Apache Eagle is an Open Source Monitoring Platform for Hadoop eco-system, which started with monitoring data activities in Hadoop. It can instantly identify access to sensitive data, recognize attacks/malicious activities and blocks access in real time.

2. 2
Apache Eagle
Monitor Hadoop in Real Time
Yong Zhang | Senior Architect |
yonzhang2012@gmail.com
Arun Manoharan | Senior Product Manager | @lycos_86

3. Big Data @ eBay
800M
Listings *
159M
Global Active Buyers *
*Q3 2015 data
7
Hadoop Clusters*
800M
HDFS operations
(single cluster)*
120 PB
Data*
Hadoop @ eBay

4. HADOOP SECURITY
Authorization &
Access Control
Perimeter
Security
Data
Classification
Activity
Monitoring
Security
MDR
• Perimeter Security
• Authorization &
Access Control
• Discovery
• Activity Monitoring
Security for Hadoop

5. Who is accessing the data?
What data are they accessing?
Is someone trying to access data that they don’t have access to?
Are there any anomalous access patterns?
Is there a security threat?
How to monitor and get notified during or prior to an anomalous event occurring?
Motivation

6. Apache Eagle
Apache Eagle: Monitor Hadoop in Real Time
Apache Eagle is an Open Source Monitoring Platform for Hadoop
eco-system, which started with monitoring data activities in
Hadoop. It can instantly identify access to sensitive data, recognize
attacks/malicious activities and blocks access in real time.
In conjunction with components such as Ranger, Sentry,
Knox, DgSecure and Splunk etc., Eagle provides
comprehensive solution to secure sensitive data stored in
Hadoop.

7. Apache Eagle Composition
Apache Eagle
Integrations Alert Engine
HDFS
AUDIT
HIVE
QUERY
HBASE
AUDIT
CASSANDRA
AUDIT
MapR
AUDIT
2 HADOOP
Performance
Metric
Namenode
JMX
Metrics
Datanode
JMX
Metrics
System
Metrics
3 M/R Job
Performance
Metric
History Job
Metrics
Running
Job Metrics
4 Spark Job
Performance
Metric
Spark Job
Metrics
Queue
Metrics
1 Data Activity
Monitoring
RM
JMX
Metrics
1 Policy Store
2 Metadata API
3 Scalability
4 Extensibility
[Domains] [Applications]

8. More Integrations
•Cassandra
•MapR
•Mongo DB
•Job
•Queue

9. Extensibility
 Ranger
• As remediation engine
• As generic data source
 DgSecure
• Source of truth for data classification
 Splunk
• Syslog format output
• EAGLE alert output is the 1st abstraction of analytics and Splunk is
the 2nd abstraction

10. Eagle Architecture

11. Highlights
1. Turn-key integration: after installation, user defines rules
2. Comprehensive rules on high volume of data: Eagle solves some
unique problem in Hadoop
3. Hot deploy rule: Eagle does not provide a lot of charts, instead it
allows user to write ad-hoc rule and hot deploy it.
4. Metadata driven: kept in mind, here metadata includes policy, event
schema and UI component etc.
5. Extensibility: Keep in mind that Eagle can’t succeed alone, Eagle has to
be integrated with other system for example data classification, policy
enforcement etc.
6. Monolithic storm topology: application pre-processing are running
together with alert engine.

12. Example 1: Integration with HDFS AUDIT log
• Ingestion
 KafkaLog4jAppender+Kafk
a
 Logstash+Kafka
• Partition
 By user
• Pre-processing
 Sensitivity join
 Command re-assembler
Namenode
Kafka
Partition_1
Kafka
Partition_2
Kafka
Partition_N
Storm
Kafka
Spout
User1 User1
Alert
Executor_1
Alert
Executor_2
Alert
Executor_K
User2 User2
User1
User2

13. Data Classification - HDFS
•Browse HDFS file system
•Batch import sensitivity metadata through Eagle API
•Manually mark sensitivity in Eagle UI

