Added: eagle/site/docs/v0.5.0/mkdocs/search_index.json
URL:
http://svn.apache.org/viewvc/eagle/site/docs/v0.5.0/mkdocs/search_index.json?rev=1789961&view=auto
==============================================================================
--- eagle/site/docs/v0.5.0/mkdocs/search_index.json (added)
+++ eagle/site/docs/v0.5.0/mkdocs/search_index.json Mon Apr 3 11:29:31 2017
@@ -0,0 +1,739 @@
+{
+ "docs": [
+ {
+ "location": "/",
+ "text": "What is Eagle\n\n\n Apache Eagle \n (incubating) is a
highly extensible, scalable monitoring and alerting platform, designed with its
flexible application framework and proven big data technologies, such as Kafka,
Spark and Storm. It ships a rich set of applications for big data platform
monitoring, e.g. HDFS/HBase/YARN service health check, JMX metrics, daemon
logs, audit logs and yarn applications. External Eagle developers can define
applications to monitoring their NoSQLs or Web Servers, and publish to Eagle
application repository at your own discretion. It also provides the
state-of-art alert engine to report security breaches, service failures, and
application anomalies, highly customizable by the alert policy definition.
\n\n\n\n\nTerminology\n\n\nSite\n\n\n\n\nA virtual concept in Apache Eagle. You
can use it to manage a group of application instances, and distinguish the
applications if you have a certain application installed for multiple
times.\n\n\n\
n\nApplication\n\n\n\n\nApplication(or Monitoring Application) is the
first-class citizen in Apache Eagle, it stands for an end-to-end
monitoring/alerting solution, which usually contains the monitoring source
onboarding, source schema specification, alerting policy and dashboard
definition.\n\n\n\n\nStream\n\n\n\n\nStream is the input for Alert Engine, each
Application should have its own stream to be defined by the developer. Usually,
it will have a POJO-like structure included in the stream definition. Once it's
defined, Application should have the logic to write data into
Kafka.\n\n\n\n\nData Activity Monitoring\n\n\n\n\nA built-in monitoring
application to monitor HDFS/HBase/Hive operations, and allow users to define
certain policies to detect sensitive data access and malicious data operations
in real-time.\n\n\n\n\nAlert Engine\n\n\n\n\nA specific built-in application
shared for all other monitoring applications, it reads data from Kafka, and
processes the data by applying th
e policy in real-time manner, and generates alert notification. So we call
this application as the Alert Engine.\n\n\n\n\nPolicy\n\n\n\n\nA rule used by
Alert Engine to match the data input from Kafka. Policy is defined in
\nSiddhiQL\n format.\n\n\n\n\nAlert\n\n\n\n\nIf any data input to Alert Engine
meets the policy, the Alert Engine will generate a message and publish it
through alert publisher. We call such messages as the alerts.\n\n\n\n\nAlert
Publisher\n\n\n\n\nIt will publish the alert to external channels which can be
the SMTP channel, the Kafka channel, Slack channel or other storage
systems.\n\n\n\n\nKey Qualities\n\n\nExtensible\n\n\n\n\nApache Eagle built its
core framework around the application concept, application itself includes the
logic for monitoring source data collection, pre-processing and normalization.
Developer can easily develop his own out-of-box monitoring applications using
Eagle's application framework, and deploy into
Eagle.\n\n\n\n\nScalable\n\n\n\n\n
The Eagle core team has chosen the proven big data technologies to build its
fundamental runtime, and apply a scalable core to make it adaptive according to
the throughput of data stream as well as the number of monitored
applications.\n\n\n\n\nReal-time\n\n\n\n\nStorm or Spark Streaming based
computing engine allow us to apply the policy to data stream and generate
alerts in real-time manner.\n\n\n\n\nDynamic\n\n\n\n\nThe user can freely
enable or disable a monitoring application without restarting the service.
Eagle user can dynamically add/delet/change their alert policies without any
impact to the underlying runtime.\n\n\n\n\nEasy-of-Use\n\n\n\n\nUser can enable
the monitoring for a service within minutes effort by just choosing the
corresponding monitoring application and configuring few parameters for the
service.\n\n\n\n\nNon-Invasive\n\n\n\n\nApache Eagle uses the out-of-box
applications to monitor services, you don't need any change to your existing
services.\n\n\n\n\n\n\nU
se Case Examples\n\n\nData Activity Monitoring\n\n\n\n\n\n\nData activity
represents how user explores data provided by big data platforms. Analyzing
data activity and alerting for insecure access are fundamental requirements for
securing enterprise data. As data volume is increasing exponentially with
Hadoop, Hive, Spark technology, understanding data activities for every user
becomes extremely hard, let alone to alert for a single malicious event in real
time among petabytes streaming data per day.\n\n\n\n\n\n\nSecuring enterprise
data starts from understanding data activities for every user. Apache Eagle
(incubating, called Eagle in the following) has integrated with many popular
big data platforms e.g. Hadoop, Hive, Spark, Cassandra etc. With Eagle user can
browse data hierarchy, mark sensitive data and then create comprehensive policy
to alert for insecure data access.\n\n\n\n\n\n\nJob Performance
Analysis\n\n\n\n\n\n\nRunning map/reduce job is the most popular way people use
t
o analyze data in Hadoop system. Analyzing job performance and providing
tuning suggestions are critical for Hadoop system stability, job SLA and
resource usage etc.\n\n\n\n\n\n\nEagle analyzes job performance with two
complementing approaches. First Eagle periodically takes snapshots for all
running jobs with YARN API, secondly Eagle continuously reads job lifecycle
events immediately after the job is completed. With the two approaches, Eagle
can analyze single job's trend, data skew problem, failure reasons etc. More
interestingly, Eagle can analyze whole Hadoop cluster's performance by taking
into account all jobs.\n\n\n\n\n\n\nCluster Performance
Analytics\n\n\n\n\n\n\nIt is critical to understand why a cluster performs bad.
Is that because of some crazy jobs recently on-boarded, or huge amount of tiny
files, or namenode performance degrading?\n\n\n\n\n\n\nEagle in realtime
calculates resource usage per minute out of individual jobs, e.g. CPU, memory,
HDFS IO bytes, HDFS IO numO
ps etc. and also collects namenode JMX metrics. Correlating them together will
easily help system administrator find root cause for cluster
slowness.\n\n\n\n\n\n\n\n\nDisclaimer\n\n\n\n\nApache Eagle now is being
incubated, and therefore, across the whole documentation site, all appearances
of case-insensitive word \neagle\n and \napache eagle\n represent \nApache
Eagle (incubating)\n. This could be seen as a part of disclaimer.",
+ "title": "Home"
+ },
+ {
+ "location": "/#what-is-eagle",
+ "text": "Apache Eagle (incubating) is a highly extensible,
scalable monitoring and alerting platform, designed with its flexible
application framework and proven big data technologies, such as Kafka, Spark
and Storm. It ships a rich set of applications for big data platform
monitoring, e.g. HDFS/HBase/YARN service health check, JMX metrics, daemon
logs, audit logs and yarn applications. External Eagle developers can define
applications to monitoring their NoSQLs or Web Servers, and publish to Eagle
application repository at your own discretion. It also provides the
state-of-art alert engine to report security breaches, service failures, and
application anomalies, highly customizable by the alert policy definition.",
+ "title": "What is Eagle"
+ },
+ {
+ "location": "/#terminology",
+ "text": "",
+ "title": "Terminology"
+ },
+ {
+ "location": "/#site",
+ "text": "A virtual concept in Apache Eagle. You can use it to
manage a group of application instances, and distinguish the applications if
you have a certain application installed for multiple times.",
+ "title": "Site"
+ },
+ {
+ "location": "/#application",
+ "text": "Application(or Monitoring Application) is the first-class
citizen in Apache Eagle, it stands for an end-to-end monitoring/alerting
solution, which usually contains the monitoring source onboarding, source
schema specification, alerting policy and dashboard definition.",
+ "title": "Application"
+ },
+ {
+ "location": "/#stream",
+ "text": "Stream is the input for Alert Engine, each Application
