We’ll present details about Argus, a time-series monitoring and alerting platform developed at Salesforce to provide insight into the health of infrastructure as an alternative to systems such as Graphite and Seyren.

1. Argus Production
Monitoring At
Salesforce
Service Health & Observability at Scale
Tom Valine
Director, Infrastructure Engineering
tvaline@salesforce.com
in/tvaline
Bhinav Sura
Software Engineer, Infrastructure Engineering
bhinav.sura@salesforce.com
in/bhinavsura

2. What is Argus?
● Time Series Data & Events
● Inbuilt Service Protection
● Alerting
● Flexible Dashboarding
● Full REST API
● High Throughput
● Low Latency
● Horizontally Scalable
● In Use By
○ Capacity Planning
○ Search
○ Feature Teams
○ Site Reliability
○ Customer Success

3. But Why Another Monitoring System?
● Technology changes
frequently!
● Insulate our customers
● Performance
● Trust
● Programmatic access for
everything
● Multi-tenancy
● Correlation with non-
timeseries data
● Highly dimensional

4. I’ve seen this somewhere before...
Metrics
● Transforms
● Namespace
● Scope
● Name
● Tags
● Aggregator
● Downsampler
Events
● Namespace
● Scope
● Name
● Tags
● Type
● User
SCALE(-2d:-1d:dva:argus:freemem{host=*}:min:1d-min, $1e-6)
TRANSFORM
START
END
NAMESPACE
SCOPE
METRIC
TAGS
AGG
DS
PARAMS
-2d:-1d:dva:argus:release{host=*}:major:admin
START
END
NAMESPACE
SCOPE
NAME
TAGS
TYPE
USER

5. ● First Class Data
● Decoupled from Time
Series
● Multiple Events Per
Timestamp
● Event Categories
● Identifiable per User
● Overlay on Any Time
Series
Events

6. Alerting
● CRON Format
● Alert on Missing Data
● Single Ended & Range
Comparisons
● Inertia
● Cooldown
● Multiple Triggers
● Multiple Notifications
○ Audit
○ Email
○ GOC++
○ Salesforce Chatter
○ PagerDuty
● Event Backannotation

7. Warden
● Policy Driven Suspension
Mechanism
● Per User
● Application & Subsystem
● Progressively Punitive
● Indefinite Suspension
Supported
● Customizeable

8. Dashboarding
● Maintaining dashboards is
a horrible business to be
in
● Empower the users, get
out of their way
● Markup based
● Custom tags for
visualization elements
● HTML for everything else

9. REST
● API First
● All functionality exposed
via services
● Decoupled UI
● Authenticated
○ Login
○ Do stuff
○ Logout
● Get out of User's Way!
○ Orchestra Client
○ ArgusPoke
○ Dashboard Creation
Tool

10. How does it work?
METRICS ANNOTATION USER ENTITY
ALERTS MAIL SCHEDULING MONITORING
WEB SERVICES
AUTH ORM MQ TSDB
WEB UI CUSTOM APPS OTHER CLIENTS
DASHBOARD MANAGEMENT WARDEN NAMESPACE
SCHEMA WILDCARDING CACHING INTERLOCK

11. Okay, but how does it REALLY work?
MESSAGE BUS
HBASE/TSDB/RDBMS/CACHING
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12. Cool, how will it evolve going forward?
HBASE/TSDB/RDBMS/CACHE
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HBASE/TSDB/RDBMS/CACHE HBASE/TSDB/RDBMS/CACHE HBASE/TSDB/RDBMS/CACHE HBASE/TSDB/RDBMS/CACHE
ROUTE/FORK/JOIN+M/R
ROUTE/FORK/JOIN+M/R
MESSAGE BUS MESSAGE BUS MESSAGE BUS MESSAGE BUS MESSAGE BUS
ROUTE/FORK/JOIN+M/R
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13. Alert Evaluation Data Flows
Message Queue:
1. Scheduling Service updates
alert schedule every 10 minutes.
2. Scheduler submits scheduled
jobs to queue
3. Minimum interval of 1 minute
Alert Client:
1. Dequeues from alert queue.
2. Query ranges adjusted for
scheduling latency
3. Triggers evaluated
4. Notifications sent
5. Cooldowns updated.
ALERT DATA STORE
SCHEUDLING
SERVICE
ALERT CACHE
ARGUS WS
ALERT 8713
...
ALERT 4141
ALERT 9810