14.  One user command generates multiple HDFS audit events
 Eagle does reverse engineering to figure out original user command
 Example
COPYFROMLOCAL_PATTERN = “every a = eventStream[cmd==‘getfileinfo’] ” +
“-> b = eventStream[cmd==‘getfileinfo’ and user==a.user and src==str:concat(a.src,‘._COPYING_’)] ” +
“-> c = eventStream[cmd==‘create’ and user==a.user and src==b.src] ” +
“-> d = eventStream[cmd==‘getfileinfo’ and user==a.user and src==b.src] ” +
“-> e = eventStream[cmd==‘delete’ and user==a.user and src==a.src] ” +
“-> f = eventStream[cmd==‘rename’ and user==a.user and src==b.src and dst==a.src]”
2015-11-20 00:06:47,090 INFO FSNamesystem.audit: allowed=true ugi=root (auth:SIMPLE) ip=/10.0.2.15 cmd=getfileinfo src=/tmp/private dst=null perm=null proto=rpc
2015-11-20 00:06:47,185 INFO FSNamesystem.audit: allowed=true ugi=root (auth:SIMPLE) ip=/10.0.2.15 cmd=getfileinfo src=/tmp/private._COPYING_ dst=null perm=null
proto=rpc
2015-11-20 00:06:47,254 INFO FSNamesystem.audit: allowed=true ugi=root (auth:SIMPLE) ip=/10.0.2.15 cmd=create src=/tmp/private._COPYING_ dst=null
perm=root:hdfs:rw-r--r-- proto=rpc
2015-11-20 00:06:47,289 INFO FSNamesystem.audit: allowed=true ugi=root (auth:SIMPLE) ip=/10.0.2.15 cmd=getfileinfo src=/tmp/private._COPYING_ dst=null perm=null
proto=rpc
2015-11-20 00:06:47,609 INFO FSNamesystem.audit: allowed=true ugi=root (auth:SIMPLE) ip=/10.0.2.15 cmd=delete src=/tmp/private dst=null perm=null proto=rpc
2015-11-20 00:06:47,624 INFO FSNamesystem.audit: allowed=true ugi=root (auth:SIMPLE) ip=/10.0.2.15 cmd=rename src=/tmp/private._COPYING_ dst=/tmp/private
perm=root:hdfs:rw-r--r-- proto=rpc
User Command Re-assembly

15. • Policy evaluation is stateful (one user’s data has to go to one physical bolt)
• Partition by user all the way (hash)
• User is not balanced at all
• Greedy algorithm https://en.wikipedia.org/wiki/Partition_problem#The_greedy_algorithm
Data Skew Problem

16.  Policy weight is not even
• Regex policy is CPU intensive
• Window based policy is Memory intensive
Computation Skew Problem

17. Example 2: Integration with Hive
• Ingestion
 Yarn API
• Partition
 user
• Pre-
processing
 Sensitivity join
 Hive SQL parser

18. Data Classification - Hive
•Browse Hive databases/tables/columns
•Batch import sensitivity metadata through Eagle API
•Manually mark sensitivity in Eagle UI

19. Eagle Alert Engine Overview
1 Runs CEP engine on Apache
Storm
• Use CEP engine as library (Siddhi CEP)
• Evaluate policy on streamed data
• Rule is hot deployable
2 Inject policy dynamically
• API
• Intuitive UI
3 Scalability
• Computation
# of policies (policy placement)
• Storage
# of events (event partition)
4 Extensibility for policy
enforcement
• Post-alert processing with plugin

20. Run CEP Engine on Storm
Storm Bolt
CEP
Worker
CEP
Worker
CEP
Worker
… …
Policy
Check
Thread
Policy
Store
Metadata API
event1
event1
event1
event1
policy1,2,3,4,5,6policy1,2,3
policy1
policy2
policy3
Storm Bolt
event1
policy4,5,6
event schema

21. Primitives – event, policy, alert
Raw Event
2015-10-11 01:00:00,014 INFO FSNamesystem.audit: allowed=true
ugi=user_tom@sandbox.hortonworks.com (auth:KERBEROS) ip=/10.0.0.1 cmd=getfileinfo
src=/tmp/private dst=null perm=null
Alert Event
Timestamp, cmd, src, dst, ugi, sensitivityType, securityZone
Policy
viewPrivate: from hdfsAuditLogEventStream[(cmd=='getfileinfo') and (src=’/tmp/private’)]
Alert
2015-10-11 01:00:09[UTC] hdfsAuditLog viewPrivate user_tom/10.0.0.1 The Policy "viewPrivate" has
been detected with the below information: timestamp="1445993770932" allowed="true"
cmd="getfileinfo" host="/10.0.0.1" sensitivityType="PRIVATE" securityZone="NA" src="/tmp/private"
dst="NA" user=“user_tom”

22. Event Schema
• Modeling event

23. 1 Single event evaluation
• threshold check with various conditions
Policy Capabilities
2 Event window based evaluation
• various window semantics (time/length sliding/batch window)
• comprehensive aggregation support
3 Correlation for multiple event
streams
• SQL-like join
4 Pattern Match and
Sequence
• a happens followed by b
Powered by Siddhi 3.0.5, but Eagle provides dynamic capabilities
and intuitive API/UI