should have its own stream to be defined by the developer. Usually, it will
have a POJO-like structure included in the stream definition. Once it's
defined, Application should have the logic to write data into Kafka.",
+ "title": "Stream"
+ },
+ {
+ "location": "/#data-activity-monitoring",
+ "text": "A built-in monitoring application to monitor
HDFS/HBase/Hive operations, and allow users to define certain policies to
detect sensitive data access and malicious data operations in real-time.",
+ "title": "Data Activity Monitoring"
+ },
+ {
+ "location": "/#alert-engine",
+ "text": "A specific built-in application shared for all other
monitoring applications, it reads data from Kafka, and processes the data by
applying the policy in real-time manner, and generates alert notification. So
we call this application as the Alert Engine.",
+ "title": "Alert Engine"
+ },
+ {
+ "location": "/#policy",
+ "text": "A rule used by Alert Engine to match the data input from
Kafka. Policy is defined in SiddhiQL format.",
+ "title": "Policy"
+ },
+ {
+ "location": "/#alert",
+ "text": "If any data input to Alert Engine meets the policy, the
Alert Engine will generate a message and publish it through alert publisher. We
call such messages as the alerts.",
+ "title": "Alert"
+ },
+ {
+ "location": "/#alert-publisher",
+ "text": "It will publish the alert to external channels which can
be the SMTP channel, the Kafka channel, Slack channel or other storage
systems.",
+ "title": "Alert Publisher"
+ },
+ {
+ "location": "/#key-qualities",
+ "text": "",
+ "title": "Key Qualities"
+ },
+ {
+ "location": "/#extensible",
+ "text": "Apache Eagle built its core framework around the
application concept, application itself includes the logic for monitoring
source data collection, pre-processing and normalization. Developer can easily
develop his own out-of-box monitoring applications using Eagle's application
framework, and deploy into Eagle.",
+ "title": "Extensible"
+ },
+ {
+ "location": "/#scalable",
+ "text": "The Eagle core team has chosen the proven big data
technologies to build its fundamental runtime, and apply a scalable core to
make it adaptive according to the throughput of data stream as well as the
number of monitored applications.",
+ "title": "Scalable"
+ },
+ {
+ "location": "/#real-time",
+ "text": "Storm or Spark Streaming based computing engine allow us
to apply the policy to data stream and generate alerts in real-time manner.",
+ "title": "Real-time"
+ },
+ {
+ "location": "/#dynamic",
+ "text": "The user can freely enable or disable a monitoring
application without restarting the service. Eagle user can dynamically
add/delet/change their alert policies without any impact to the underlying
runtime.",
+ "title": "Dynamic"
+ },
+ {
+ "location": "/#easy-of-use",
+ "text": "User can enable the monitoring for a service within
minutes effort by just choosing the corresponding monitoring application and
configuring few parameters for the service.",
+ "title": "Easy-of-Use"
+ },
+ {
+ "location": "/#non-invasive",
+ "text": "Apache Eagle uses the out-of-box applications to monitor
services, you don't need any change to your existing services.",
+ "title": "Non-Invasive"
+ },
+ {
+ "location": "/#use-case-examples",
+ "text": "",
+ "title": "Use Case Examples"
+ },
+ {
+ "location": "/#data-activity-monitoring_1",
+ "text": "Data activity represents how user explores data provided
by big data platforms. Analyzing data activity and alerting for insecure access
are fundamental requirements for securing enterprise data. As data volume is
increasing exponentially with Hadoop, Hive, Spark technology, understanding
data activities for every user becomes extremely hard, let alone to alert for a
single malicious event in real time among petabytes streaming data per day.
Securing enterprise data starts from understanding data activities for every
user. Apache Eagle (incubating, called Eagle in the following) has integrated
with many popular big data platforms e.g. Hadoop, Hive, Spark, Cassandra etc.
With Eagle user can browse data hierarchy, mark sensitive data and then create
comprehensive policy to alert for insecure data access.",
+ "title": "Data Activity Monitoring"
+ },
+ {
+ "location": "/#job-performance-analysis",
+ "text": "Running map/reduce job is the most popular way people use
to analyze data in Hadoop system. Analyzing job performance and providing
tuning suggestions are critical for Hadoop system stability, job SLA and
resource usage etc. Eagle analyzes job performance with two complementing
approaches. First Eagle periodically takes snapshots for all running jobs with
YARN API, secondly Eagle continuously reads job lifecycle events immediately
after the job is completed. With the two approaches, Eagle can analyze single
job's trend, data skew problem, failure reasons etc. More interestingly, Eagle
can analyze whole Hadoop cluster's performance by taking into account all
jobs.",
+ "title": "Job Performance Analysis"
+ },
+ {
+ "location": "/#cluster-performance-analytics",
+ "text": "It is critical to understand why a cluster performs bad.
Is that because of some crazy jobs recently on-boarded, or huge amount of tiny
files, or namenode performance degrading? Eagle in realtime calculates
resource usage per minute out of individual jobs, e.g. CPU, memory, HDFS IO
bytes, HDFS IO numOps etc. and also collects namenode JMX metrics. Correlating
them together will easily help system administrator find root cause for cluster
slowness.",
+ "title": "Cluster Performance Analytics"
+ },
+ {
+ "location": "/#disclaimer",
+ "text": "Apache Eagle now is being incubated, and therefore,
across the whole documentation site, all appearances of case-insensitive word
eagle and apache eagle represent Apache Eagle (incubating) . This could be
seen as a part of disclaimer.",
+ "title": "Disclaimer"
+ },
+ {
+ "location": "/getting-started/",
+ "text": "Architecture\n\n\n\n\nEagle
Apps\n\n\n\n\nSecurity\n\n\nHadoop\n\n\nOperational Intelligence\n\n\n\n\nFor
more applications, see \nApplications\n.\n\n\nEagle Interface\n\n\n\n\nREST
Service\n\n\nManagement UI\n\n\nCustomizable Analytics
Visualization\n\n\n\n\nEagle Integration\n\n\n\n\nApache
Ambari\n\n\nDocker\n\n\nApache Ranger\n\n\nDataguise\n\n\n\n\nEagle
Framework\n\n\nEagle has multiple distributed real-time frameworks for
efficiently developing highly scalable monitoring applications.\n\n\nAlert
Engine\n\n\n\n\n\n\nReal-time: Apache Storm (Execution Engine) + Kafka (Message
Bus)\n\n\n\n\nDeclarative Policy: SQL (CEP) on Streaming\n from
hadoopJmxMetricEventStream\n [metric ==
\"hadoop.namenode.fsnamesystemstate.capacityused\" and value \n 0.9] \n
select metric, host, value, timestamp, component, site \n insert into
alertStream;\n\n\n\n\n\n\nDynamical onboarding \n correlation\n\n\n\n\nNo
downtime migration and upgrading\n\n\n\n
\nStorage Engine\n\n\n\n\n\n\n\n\nLight-weight ORM Framework for
HBase/RDMBS\n\n\n@Table(\"HbaseTableName\")\n@ColumnFamily(\"ColumnFamily\")\n@Prefix(\"RowkeyPrefix\")\n@Service(\"UniqueEntitytServiceName\")\n@JsonIgnoreProperties(ignoreUnknown
= true)\n@TimeSeries(false)\n@Indexes({\n
@Index(name=\"Index_1_alertExecutorId\", columns = { \"alertExecutorID\" },
unique = true)})\npublic class AlertDefinitionAPIEntity extends
TaggedLogAPIEntity{\n@Column(\"a\")\nprivate String
desc;\n\n\n\n\n\n\n\nFull-function SQL-Like REST Query
\n\n\nQuery=UniqueEntitytServiceName[@site=\"sandbox\"]{*}\n\n\n\n\n\n\n\nOptimized
Rowkey design for time-series data, optimized for metric/entity/log, etc.
different storage types\n\n\nRowkey ::= Prefix | Partition Keys | timestamp |
tagName | tagValue | \u2026\n\n\n\n\n\n\n\nSecondary Index Support\n
@Indexes(, unique = true/false)})\n\n\n\n\n\n\nNative HBase Coprocessor\n
org.apache.eagle.storage.hbase.query.coprocessor.AggregateProtocol
EndPoint\n\n\n\n\n\n\nUI Framework\n\n\nEagle UI is consist of following
parts:\n\n\n\n\nEagle Main UI\n\n\nEagle App
Portal/Dashboard/Widgets\n\n\nEagle Customized Dashboard \n\n\n\n\nApplication
Framework\n\n\nApplication\n\n\nAn \"Application\" or \"App\" is composed of
data integration, policies and insights for one data source.\n\n\nApplication
Descriptor\n\n\nAn \"Application Descriptor\" is a static packaged metadata
information consist of basic information like type, name, version, description,
and application process, configuration, streams, docs, policies and so on.
