani during the Roman Republic.
The Romans Did It
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@rawkode
Like modern joint-stock companies, the publicani were legal bodies independent of their members whose ownership was divided into shares, or partes. There is evidence that these shares were sold to public investors and traded in a type of over-the-counter market in the Forum, near the Temple of Castor and Pollux. The shares fluctuated in value, encouraging the activity of speculators, or quaestors.
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@rawkode
In 1602 … First IPO: Dutch East India Company
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@rawkode
In 1873 … First US IPO: Bank of North America
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@rawkode
In 1884 … What was the price of wheat?
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@rawkode
A Comparison of the Fluctuations in the Price of Wheat and in the Cotton and Silk Imports into Great Britain
First Documented Time Series
J. H. PoyntingJournal of the Statistical Society of LondonVol. 47, No. 1 (Mar., 1884), pp. 34-74
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@rawkode
What is all this?This is the first (or one of) paper that added the dimension of time to statistical mathematics
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@rawkode
Most data is best understood in the dimension of time
@pauldix, CTO
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Introduction to Time Series
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@rawkode
What Will We Cover?
➔ Time Series Data➔ Time Series Databases➔ Getting to Know InfluxDB➔ Value of Time Series Data➔ Advancing Monitoring with Time Series
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Time Series DataWhat is it?
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@rawkode
Time Series DataData with a timestamp
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@rawkode
Healthcheck Failed
Mem100%
DB MigrationRun
Scotland Qualify for World Cup
Pod KilledBy OOM Pod Restarted Git
Commit
V1.1.3 Deployed CPU12% CI Passed CI Started
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@rawkode
Healthcheck Failed
Mem100%
DB MigrationRun
Scotland Qualify for World Cup
Pod KilledBy OOM Pod Restarted Git
Commit
V1.1.3 Deployed CPU12% CI Passed CI Started
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@rawkode
Healthcheck Failed
Mem100%
DB MigrationRun
Scotland Qualify for World Cup
Pod KilledBy OOM Pod Restarted Git
Commit
V1.1.3 Deployed CPU12% CI Passed CI Started
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@rawkode
Healthcheck Failed
Mem100%
DB MigrationRun
Scotland Qualify for World Cup
Pod KilledBy OOM Pod Restarted Git
Commit
V1.1.3 Deployed CPU12% CI Passed CI Started
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@rawkode
Healthcheck Failed
Mem100%
DB MigrationRun
Scotland Qualify for World Cup
Pod KilledBy OOM Pod Restarted Git
Commit
V1.1.3 Deployed CPU12% CI Passed CI Started
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@rawkode
Healthcheck Failed
Mem100%
DB MigrationRun
Pod KilledBy OOM V1.1.3 Deployed
Time
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@rawkode
What is Time Series Data?
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@rawkode
Irregular (Events)
➔ Unpredictable➔ Inconsistent Intervals
What is Time Series Data?
Regular (Metrics)
➔ Predictable➔ Evenly Distributed
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@rawkode
Regular / Metrics★ CPU Usage★ Memory Usage★ Ping Time for Google.com★ Number of Processes
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@rawkode
Irregular / Events★ User Clicked Login★ Authentication Failed★ CI Published v1.3.1★ Network Cable Unplugged
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@rawkode
Metrics vs. EventsAll Metrics are an aggregation of events
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@rawkode
Collecting Metrics & EventsWith Prometheus Exporters or Telegraf
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@rawkode
Collecting Metrics & EventsInputs:➔ CloudWatch➔ Elasticsearch➔ Kafka➔ Jenkins➔ Kubernetes➔ Linux➔ Puppet➔ Windows➔ x509
Outputs:➔ CloudWatch➔ Kafka➔ DataDog➔ Elasticsearch➔ Graphite➔ Prometheus
Exporters:➔ Atlassian➔ Ceph➔ Consul➔ Kubernetes➔ Memcached➔ MySQL
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@rawkode
Push AND Pull
Metrics are pulled at a regular interval
Consistent and reliable intervals
Events NEED to be pushed as they happen
Inconsistent intervals
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@rawkode
Time Series DataUse Cases
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@rawkode
IoT / Sensor
➔ Thermostats➔ Electric Engines➔ Smart Things➔ GPS➔ Fitbits
Real Time Analytics
➔ Website Tracking
➔ Stock Prices➔ Currency
Exchange Rates
Use Cases for Time SeriesMonitoring
➔ Infrastructure➔ Applications➔ Third Party
Services
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Time Series DatabasesTSDB’s
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@rawkode
Time Series databases are optimized for collecting, storing, retrieving, and processing of Time Series data.
Time Series Databases
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@rawkode
➔ High Write Frequency
➔ Reads are range scans
➔ TTL / Lifecycle Management
➔ Time Sensitive
Time Series Databases
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@rawkode
12%Are you in the 88%?
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@rawkode
13%It’s Not Too Late!