14. Metric & Event Data Flows
Message Queue:
1. Writes are asynchronous with high
degree of parallelism.
2. Queue used as a shock absorber.
Tolerant to lower level
failures/downtime.
3. Kafka for scalability. One topic each
for metrics and annotations.
Number of partitions in the order of
100s.
ArgusMetricsQueue:
1. Consumed by 2 types of clients:
MetricCommit and SchemaCommit
2. MetricCommit client commits the
actual time series data to persistent
storage (using OTSDB or Phoenix).
3. SchemaCommit client only uses the
metric metadata to create metric
schema records and commits them
to HBase (using AsyncHBase).
TIMESERIES STORE
ARGUS WS
METRIC
...
METRIC
METRIC
METRIC SERVICE
SCHEMA STORE

15. TSDB Service Implementation - OpenTSDB
● Uses HBase underneath
● RowKey: <metric_uid><timestamp><tagk1><tagv1>[...<tagkn><tagvn>].
● Stores actual time series values on hourly boundaries (All values within an hour stored in the
same cell)
● Pros:
○ Extremely fast when you query using complete metric name.
○ 5M datapoints/min write throughput per write daemon.
● Cons:
○ Tag Cardinality - Total number of tags per metric is limited to 8
○ Tag Cardinality - As product of tag values across all tag keys increases, performance decreases
drastically
○ UID Exhaustion - 16M UIDs each for metric, tagk and tagv names by default. Once these are
exhausted, no new metrics, tagk or tagv can be created.

16. TSDB Service Implementation - Phoenix
● Uses HBase underneath
● RowKey: <metric_uid><timestamp><tagv1>[...<tagvn>].
● Metric modeled as Phoenix VIEW
○ Schema is introspectable and managed outside of data
○ Supports secondary indexes on value and/or tag(s)
● Parallelizes query and pushes computation to server
○ Server-side aggregation conserves network bandwidth
○ Allows SKIP_SCAN filter optimization for minimizing data scanned
○ Leverages ROW_TIMESTAMP optimization for filtering HFiles
● Performance on par or better than OpenTSDB
● Ad hoc SQL query capability
○ Join against other Phoenix tables
● Longer term leverage Drillix (Phoenix + Drill)
○ Cross cluster queries
○ Joins to other non HBase data sources

17. Schema Service Motivation
● Discover Metrics
○ What all metrics exist within a scope?
○ For a given <scope, metric> combination, what all tags exist?
○ Given a metric, what all scopes contain this metric?
○ What are all the tag values that exist for a given tag key?
● Support Wildcard Queries
○ Non-wildcard query
■ -1h:system.myDatacenter.myPod:Cpu.perc:avg:1m-avg
○ Wildcard query
■ -1h:system.myDatacenter.*:Cpu.perc:avg:1m-avg
■ -1h:system.myDatacenter.myPod:Cpu*:avg:1m-avg
■ -1h:system.myDatacenter.myPod:Cpu.perc{device=*app*}:avg:1m-avg

18. Schema Service Implementation
● AsyncHBase Schema Service:
○ Uses HBase underneath
○ SchemaRecord: namespace, scope, metricname, tagk, tagv. No data points.
○ Each record indexed in 2 ways in 2 different tables.
○ MetricIndexed schema table:
■ RowKey: <metricname><scope><namespace><tagk><tagv>
○ ScopeIndexed schema table:
■ RowKey: <scope><metricname><namespace><tagk><tagv>
○ Decide what table to use based on the type of query.
○ Pros:
■ Efficient retrieval for schema records for most types of queries
○ Cons:
■ Storage duplication
● DiscoveryService:
○ Uses SchemaService internally
○ Ability to filter records by type
■ For e.g. Filter all unique scopes that match *myScope*
○ Expand Wildcard query and return a collection of non-wildcard queries

19. Caching
● CachedTSDB Service:
○ Uses RedisCache service and the configured TSDBService implementation (OpenTSDB or
PhoenixTSDB)
○ Query Level Caching (caches synthetic data)
○ Caches data spanning a window of more than last 24 hours.
○ Data is cached by fracturing it on day boundary.
■ For e.g.: Query spanning 5 days is stored using 5 keys on the cache.
○ Support for partial hits
○ Cache expiry time of an hour (can be increased by running a separate Cache update process)
● CachedDiscovery Service:
○ Uses RedisCache service and the configured DiscoveryService implementation
○ Cache queries already expanded
○ Cache expiry time of a day

20. Developed By
● Anand Subramanian
● Bhinav Sura
● Tom Valine
● Jigna Bhatt
● Ruofan Zhang
● Dilip Devaraj
● Raj Sarkapally
● Kiran Gowdru
More Information
​https://github.com/SalesforceEng/Argus

21. thank y u