\n\n\nHere is an example ApplicationDesc of \nJPM_WEB_APP\n\n\n {\n type:
\"JPM_WEB_APP\",\n name: \"Job Performance Monitoring Web \",\n version:
\"0.5.0-incubating\",\n description: null,\n appClass:
\"org.apache.eagle.app.StaticApplication\",\n jarPath:
\"/opt/eagle/0.5.0-incubating-SNAPSHOT-build-20161103T0332/eagle-0.5.0-incubating-SNAPSHOT/lib/eagle-topology-0.5.0-incubating-SNAPSHOT-
hadoop-2.4.1-11-assembly.jar\",\n viewPath: \"/apps/jpm\",\n
providerClass: \"org.apache.eagle.app.jpm.JPMWebApplicationProvider\",\n
configuration: {\n properties: [{\n name: \"service.host\",\n
displayName: \"Eagle Service Host\",\n value:
\"localhost\",\n description: \"Eagle Service Host, default:
localhost\",\n required: false\n }, {\n name:
\"service.port\",\n displayName: \"Eagle Service Port\",\n
value: \"8080\",\n description: \"Eagle Service Port, default:
8080\",\n required: false\n }]\n },\n streams: null,\n
docs: null,\n executable: false,\n dependencies: [{\n type:
\"MR_RUNNING_JOB_APP\",\n version: \"0.5.0-incubating\",\n
required: true\n }, {\n type: \"MR_HISTORY_JOB_APP\",\n
version: \"0.5.0-incubating\",\n required: true\n }]\n
}\n\n\n\nApplication Provider\n\n\n
Appilcation Provider is a package management and loading mechanism leveraging
\nJava SPI\n.\n\n\nFor example, in file
\nMETA-INF/services/org.apache.eagle.app.spi.ApplicationProvider\n, place the
full class name of an application
provider:\n\n\norg.apache.eagle.app.jpm.JPMWebApplicationProvider\n\n\n\n\n\nConcepts\n\n\n\n\nHere
are some terms we are using in Apache Eagle (incubating, called Eagle in the
following), please check them for your reference. They are basic knowledge of
Eagle which also will help to well understand Eagle.\n\n\n\n\nSite\n\n\n\n\nA
site can be considered as a physical data center. Big data platform e.g. Hadoop
may be deployed to multiple data centers in an
enterprise.\n\n\n\n\nApplication\n\n\n\n\nAn \"Application\" or \"App\" is
composed of data integration, policies and insights for one data
source.\n\n\n\n\nPolicy\n\n\n\n\nA \"Policy\" defines the rule to alert. Policy
can be simply a filter expression or a complex window based aggregation rules
etc.\n\n\
n\n\nAlerts\n\n\n\n\nAn \"Alert\" is an real-time event detected with certain
alert policy or correlation logic, with different severity levels like
INFO/WARNING/DANGER.\n\n\n\n\nData Source\n\n\n\n\nA \"Data Source\" is a
monitoring target data. Eagle supports many data sources HDFS audit logs, Hive2
query, MapReduce job etc.\n\n\n\n\nStream\n\n\n\n\nA \"Stream\" is the
streaming data from a data source. Each data source has its own
stream.\n\n\n\n\n\n\nQuick Start\n\n\nDeployment\n\n\nPrerequisites\n\n\nEagle
requires the following dependencies:\n\n\n\n\nFor streaming platform
dependencies\n\n\nStorm: 0.9.3 or later\n\n\nHadoop: 2.6.x or later\n\n\nHbase:
0.98.x or later\n\n\nKafka: 0.8.x or later\n\n\nZookeeper: 3.4.6 or
later\n\n\nJava: 1.8.x\n\n\n\n\n\n\nFor metadata database dependencies (Choose
one of them)\n\n\nMangoDB 3.2.2 or later\n\n\nInstallation is
required\n\n\n\n\n\n\nMysql 5.1.x or later\n\n\nInstallation is
required\n\n\n\n\n\n\n\n\n\n\n\n\nNotice: \n\n\n\n\nStorm
0.9.x does NOT support JDK8. You can replace asm-4.0.jar with asm-all-5.0.jar
in the storm lib directory. \nThen restart other
services(nimbus/ui/supervisor).\n\n\n\n\n\nInstallation\n\n\nBuild
Eagle\n\n\n\n\n\n\nDownload the latest version of Eagle source code.\n\n\ngit
clone https://github.com/apache/incubator-eagle.git\n\n\n\n\n\n\n\nBuild the
source code, and a tar.gz package will be generated under
eagle-server-assembly/target\n\n\nmvn clean install
-DskipTests\n\n\n\n\n\n\n\nDeploy Eagle\n\n\n\n\nCopy binary package to your
server machine. In the package, you should find:\n\n\nbin/\n: scripts used for
start eagle server\n\n\nconf/\n: default configurations for eagle server
setup.\n\n\nlib/\n : all included software packages for eagle
server\n\n\n\n\n\n\nChange configurations under
\nconf/\n\n\neagle.conf\n\n\nserver.yml\n\n\n\n\n\n\n\n\nRun
eagle-server.sh\n\n\n./bin/eagle-server.sh start\n\n\n\n\n\n\n\nCheck eagle
server\n\n\n\n\nVisit http://host:port/ in your web browser.\
n\n\n\n\n\n\n\n\nSetup Your Monitoring Case\n\n\nPlaceholder for topic: Setup
Your Monitoring Case",
+ "title": "Getting Started"
+ },
+ {
+ "location": "/getting-started/#architecture",
+ "text": "",
+ "title": "Architecture"
+ },
+ {
+ "location": "/getting-started/#eagle-apps",
+ "text": "Security Hadoop Operational Intelligence For more
applications, see Applications .",
+ "title": "Eagle Apps"
+ },
+ {
+ "location": "/getting-started/#eagle-interface",
+ "text": "REST Service Management UI Customizable Analytics
Visualization",
+ "title": "Eagle Interface"
+ },
+ {
+ "location": "/getting-started/#eagle-integration",
+ "text": "Apache Ambari Docker Apache Ranger Dataguise",
+ "title": "Eagle Integration"
+ },
+ {
+ "location": "/getting-started/#eagle-framework",
+ "text": "Eagle has multiple distributed real-time frameworks for
efficiently developing highly scalable monitoring applications.",
+ "title": "Eagle Framework"
+ },
+ {
+ "location": "/getting-started/#alert-engine",
+ "text": "Real-time: Apache Storm (Execution Engine) + Kafka
(Message Bus) Declarative Policy: SQL (CEP) on Streaming\n from
hadoopJmxMetricEventStream\n [metric ==
\"hadoop.namenode.fsnamesystemstate.capacityused\" and value 0.9] \n
select metric, host, value, timestamp, component, site \n insert into
alertStream; Dynamical onboarding correlation No downtime migration and
upgrading",
+ "title": "Alert Engine"
+ },
+ {
+ "location": "/getting-started/#storage-engine",
+ "text": "Light-weight ORM Framework for HBase/RDMBS
@Table(\"HbaseTableName\")\n@ColumnFamily(\"ColumnFamily\")\n@Prefix(\"RowkeyPrefix\")\n@Service(\"UniqueEntitytServiceName\")\n@JsonIgnoreProperties(ignoreUnknown
= true)\n@TimeSeries(false)\n@Indexes({\n
@Index(name=\"Index_1_alertExecutorId\", columns = { \"alertExecutorID\" },
unique = true)})\npublic class AlertDefinitionAPIEntity extends
TaggedLogAPIEntity{\n@Column(\"a\")\nprivate String desc; Full-function
SQL-Like REST Query Query=UniqueEntitytServiceName[@site=\"sandbox\"]{*}
Optimized Rowkey design for time-series data, optimized for metric/entity/log,
etc. different storage types Rowkey ::= Prefix | Partition Keys | timestamp |
tagName | tagValue | \u2026 Secondary Index Support\n @Indexes(,
unique = true/false)}) Native HBase Coprocessor\n
org.apache.eagle.storage.hbase.query.coprocessor.AggregateProtocolEndPoint",
+ "title": "Storage Engine"
+ },
+ {
+ "location": "/getting-started/#ui-framework",
+ "text": "Eagle UI is consist of following parts: Eagle Main UI
Eagle App Portal/Dashboard/Widgets Eagle Customized Dashboard",
+ "title": "UI Framework"
+ },
+ {
+ "location": "/getting-started/#application-framework",
+ "text": "",
+ "title": "Application Framework"
+ },
+ {
+ "location": "/getting-started/#application",
+ "text": "An \"Application\" or \"App\" is composed of data
integration, policies and insights for one data source.",
+ "title": "Application"
+ },
+ {
+ "location": "/getting-started/#application-descriptor",