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@rawkode
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@rawkode
DisclaimerMost of this isn’t unique to InfluxDB
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InfluxDBIntroductions
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@rawkode
InfluxDB➔ TSDB➔ Open-Source➔ FullStack (Telegraf,
InfluxDB, Chronograf, and Kapacitor)
➔ v2 …
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@rawkode
PointsAt any point in time, this value was N
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@rawkode
load,host=vm1 1m=6.32,5m=8.20,15m=9.55 123456789
Point● Series● Fields● Timestamp
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@rawkode
● load,host=vm1
● stock_price,market=NASDAQ,ticker=GOOG
● users,service=comments
Series● Name● Tag Keys● Tag Values
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@rawkode
stock_price,market=NASDAQ,ticker=GOOG
stock_price,market=NASDAQ,ticker=APPL
Series● Name● Tag Keys● Tag Values
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@rawkode
Fields
➔ Not Indexed➔ Multiple Data Types
Tags & Fields
Tags
➔ Indexed➔ String Types
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Value of Time Series Data
Isn’t It Valuable Forever?
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@rawkode
Resolution
The predictable interval at which we will collect our time series data
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@rawkode
The value of all time series data is directly correlated with the resolution that the data is available
Value of Time Series Data
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Cost of Time Series Data
Wait, Isn’t It Free?!
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@rawkode
Examplecpu,machine=abc1 usage=1.66 timestamp
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@rawkode
Resolution➔ 1 Measurement➔ 1 Series➔ 1s Resolution
86400 PointsPer Day
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@rawkode
Resolution➔ 1 Measurement➔ 2 Series➔ 1s Resolution
172800 PointsPer Day
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@rawkode
Resolution 4320000 PointsPer Day
➔ 5 Measurement➔ 10 Series➔ 1s Resolution
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@rawkode
Nasdaq 285120000000
PointsPer Day
➔ 1 Measurement➔ 3300 Series➔ 1ms Resolution
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@rawkode
Nasdaq 4752000 PointsPer Day
➔ 1 Measurement➔ 3300 Series➔ 1m Resolution
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@rawkode
Nasdaq 79200 PointsPer Day
➔ 1 Measurement➔ 3300 Series➔ 1h Resolution
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@rawkode
Nasdaq 13200 PointsPer Day
➔ 1 Measurement➔ 3300 Series➔ 6h Resolution
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@rawkode
DownsamplingLowering the Resolution
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@rawkode
Rollups with Continuous QueriesCREATE CONTINUOUS QUERY "rollup_1h" ON "nasdaq"
BEGIN SELECT mean(price) INTO yearly FROM weekly GROUP BY time(1h)END
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@rawkode
Events?Outlier / Anomaly Detection
InfluxDB Anomaly Detection
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Advancing Monitoring with Time Series
Taking Small Steps for Giant Leaps
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Application
Database
CPU > 80% MEM > 80%
Response Time > 300ms
Black Friday
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Application
Database
How do we know when to send a page to SRE / Ops?
When the application fails the health-check
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Application
DatabaseHow do we know when to send a page to SRE / Ops?
When we get more than 100 [ 5xx | Exceptions ] within a 5
minute period
ApplicationApplication
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Service A
Database A
Service B Service B Service C
Database B Database C
Virtual Network Service Mesh
Canary
Ummm?
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@rawkode
Cloud Native Architectures
Convenience Vs. Cost
You can treat the symptoms for a while …
Upgrade Your Monitoring
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@rawkode
CausalityTreating the Disease
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@rawkode
➔ Look at last weeks, months, and years of data➔ Use tags to build correlation➔ Get Statistical
◆ INTEGRAL()◆ LINEAR_PREDICTION()◆ DERIVATIVE()◆ MAD()◆ MOVING_AVERAGE()◆ HOLT_WINTERS()
Causality
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@rawkode
Have you ever been paged at 4am because the disk usage of a machine went above 85%?
Could this have been determined during office hours? (Linear Growth)
Can we use correlations to determine the cause during anomalies?
Causality
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@rawkode
In our distributed application, our p99 reports that our users are being served healthy responses in under 2ms. Our pager is going off because we’ve getting too many exceptions in the code
histogram(bins: [...])
Causality
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@rawkode
Beware
http_request_duration_seconds_bucket{le="0.3"}http_request_duration_seconds_bucket{le="0.5"}http_request_duration_seconds_bucket{le="1.0"}
http_request_duration_seconds_bucket{le="+Inf"}
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@rawkode
In our distributed application, our p99 reports that our users are being served healthy responses in under 2ms. Our pager is going off because we’ve getting too many exceptions in the code
histogram()|> mode(*)
Causality
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@rawkode
We run Big News Corp and we need to reduce our cloud costs. Instead of running at 30% utilisation, can we run at 80% utilisation?
HOLT_WINTERS
Proactive Ops
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@rawkode
Build AutomationThrough Causality, Historical Data, Prediction, and ML
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@rawkode
➔ Use a TSDB
➔ Understand Cost / Select Tags Wisely
➔ Understand the resolution you need for 1m, 6m, > 12m
Summary
➔ Rollup metrics
➔ Perform outlier detection on events
➔ Build automation, dashboarding, and reporting around your data (past, present, and future)
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@rawkode
Cheers!David McKay@rawkode
Developer Advocate@InfluxDB | #InfluxDB
That’s All Folks!