24. 1 Namenode master/slave lag
from every a =
hadoopJmxMetricEventStream[metric=="hadoop.namenode.journaltransaction.lastappliedorwrittent
xid"] -> b = hadoopJmxMetricEventStream[metric==a.metric and b.host != a.host and
(max(convert(a.value, "long")) + 100) <= max(convert(value, "long"))] within 5 min select a.host as
hostA, a.value as transactIdA, b.host as hostB, b.value as transactIdB insert into tmp;
Some policy examples
3 Namenode HA state change
from every a = hadoopJmxMetricEventStream[metric=="hadoop.namenode.hastate.active.count"] ->
b = hadoopJmxMetricEventStream[metric==a.metric and b.host == a.host and (convert(a.value,
"long") != convert(value, "long"))] within 10 min select a.host, a.value as oldHaState, b.value as
newHaState, b.timestamp as timestamp, b.metric as metric, b.component as component, b.site as
site insert into tmp;
2 Namenode last checkpoint time
• from hadoopJmxMetricEventStream[metric == "hadoop.namenode.dfs.lastcheckpointtime" and
(convert(value, "long") + 18000000) < timestamp] select metric, host, value, timestamp,
component, site insert into tmp;

25. Define policy in UI and API
curl -u ${EAGLE_SERVICE_USER}:${EAGLE_SERVICE_PASSWD} -X POST -H 'Content-
Type:application/json'
"http://${EAGLE_SERVICE_HOST}:${EAGLE_SERVICE_PORT}/eagle-
service/rest/entities?serviceName=AlertDefinitionService"
-d '
[
{
"prefix": "alertdef",
"tags": {
"site": "sandbox",
"application": "hadoopJmxMetricDataSource",
"policyId": "capacityUsedPolicy",
"alertExecutorId": "hadoopJmxMetricAlertExecutor",
"policyType": "siddhiCEPEngine"
},
"description": "jmx metric ",
"policyDef": "{"expression":"from hadoopJmxMetricEventStream[metric ==
"hadoop.namenode.fsnamesystemstate.capacityused" and convert(value,
"long") > 0] select metric, host, value, timestamp, component, site insert into
tmp; ","type":"siddhiCEPEngine"}",
"enabled": true,
"dedupeDef": "{"alertDedupIntervalMin":10,"emailDedupIntervalMin":10}",
"notificationDef":
"[{"sender":"eagle@apache.org","recipients":"eagle@apache.org","subject
":"missing block
found.","flavor":"email","id":"email_1","tplFileName":""}]"
}
]
'
1 Create policy using API 2 Create policy using UI

26. Scalability
•Scale with # of events
•Scale with # of policies

27. Statistics
• # of events evaluated per
second
• audit for policy change
Eagle Service
As of 0.3.0, Eagle stores metadata and statistics into HBASE, and
support Druid as metric store.
Metadata
• Policy
• Event schema
• Site/Application/UI Features
HBASE
• Store metrics
• Store M/R job/task data
• Rowkey design for time-series data
• HBase Coprocessor
Raw data
• Druid for metric
• HBASE for M/R job/task
etc.
• ES for log (future)
1 Data to be stored 2 Storage 3 API/UI
Druid
• Consume data from Kafka
HBASE
• filter, groupby, sort, top
Druid
• Druid query API
• Dashboard in Eagle

28. Alert Engine Limitations in Eagle 0.3
1 High cost for integrating
• Coding for onboarding new data source
• Monolithic topology for pre-processing and alert
3 Policy capability restricted by event
partition
• Can’t do ad-hoc group-by policy expression
For example from groupby user to groupby cmd
2 Not multi-tenant
• Alert engine is embedded into application
• Many separate Storm topologies
4 Correlation is not declarative
• Coding for correlating existing data sources
If traffic is partitioned by user, policy only
supports expression of user based group-by
One storm topology even for one trivial data
source
Even if it is a simple data source, you have
to write storm topology and then deploy
Can’t declare correlations for multiple
metrics
5 Stateful policy evaluation
• fail over when bolt is down
How to replay one week history data when
node is down

29. Eagle Next Releases
• Improve User experience
 Remote start storm topology
 Metadata stored in RDBMS
Eagle 0.4 Eagle 0.5
• Alert Engine as Platform
 No monolithic topology
 Declarative data source onboard
 Easy correlation
 Support policies with any field group-by
 Elastic capacity management

30. USER PROFILE ALGORITHMS…
Eigen Value Decomposition
• Compute mean and variance
• Compute Eigen Vectors and determine Principal Components
• Normal data points lie near first few principal components
• Abnormal data points lie further from first few principal components and
closer to later components

31. USER PROFILE ARCHITECTURE

32. dev@eagle.incubator.apache.org
http://eagle.incubator.apache.org
https://github.com/apache/incubator-eagleGithub
Dev Mail
List
@TheApacheEagleTwitter
Q & A