+ "text": "An \"Application Descriptor\" is a static packaged
metadata information consist of basic information like type, name, version,
description, and application process, configuration, streams, docs, policies
and so on. Here is an example ApplicationDesc of JPM_WEB_APP {\n
type: \"JPM_WEB_APP\",\n name: \"Job Performance Monitoring Web \",\n
version: \"0.5.0-incubating\",\n description: null,\n appClass:
\"org.apache.eagle.app.StaticApplication\",\n jarPath:
\"/opt/eagle/0.5.0-incubating-SNAPSHOT-build-20161103T0332/eagle-0.5.0-incubating-SNAPSHOT/lib/eagle-topology-0.5.0-incubating-SNAPSHOT-hadoop-2.4.1-11-assembly.jar\",\n
viewPath: \"/apps/jpm\",\n providerClass:
\"org.apache.eagle.app.jpm.JPMWebApplicationProvider\",\n configuration: {\n
properties: [{\n name: \"service.host\",\n
displayName: \"Eagle Service Host\",\n value: \"localhost\",\n
description: \"Eagle Service Host, de
fault: localhost\",\n required: false\n }, {\n
name: \"service.port\",\n displayName: \"Eagle Service Port\",\n
value: \"8080\",\n description: \"Eagle Service Port,
default: 8080\",\n required: false\n }]\n },\n streams:
null,\n docs: null,\n executable: false,\n dependencies: [{\n
type: \"MR_RUNNING_JOB_APP\",\n version: \"0.5.0-incubating\",\n
required: true\n }, {\n type: \"MR_HISTORY_JOB_APP\",\n
version: \"0.5.0-incubating\",\n required: true\n }]\n }",
+ "title": "Application Descriptor"
+ },
+ {
+ "location": "/getting-started/#application-provider",
+ "text": "Appilcation Provider is a package management and loading
mechanism leveraging Java SPI . For example, in file
META-INF/services/org.apache.eagle.app.spi.ApplicationProvider , place the full
class name of an application provider:
org.apache.eagle.app.jpm.JPMWebApplicationProvider",
+ "title": "Application Provider"
+ },
+ {
+ "location": "/getting-started/#concepts",
+ "text": "Here are some terms we are using in Apache Eagle
(incubating, called Eagle in the following), please check them for your
reference. They are basic knowledge of Eagle which also will help to well
understand Eagle.",
+ "title": "Concepts"
+ },
+ {
+ "location": "/getting-started/#site",
+ "text": "A site can be considered as a physical data center. Big
data platform e.g. Hadoop may be deployed to multiple data centers in an
enterprise.",
+ "title": "Site"
+ },
+ {
+ "location": "/getting-started/#application_1",
+ "text": "An \"Application\" or \"App\" is composed of data
integration, policies and insights for one data source.",
+ "title": "Application"
+ },
+ {
+ "location": "/getting-started/#policy",
+ "text": "A \"Policy\" defines the rule to alert. Policy can be
simply a filter expression or a complex window based aggregation rules etc.",
+ "title": "Policy"
+ },
+ {
+ "location": "/getting-started/#alerts",
+ "text": "An \"Alert\" is an real-time event detected with certain
alert policy or correlation logic, with different severity levels like
INFO/WARNING/DANGER.",
+ "title": "Alerts"
+ },
+ {
+ "location": "/getting-started/#data-source",
+ "text": "A \"Data Source\" is a monitoring target data. Eagle
supports many data sources HDFS audit logs, Hive2 query, MapReduce job etc.",
+ "title": "Data Source"
+ },
+ {
+ "location": "/getting-started/#stream",
+ "text": "A \"Stream\" is the streaming data from a data source.
Each data source has its own stream.",
+ "title": "Stream"
+ },
+ {
+ "location": "/getting-started/#quick-start",
+ "text": "",
+ "title": "Quick Start"
+ },
+ {
+ "location": "/getting-started/#deployment",
+ "text": "",
+ "title": "Deployment"
+ },
+ {
+ "location": "/getting-started/#prerequisites",
+ "text": "Eagle requires the following dependencies: For
streaming platform dependencies Storm: 0.9.3 or later Hadoop: 2.6.x or later
Hbase: 0.98.x or later Kafka: 0.8.x or later Zookeeper: 3.4.6 or later Java:
1.8.x For metadata database dependencies (Choose one of them) MangoDB 3.2.2
or later Installation is required Mysql 5.1.x or later Installation is
required Notice: Storm 0.9.x does NOT support JDK8. You can replace
asm-4.0.jar with asm-all-5.0.jar in the storm lib directory. \nThen restart
other services(nimbus/ui/supervisor).",
+ "title": "Prerequisites"
+ },
+ {
+ "location": "/getting-started/#installation",
+ "text": "",
+ "title": "Installation"
+ },
+ {
+ "location": "/getting-started/#build-eagle",
+ "text": "Download the latest version of Eagle source code. git
clone https://github.com/apache/incubator-eagle.git Build the source code,
and a tar.gz package will be generated under eagle-server-assembly/target mvn
clean install -DskipTests",
+ "title": "Build Eagle"
+ },
+ {
+ "location": "/getting-started/#deploy-eagle",
+ "text": "Copy binary package to your server machine. In the
package, you should find: bin/ : scripts used for start eagle server conf/ :
default configurations for eagle server setup. lib/ : all included software
packages for eagle server Change configurations under conf/ eagle.conf
server.yml Run eagle-server.sh ./bin/eagle-server.sh start Check eagle
server Visit http://host:port/ in your web browser.",
+ "title": "Deploy Eagle"
+ },
+ {
+ "location": "/getting-started/#setup-your-monitoring-case",
+ "text": "Placeholder for topic: Setup Your Monitoring Case",
+ "title": "Setup Your Monitoring Case"
+ },
+ {
+ "location": "/using-eagle/",
+ "text": "Manage Eagle and Services\n\n\n\n\n\n\nAfter Apache Eagle
has been deployed (please reference \ndeployment\n), you can enter deployment
directory and use commands below to control Apache Eagle
Server.\n\n\n./bin/eagle-server.sh start|stop|status\n\n\n\n\n\n\n\nAfter
starting the Eagle server, please type http://\n:\n/ to open the web ui of
Eagle.\n\n\n\n\n\n\n\n\nUse Eagle Web Interface\n\n\n\n\n\n\nThis is the
typical Web Interface (short for WI) after setting up your Eagle monitoring
environment. WI majorly contain the right main panel and left function
menu.\n\n\n\n\n\n\n\n\nHome\n\n\n\n\n\n\nThis is the aggregated UI for
configured sites, and the applications. It will show those created sites
created, how many application installed for each sites, and alerts generated
from that cluster. You can click \u201cMore info\u201d link to view the details
for particular site.\n\n\n\n\n\n\nThe \u201c\nWidgets\n\u201d section is
customizable; if the application develop
er have its application registered to Home page, you can find that in
\u201c\nWidgets\n\u201d section. Please check the application developer guide
about how to register applications to home widgets. It give you a shortcut to
go directly to the application home.\n\n\n\n\n\n\nAlert\n\n\n\n\nIn Alert menu,
you can define the policies, list the policies and check your alerts there.
\n\n\n\n\nIntegration\n\n\n\n\nThe integration page provides the management
functionality for Eagle. You can list the built-in applications there, create
sites, and manage the applications in your site.\n\n\n\n\nSites\n\n\n\n\nIt
also gives you a shortcut to particular site.\n\n\n\n\n\n\nSetup The Monitoring
Application\n\n\nMonitoring Applications\n\n\n\n\n\n\nEagle has an extensible
framework to dynamically add new monitoring applications in Eagle environment.
It also ships some built-in big data monitoring applications.\n\n\n\n\n\n\nGo
to \u201c\nIntegration\n\u201d -\n \u201c\nApplications\n\u201d, it wi
ll list a set of available monitoring applications which you can choose to
monitor your services.\n\n\n\n\n\n\n\n\nThe \u201c\nApplication\n\u201d column
is the display name for an application, \u201c\nStreams\n\u201d is a logical
name for the data stream from the monitored source after pre-processing, which
will consumed by Alert Engine.\n\n\n\n\n\n\nAt the moment, we have the below
built-in applications shipped with Apache Eagle. You can refer to the
application documentation to understand how to do the configuration for each
monitoring
application.\n\n\n\n\n\n\n\n\nApplication\n\n\nDescription\n\n\n\n\n\n\n\n\n\n\nTopology
Health Check\n\n\nThis application can be used to monitor the service
healthiness for HDFS, HBase and YARN. You can get alerted once the master role
or the slave role got crashed.\n\n\n\n\n\n\nHadoop JMX Metrics
Monitoring\n\n\nThis application can be used to monitor the JMX metrics data
from the master nodes of HDFS, HBase and YARN, e.g. NameNode, HBase Master
and YARN Resource Manager.\n\n\n\n\n\n\nHDFS Audit Log Monitor\n\n\nThis
application can be used to monitor the data operations in HDFS, to detect
sensitive data access and malicious operations; to protect from data leak or
data loss.\n\n\n\n\n\n\nHBase Audit Log Monitor\n\n\nSame as HDFS Audit Log
Monitor, this application is used to monitor the data operations in
HBase.\n\n\n\n\n\n\nMap Reduce History Job\n\n\nThis application is used to get
the MapReduce history job counters from YARN history server and job running
history from HDFS log directory.\n\n\n\n\n\n\nMap Reduce Running Job\n\n\nThis
application is used to get the MapReduce running job counter information using
YARN Rest API.\n\n\n\n\n\n\nHadoop Queue Monitor\n\n\nThis application is used
to get the resource scheduling and utilization info from YARN.\n\n\n\n\n\n\nMR
Metrics Aggregation\n\n\nThis application is used to aggregate the job counters
and some resource utilization in a certain period of time (daily, weekly or
monthly).\n\n\n\n\n\n\nJob Performance Monitor Web\n\n\nThis application only
contains the frontend, and depends on Map Reduce History Job and Map Reduce
Running Job.\n\n\n\n\n\n\nAlert Engine\n\n\nAlert Engine is a special
application and used to process the output data from other
applications.\n\n\n\n\n\n\n\n\n\n\n\n\nManaging Sites\n\n\nTo enable a real
monitoring use case, you have to create a site first, and install a certain
application for this site, and finally start the application. We use site
concept to group the running applications and avoid the application
conflict.\n\n\nSites\n\n\n\n\n\n\nGo to \u201c\nIntegration\n\u201d -\n
\u201c\nSites\n\u201d, there will be a table listing the managed
sites.\n\n\n\n\n\n\n\n\nCreate Site\n\n\n\n\n\n\nClick \u201c\nNew Site\n\u201d
on the bottom right of the Sites page. You can fill the information in site
creation dialog.\n\n\n\n\n\n\n\n\nThe \u201c\nSite Id\n\u201d should not be
duplicated. After the creation, you can find it in
sites page.\n\n\n\n\n\n\n\n\nConfiguring a Site\n\n\n\n\n\n\nBy clicking
\u201c\nEdit\n\u201d button or the Site column in Sites table, you can have the
Site configuration page, there you can install monitoring
applications.\n\n\n\n\n\n\n\n\nInstall and Run Applications in
Site\n\n\n\n\n\n\nChoose the particular application which you want to install,
you probably have something to fill, e.g. the HDFS NameNode address, Zookeeper
address and port. Please check each application documentation for how to
configure each application. \n\n\n\n\n\n\nAfter doing the installation, you can
start the application by clicking \n or stop the application by \n. You can
check the \u201c\nStatus\n\u201d column about the running status. Usually, it
should have \u201c\nINITIALIZED\n\u201d or \u201c\nRUNNING\n\u201d for a
healthy application.\n\n\n\n\n\n\n\n\nDefine Policies\n\n\nAfter setting up the
monitoring applications, you probably want to setup some alert policies against
the monitored data, so yo
u can get notified once any violation on the data. Eagle has a centralized
place for policy definition.\n\n\nPolicies\n\n\n\n\n\n\nGo to
\u201c\nAlert\n\u201d -\n \u201c\nPolicies\n\u201d, you can check the policies
defined and take control on whether to enable the policy:\n\n\n\n\n\n\n\n\nYou
can apply the below actions for a certain policy:\n\n\n\n\n\n\n: enable a
policy\n\n\n\n\n\n\n: disable a policy\n\n\n\n\n\n\n: edit a
policy\n\n\n\n\n\n\n: purge a policy\n\n\n\n\n\n\n\n\n\n\nDefine or Edit
Policies\n\n\n\n\n\n\nIf you want to create a new policy, click
\u201c\nAlert\n\u201d -\n \u201c\nDefine Policy\n\u201d, or you can enter into
the policy definition page by editing an existing policy. After that, you can
go to the policy list to enable the policy dynamically.\n\n\n\n\n\n\n\n\nSource
Stream\n\n\n\n\nThe source stream gives user a full view about what data stream
is available for application defined for particular site, as well as the data
structures in each data stream. Dat
a stream name is suffixed by the site name.\n\n\n\n\nPolicy Name\n\n\n\n\nThe
policy name should be globally unique.\n\n\n\n\nPublish Alerts\n\n\n\n\n\n\nIn
this section, you can define the alert publishment method by clicking the
\u201c\n+Add Publisher\n\u201d.\n\n\n\n\n\n\n\n\nYou can choose the publishment
method from an existing policy or by creating new publisher.
\n\n\n\n\n\n\nThere are four built-in publisher
types:\n\n\n\n\n\n\nEmailPublisher\n:
org.apache.eagle.alert.engine.publisher.impl.AlertEmailPublisher\n\n\n\n\n\n\nKafkaPublisher\n:
org.apache.eagle.alert.engine.publisher.impl.AlertKafkaPublisher\n\n\n\n\n\n\nSlackPublisher\n:
org.apache.eagle.alert.engine.publisher.impl.AlertSlackPublisher\n\n\n\n\n\n\nEagleStoragePlugin\n:
org.apache.eagle.alert.engine.publisher.impl.AlertEagleStoragePlugin\n\n\n\n\n\n\n\n\n\n\nPolicy
Syntax\n\n\n\n\n\n\nCurrently, we support SiddhiQL(please view Siddhi Query
Language Specification \nhere\n)\n\n\n\n\n\n\nIn order to explain how stre
am data is processed, let us take policy below as an example:\n\n\nfrom
map_reduce_failed_job_stream[site==\"sandbox\" and
currentState==\"FAILED\"]\nselect * group by jobId insert into
map_reduce_failed_job_stream_out\n\n\n\n\n\n\n\nThis policy contains below
parts:\n\n\n\n\n\n\nSource\n: from
map_reduce_failed_job_stream\n\n\n\n\n\n\nFilter\n: [site==\"sandbox\" and
currentState==\"FAILED\"]\n\n\n\n\n\n\nProjection\n: select
*\n\n\n\n\n\n\nGroupBy\n: group by jobId\n\n\n\n\n\n\nDestination\n: insert
into map_reduce_failed_job_stream_out\n\n\n\n\n\n\n\n\n\n\nSource
Streams(schema) are defined by applications, and applications will write stream
data to data sink(currently, we support kafka as data sink).\n\n\nstreams\n\n
\nstream\n\n \nstreamId\nmap_reduce_failed_job_stream\n/streamId\n\n
\ndescription\nMap Reduce Failed Job Stream\n/description\n\n
\nvalidate\ntrue\n/validate\n\n \ncolumns\n\n \ncolumn\n\n
\nname\nsite\n/name
\n\n \ntype\nstring\n/type\n\n \n/column\n\n
\u2026...\n \ncolumn\n\n \nname\njobId\n/name\n\n
\ntype\nstring\n/type\n\n \ncolumn\n\n
\nname\ncurrentState\n/name\n\n \ntype\nstring\n/type\n\n
\n/column\n\n \n/columns\n\n
\n/stream\n\n\n/streams\n\n\n\n\n\n\n\n\nAfter policy is defined, Alert engine
will create siddhi execution runtime for the policy(also load stream data
schema from metadata store). Since siddhi execution runtime knows the stream
data schema, then it will process stream data and do the
calculation.\n\n\n\n\n\n\n\n\nMonitoring Dashboard\n\n\n\n\n\n\nAfter setting
the sites and applications, you can find the site item from the home page or
\u201cSites\u201d menu.\n\n\n\n\n\n\nHere is a site home example. After
entering the site home, the left menu will be replaced by application dashboard
links only related to that site, so you ca
n switch between the application dashboard quickly. In the right panel, it
contains the application icons installed in this site, but depends on if the
application has its dashboard defined. You can click the application icon or
the application links to go to the application dashboard home. Please check the
application documentation about how to use the application monitoring
dashboard.\n\n\n\n\n\n\n\n\n\n\nCheck The Alerts\n\n\n\n\n\n\nEagle has all the
alerts generated by all the applications stored in its database, so you can
check your application alerts from Eagle WI. \n\n\n\n\n\n\nGo to
\u201c\nAlert\n\u201d -\n \u201c\nAlerts\n\u201d, you can find the alerts
table.\n\n\n\n\n\n\n\n\nAlso you can check more detailed information by
clicking \u201c\nDetail\n\u201d link for each alert
item.\n\n\n\n\n\n\n\n\n\n\nHow to stream audit log into
Kafka\n\n\nLogstash\n\n\nThe sample configuration is tested with
logstash-2.3.4. Logstash is required to be installed on the namenode host.\n\n
\n\n\n\n\nStep 1\n: Create a Kafka topic as the streaming input.\n\n\nHere is
an sample Kafka command to create topic 'sandbox_hdfs_audit_log'\n\n\ncd
\nkafka-home\n\nbin/kafka-topics.sh --create --zookeeper localhost:2181
--replication-factor 1 --partitions 1 --topic
sandbox_hdfs_audit_log\n\n\n\n\n\n\n\nStep 2\n: Create a Logstash configuration
file under ${LOGSTASH_HOME}/conf. Here is a sample.\n\n\ninput {\n file
{\n type =\n \"hdp-nn-audit\"\n path =\n
\"/tmp/test/hdfs-audit.log\"\n start_position =\n end\n
sincedb_path =\n \"/dev/null\"\n }\n }\n output {\n if [type] ==
\"hdp-nn-audit\" {\n kafka {\n codec =\n plain {\n
format =\n \"%{message}\"\n }\n bootstrap_servers
=\n \"host:9092\"\n topic_id =\n \"hdfs_audit_log\"\n
acks =\n \"0\"\n timeout_ms =\n 10000\n\n
send_buffer_bytes =\n 102400\n client_id =\n \"hdp-n
n-audit\"\n\n workers =\n 10\n compression_type =\n
\"gzip\"\n }\n # stdout { codec =\n rubydebug }\n
}\n}\n\n\n\n\n\n\n\nStep 4\n: Start Logstash\n\n\nbin/logstash -f
conf/sample.conf\n\n\n\n\n\n\n\nStep 5\n: Check whether logs are flowing into
the kafka topic specified by \ntopic_id\n\n\n\n\n\n\nFilebeat\n\n\nThe sample
filebeat.yml is tested with filebeat-5.0.0-beta1-linux-x86_64. The throughput
can be up to 20K messages per second. Filebeat is required to be installed on
the namenode host.\n\n\n filebeat.publish_async: false\n
filebeat.spool_size: 8192\n filebeat.idle_timeout: 5s\n max_procs: 1\n
queue_size: 1000\n\n filebeat.prospectors:\n - input_type: log\n
paths:\n - /tmp/test/hdfs-audit.log\n #tail_files: true\n
harvester_buffer_size: 8192\n\n output.kafka:\n enabled: true\n
hosts: [\"host:9092\"]\n topic: \"phx_hdfs_audit_log\"\n client_id:
\"client-host\"\n work
er: 10\n max_retries: 3\n bulk_max_size: 8192\n
channel_buffer_size: 512\n timeout: 10\n broker_timeout: 3s\n
keep_alive: 0\n compression: none\n max_message_bytes: 1000000\n
required_acks: 0\n flush_interval: 1\n\n logging.metrics.period:
10s\n\n processors:\n - include_fields:\n fields: [\"message\",
\"beat.hostname\"]\n\n\n\nLog4j Kafka Appender\n\n\nThis sample configuration
is tested in HDP sandbox. \nRestarting namenode is required\n after updating
the log4j configuration. \n\n\n\n\n\n\nStep 1\n: Create a Kafka topic. Here is
an example Kafka command for creating topic \"sandbox_hdfs_audit_log\"\n\n\ncd
\nkafka-home\n\nbin/kafka-topics.sh --create --zookeeper localhost:2181
--replication-factor 1 --partitions 1 --topic
sandbox_hdfs_audit_log\n\n\n\n\n\n\n\nStep 2\n: Configure
$HADOOP_CONF_DIR/log4j.properties, and add a log4j appender
\"KAFKA_HDFS_AUDIT\" to hdfs audit logging\n\n\nlog4j.appender.KAFKA_HDFS_A
UDIT=org.apache.eagle.log4j.kafka.KafkaLog4jAppender\nlog4j.appender.KAFKA_HDFS_AUDIT.Topic=sandbox_hdfs_audit_log\nlog4j.appender.KAFKA_HDFS_AUDIT.BrokerList=sandbox.hortonworks.com:6667\nlog4j.appender.KAFKA_HDFS_AUDIT.KeyClass=org.apache.eagle.log4j.kafka.hadoop.AuditLogKeyer\nlog4j.appender.KAFKA_HDFS_AUDIT.Layout=org.apache.log4j.PatternLayout\nlog4j.appender.KAFKA_HDFS_AUDIT.Layout.ConversionPattern=%d{ISO8601}
%p %c{2}:
%m%n\nlog4j.appender.KAFKA_HDFS_AUDIT.ProducerType=async\n#log4j.appender.KAFKA_HDFS_AUDIT.BatchSize=1\n#log4j.appender.KAFKA_HDFS_AUDIT.QueueSize=1\n\n\n\n\n\n\n\nStep
3\n: Edit $HADOOP_CONF_DIR/hadoop-env.sh, and add the reference to
KAFKA_HDFS_AUDIT to
HADOOP_NAMENODE_OPTS.\n\n\n-Dhdfs.audit.logger=INFO,DRFAAUDIT,KAFKA_HDFS_AUDIT\n\n\n\n\n\n\n\nStep
4\n: Edit $HADOOP_CONF_DIR/hadoop-env.sh, and append the following command to
it.\n\n\nexport
HADOOP_CLASSPATH=${HADOOP_CLASSPATH}:/path/to/eagle/lib/log4jkafka/lib/*\n\n\n\n\n\n\n\nStep
5\n: save the changes an
d restart the namenode.\n\n\n\n\n\n\nStep 6\n: Check whether logs are flowing
into Topic sandbox_hdfs_audit_log\n\n\n$
/usr/hdp/current/kafka-broker/bin/kafka-console-consumer.sh --zookeeper
localhost:2181 --topic sandbox_hdfs_audit_log",
+ "title": "Using Eagle"
+ },
+ {
+ "location": "/using-eagle/#manage-eagle-and-services",
+ "text": "After Apache Eagle has been deployed (please reference
deployment ), you can enter deployment directory and use commands below to
control Apache Eagle Server. ./bin/eagle-server.sh start|stop|status After
starting the Eagle server, please type http:// : / to open the web ui of
Eagle.",
+ "title": "Manage Eagle and Services"
+ },
+ {
+ "location": "/using-eagle/#use-eagle-web-interface",
+ "text": "This is the typical Web Interface (short for WI) after
setting up your Eagle monitoring environment. WI majorly contain the right main
panel and left function menu.",
+ "title": "Use Eagle Web Interface"
+ },
+ {
+ "location": "/using-eagle/#home",
+ "text": "This is the aggregated UI for configured sites, and the
applications. It will show those created sites created, how many application
installed for each sites, and alerts generated from that cluster. You can click
\u201cMore info\u201d link to view the details for particular site. The
\u201c Widgets \u201d section is customizable; if the application developer
have its application registered to Home page, you can find that in \u201c
Widgets \u201d section. Please check the application developer guide about how
to register applications to home widgets. It give you a shortcut to go directly
to the application home.",
+ "title": "Home"
+ },
+ {
+ "location": "/using-eagle/#alert",
+ "text": "In Alert menu, you can define the policies, list the
policies and check your alerts there.",
+ "title": "Alert"
+ },
+ {
+ "location": "/using-eagle/#integration",
+ "text": "The integration page provides the management
functionality for Eagle. You can list the built-in applications there, create
sites, and manage the applications in your site.",
+ "title": "Integration"
+ },
+ {
+ "location": "/using-eagle/#sites",
+ "text": "It also gives you a shortcut to particular site.",
+ "title": "Sites"
+ },
+ {
+ "location": "/using-eagle/#setup-the-monitoring-application",
+ "text": "",
+ "title": "Setup The Monitoring Application"
+ },
+ {
+ "location": "/using-eagle/#monitoring-applications",
+ "text": "Eagle has an extensible framework to dynamically add new
monitoring applications in Eagle environment. It also ships some built-in big
data monitoring applications. Go to \u201c Integration \u201d - \u201c
Applications \u201d, it will list a set of available monitoring applications
which you can choose to monitor your services. The \u201c Application
\u201d column is the display name for an application, \u201c Streams \u201d is
a logical name for the data stream from the monitored source after
pre-processing, which will consumed by Alert Engine. At the moment, we have
the below built-in applications shipped with Apache Eagle. You can refer to the
application documentation to understand how to do the configuration for each
monitoring application. Application Description Topology Health Check
This application can be used to monitor the service healthiness for HDFS,
HBase and YARN. You can get alerted once the master role or the slave role got
crashed. Hadoop JMX Metrics Monitoring This application can be used to
monitor the JMX metrics data from the master nodes of HDFS, HBase and YARN,
e.g. NameNode, HBase Master and YARN Resource Manager. HDFS Audit Log
Monitor This application can be used to monitor the data operations in HDFS,
to detect sensitive data access and malicious operations; to protect from data
leak or data loss. HBase Audit Log Monitor Same as HDFS Audit Log Monitor,
this application is used to monitor the data operations in HBase. Map Reduce
History Job This application is used to get the MapReduce history job counters
from YARN history server and job running history from HDFS log directory.
Map Reduce Running Job This application is used to get the MapReduce running
job counter information using YARN Rest API. Hadoop Queue Monitor This
application is used to get the resource scheduling and utilization info from
YARN. MR Metrics Aggregation This application is used to aggregat
e the job counters and some resource utilization in a certain period of time
(daily, weekly or monthly). Job Performance Monitor Web This application
only contains the frontend, and depends on Map Reduce History Job and Map
Reduce Running Job. Alert Engine Alert Engine is a special application and
used to process the output data from other applications.",
+ "title": "Monitoring Applications"
+ },
+ {
+ "location": "/using-eagle/#managing-sites",
+ "text": "To enable a real monitoring use case, you have to create
a site first, and install a certain application for this site, and finally
start the application. We use site concept to group the running applications
and avoid the application conflict.",
+ "title": "Managing Sites"
+ },
+ {
+ "location": "/using-eagle/#sites_1",
+ "text": "Go to \u201c Integration \u201d - \u201c Sites \u201d,
there will be a table listing the managed sites.",
+ "title": "Sites"
+ },
+ {
+ "location": "/using-eagle/#create-site",
+ "text": "Click \u201c New Site \u201d on the bottom right of the
Sites page. You can fill the information in site creation dialog. The
\u201c Site Id \u201d should not be duplicated. After the creation, you can
find it in sites page.",
+ "title": "Create Site"
+ },
+ {
+ "location": "/using-eagle/#configuring-a-site",
+ "text": "By clicking \u201c Edit \u201d button or the Site column
in Sites table, you can have the Site configuration page, there you can install
monitoring applications.",
+ "title": "Configuring a Site"
+ },
+ {
+ "location": "/using-eagle/#install-and-run-applications-in-site",
+ "text": "Choose the particular application which you want to
install, you probably have something to fill, e.g. the HDFS NameNode address,
Zookeeper address and port. Please check each application documentation for how
to configure each application. After doing the installation, you can start
the application by clicking or stop the application by . You can check the
\u201c Status \u201d column about the running status. Usually, it should have
\u201c INITIALIZED \u201d or \u201c RUNNING \u201d for a healthy application.",
+ "title": "Install and Run Applications in Site"
+ },
+ {
+ "location": "/using-eagle/#define-policies",
+ "text": "After setting up the monitoring applications, you
probably want to setup some alert policies against the monitored data, so you
can get notified once any violation on the data. Eagle has a centralized place
for policy definition.",
+ "title": "Define Policies"
+ },
+ {
+ "location": "/using-eagle/#policies",
+ "text": "Go to \u201c Alert \u201d - \u201c Policies \u201d, you
can check the policies defined and take control on whether to enable the
policy: You can apply the below actions for a certain policy: : enable a
policy : disable a policy : edit a policy : purge a policy",
+ "title": "Policies"
+ },
+ {
+ "location": "/using-eagle/#define-or-edit-policies",
+ "text": "If you want to create a new policy, click \u201c Alert
\u201d - \u201c Define Policy \u201d, or you can enter into the policy
definition page by editing an existing policy. After that, you can go to the
policy list to enable the policy dynamically.",
+ "title": "Define or Edit Policies"
+ },
+ {
+ "location": "/using-eagle/#source-stream",
+ "text": "The source stream gives user a full view about what data
stream is available for application defined for particular site, as well as the
data structures in each data stream. Data stream name is suffixed by the site
name.",
+ "title": "Source Stream"
+ },
+ {
+ "location": "/using-eagle/#policy-name",
+ "text": "The policy name should be globally unique.",
+ "title": "Policy Name"
+ },
+ {
+ "location": "/using-eagle/#publish-alerts",
+ "text": "In this section, you can define the alert publishment
method by clicking the \u201c +Add Publisher \u201d. You can choose the
publishment method from an existing policy or by creating new publisher.
There are four built-in publisher types: EmailPublisher :
org.apache.eagle.alert.engine.publisher.impl.AlertEmailPublisher
KafkaPublisher :
org.apache.eagle.alert.engine.publisher.impl.AlertKafkaPublisher
SlackPublisher :
org.apache.eagle.alert.engine.publisher.impl.AlertSlackPublisher
EagleStoragePlugin :
org.apache.eagle.alert.engine.publisher.impl.AlertEagleStoragePlugin",
+ "title": "Publish Alerts"
+ },
+ {
+ "location": "/using-eagle/#policy-syntax",
+ "text": "Currently, we support SiddhiQL(please view Siddhi Query
Language Specification here ) In order to explain how stream data is
processed, let us take policy below as an example: from
map_reduce_failed_job_stream[site==\"sandbox\" and
currentState==\"FAILED\"]\nselect * group by jobId insert into
map_reduce_failed_job_stream_out This policy contains below parts: Source
: from map_reduce_failed_job_stream Filter : [site==\"sandbox\" and
currentState==\"FAILED\"] Projection : select * GroupBy : group by jobId
Destination : insert into map_reduce_failed_job_stream_out Source
Streams(schema) are defined by applications, and applications will write stream
data to data sink(currently, we support kafka as data sink). streams \n
stream \n streamId map_reduce_failed_job_stream /streamId \n
description Map Reduce Failed Job Stream /description \n validate true
/validate \n columns \n column \n
name site /name \n type string /type \n
/column \n \u2026...\n column \n name
jobId /name \n type string /type \n column \n
name currentState /name \n type string /type \n
/column \n /columns \n /stream /streams After policy is
defined, Alert engine will create siddhi execution runtime for the policy(also
load stream data schema from metadata store). Since siddhi execution runtime
knows the stream data schema, then it will process stream data and do the
calculation.",
+ "title": "Policy Syntax"
+ },
+ {
+ "location": "/using-eagle/#monitoring-dashboard",
+ "text": "After setting the sites and applications, you can find
the site item from the home page or \u201cSites\u201d menu. Here is a site
home example. After entering the site home, the left menu will be replaced by
application dashboard links only related to that site, so you can switch
between the application dashboard quickly. In the right panel, it contains the
application icons installed in this site, but depends on if the application has
its dashboard defined. You can click the application icon or the application
links to go to the application dashboard home. Please check the application
documentation about how to use the application monitoring dashboard.",
+ "title": "Monitoring Dashboard"
+ },
+ {
+ "location": "/using-eagle/#check-the-alerts",
+ "text": "Eagle has all the alerts generated by all the
applications stored in its database, so you can check your application alerts
from Eagle WI. Go to \u201c Alert \u201d - \u201c Alerts \u201d, you can
find the alerts table. Also you can check more detailed information by
clicking \u201c Detail \u201d link for each alert item.",
+ "title": "Check The Alerts"
+ },
+ {
+ "location": "/using-eagle/#how-to-stream-audit-log-into-kafka",
+ "text": "",
+ "title": "How to stream audit log into Kafka"
+ },
+ {
+ "location": "/using-eagle/#logstash",
+ "text": "The sample configuration is tested with logstash-2.3.4.
Logstash is required to be installed on the namenode host. Step 1 : Create a
Kafka topic as the streaming input. Here is an sample Kafka command to create
topic 'sandbox_hdfs_audit_log' cd kafka-home \nbin/kafka-topics.sh --create
--zookeeper localhost:2181 --replication-factor 1 --partitions 1 --topic
sandbox_hdfs_audit_log Step 2 : Create a Logstash configuration file under
${LOGSTASH_HOME}/conf. Here is a sample. input {\n file {\n type
= \"hdp-nn-audit\"\n path = \"/tmp/test/hdfs-audit.log\"\n
start_position = end\n sincedb_path = \"/dev/null\"\n }\n }\n
output {\n if [type] == \"hdp-nn-audit\" {\n kafka {\n
codec = plain {\n format = \"%{message}\"\n }\n
bootstrap_servers = \"host:9092\"\n topic_id =
\"hdfs_audit_log\"\n acks = \"0\"\n timeout
_ms = 10000\n\n send_buffer_bytes = 102400\n client_id
= \"hdp-nn-audit\"\n\n workers = 10\n compression_type
= \"gzip\"\n }\n # stdout { codec = rubydebug }\n }\n}
Step 4 : Start Logstash bin/logstash -f conf/sample.conf Step 5 : Check
whether logs are flowing into the kafka topic specified by topic_id",
+ "title": "Logstash"
+ },
+ {
+ "location": "/using-eagle/#filebeat",
+ "text": "The sample filebeat.yml is tested with
filebeat-5.0.0-beta1-linux-x86_64. The throughput can be up to 20K messages per
second. Filebeat is required to be installed on the namenode host.
filebeat.publish_async: false\n filebeat.spool_size: 8192\n
filebeat.idle_timeout: 5s\n max_procs: 1\n queue_size: 1000\n\n
filebeat.prospectors:\n - input_type: log\n paths:\n -
/tmp/test/hdfs-audit.log\n #tail_files: true\n harvester_buffer_size:
8192\n\n output.kafka:\n enabled: true\n hosts: [\"host:9092\"]\n
topic: \"phx_hdfs_audit_log\"\n client_id: \"client-host\"\n
worker: 10\n max_retries: 3\n bulk_max_size: 8192\n
channel_buffer_size: 512\n timeout: 10\n broker_timeout: 3s\n
keep_alive: 0\n compression: none\n max_message_bytes: 1000000\n
required_acks: 0\n flush_interval: 1\n\n logging.metrics.period:
10s\n\n processors:\n - include_fie
lds:\n fields: [\"message\", \"beat.hostname\"]",
+ "title": "Filebeat"
+ },
+ {
+ "location": "/using-eagle/#log4j-kafka-appender",
+ "text": "This sample configuration is tested in HDP sandbox.
Restarting namenode is required after updating the log4j configuration.
Step 1 : Create a Kafka topic. Here is an example Kafka command for creating
topic \"sandbox_hdfs_audit_log\" cd kafka-home \nbin/kafka-topics.sh --create
--zookeeper localhost:2181 --replication-factor 1 --partitions 1 --topic
sandbox_hdfs_audit_log Step 2 : Configure $HADOOP_CONF_DIR/log4j.properties,
and add a log4j appender \"KAFKA_HDFS_AUDIT\" to hdfs audit logging
log4j.appender.KAFKA_HDFS_AUDIT=org.apache.eagle.log4j.kafka.KafkaLog4jAppender\nlog4j.appender.KAFKA_HDFS_AUDIT.Topic=sandbox_hdfs_audit_log\nlog4j.appender.KAFKA_HDFS_AUDIT.BrokerList=sandbox.hortonworks.com:6667\nlog4j.appender.KAFKA_HDFS_AUDIT.KeyClass=org.apache.eagle.log4j.kafka.hadoop.AuditLogKeyer\nlog4j.appender.KAFKA_HDFS_AUDIT.Layout=org.apache.log4j.PatternLayout\nlog4j.appender.KAFKA_HDFS_AUDIT.Layout.ConversionPattern=%d{ISO8601}
%p %c{2}: %m%n\nlog
4j.appender.KAFKA_HDFS_AUDIT.ProducerType=async\n#log4j.appender.KAFKA_HDFS_AUDIT.BatchSize=1\n#log4j.appender.KAFKA_HDFS_AUDIT.QueueSize=1
Step 3 : Edit $HADOOP_CONF_DIR/hadoop-env.sh, and add the reference to
KAFKA_HDFS_AUDIT to HADOOP_NAMENODE_OPTS.
-Dhdfs.audit.logger=INFO,DRFAAUDIT,KAFKA_HDFS_AUDIT Step 4 : Edit
$HADOOP_CONF_DIR/hadoop-env.sh, and append the following command to it. export
HADOOP_CLASSPATH=${HADOOP_CLASSPATH}:/path/to/eagle/lib/log4jkafka/lib/*
Step 5 : save the changes and restart the namenode. Step 6 : Check whether
logs are flowing into Topic sandbox_hdfs_audit_log $
/usr/hdp/current/kafka-broker/bin/kafka-console-consumer.sh --zookeeper
localhost:2181 --topic sandbox_hdfs_audit_log",
+ "title": "Log4j Kafka Appender"
+ },
+ {
+ "location": "/applications/",
+ "text": "HDFS Data Activity Monitoring\n\n\nMonitor
Requirements\n\n\nThis application aims to monitor user activities on HDFS via
the hdfs audit log. Once any abnormal user activity is detected, an alert is
sent in several seconds. The whole pipeline of this application
is\n\n\n\n\n\n\nKafka ingest: this application consumes data from Kafka. In
other words, users have to stream the log into Kafka first. \n\n\n\n\n\n\nData
re-procesing, which includes raw log parser, ip zone joiner, sensitivity
information joiner. \n\n\n\n\n\n\nKafka sink: parsed data will flows into Kafka
again, which will be consumed by the alert engine. \n\n\n\n\n\n\nPolicy
evaluation: the alert engine (hosted in Alert Engine app) evaluates each data
event to check if the data violate the user defined policy. An alert is
generated if the data matches the policy.\n\n\n\n\n\n\n\n\nSetup \n
Installation\n\n\n\n\n\n\nChoose a site to install this application. For
example 'sandbox'\n\n\n\n\n\n\nInstall \"H
dfs Audit Log Monitor\" app step by step\n\n\n\n\n\n\n\n\n\n\n\n\nHow to
collect the log\n\n\nTo collect the raw audit log on namenode servers, a log
collector is needed. Users can choose any tools they like. There are some
common solutions available: \nlogstash\n, \nfilebeat\n, log4j appender, etcs.
\n\n\nFor detailed instruction, refer to: \nHow to stream audit log into
Kafka\n\n\nSample policies\n\n\n1. monitor file/folder operations\n\n\nDelete a
file/folder on HDFS. \n\n\nfrom
HDFS_AUDIT_LOG_ENRICHED_STREAM_SANDBOX[str:contains(src,'/tmp/test/subtest')
and ((cmd=='rename' and str:contains(dst, '.Trash')) or cmd=='delete')] select
* group by user insert into
hdfs_audit_log_enriched_stream_out\n\n\n\n\nHDFS_AUDIT_LOG_ENRICHED_STREAM_SANDBOX
is the input stream name, and hdfs_audit_log_enriched_stream_out is the output
stream name, the content between [] is the monitoring conditions. \ncmd\n,
\nsrc\n and \ndst\n is the fields of hdfs audit logs.\n\n\n\n\n2. classify the
file/folde
r on HDFS\n\n\nUsers may want to mark some folders/files on HDFS as sensitive
content. For example, by marking '/sys/soj' as \"SOJ\", users can monitor any
operations they care about on 'sys/soj' and its subfolders/files.\n\n\nfrom
HDFS_AUDIT_LOG_ENRICHED_STREAM_SANDBOX[sensitivityType=='SOJ' and
cmd=='delete')] select * group by user insert into
hdfs_audit_log_enriched_stream_out\n\n\n\n\nThe example policy monitors the
'delete' operation on files/subfolders under /sys/soj. \n\n\n3. Classify the IP
Zone\n\n\nIn some cases, the ips are classified into different zones. For some
zone, it may have higher secrecy. Eagle providers ways to monitor user
activities on IP level. \n\n\nfrom
HDFS_AUDIT_LOG_ENRICHED_STREAM_SANDBOX[securityZone=='SECURITY' and
cmd=='delete')] select * group by user insert into
hdfs_audit_log_enriched_stream_out\n\n\n\n\nThe example policy monitors the
'delete' operation on hosts in 'SECURITY' zone. \n\n\nQuestions on this
application\n\n\n\n\nJMX Monitoring\n\n\
n\n\n\n\nApplication \"\nHADOOP_JMX_METRIC_MONITOR\n\" provide embedded
collector script to ingest hadoop/hbase jmx metric as eagle stream and provide
ability to define alert policy and detect anomaly in real-time from
metric.\n\n\n\n\n\n\n\n\nFields\n\n\n\n\n\n\n\n\n\n\n\n\nType\n\n\nHADOOP_JMX_METRIC_MONITOR\n\n\n\n\n\n\nVersion\n\n\n0.5.0-version\n\n\n\n\n\n\nDescription\n\n\nCollect
JMX Metric and monitor in
real-time\n\n\n\n\n\n\nStreams\n\n\nHADOOP_JMX_METRIC_STREAM\n\n\n\n\n\n\nConfiguration\n\n\nJMX
Metric Kafka Topic (default: hadoop_jmx_metric_{SITE_ID})\nKafka Broker List
(default: localhost:6667)\n\n\n\n\n\n\n\n\n\n\n\n\nSetup \n
Installation\n\n\n\n\n\n\nMake sure already setup a site (here use a demo site
named \"sandbox\").\n\n\n\n\n\n\nInstall \"Hadoop JMX Monitor\" app in eagle
server.\n\n\n\n\n\n\n\n\nConfigure Application
settings.\n\n\n\n\n\n\n\n\nEnsure a kafka topic named
hadoop_jmx_metric_{SITE_ID} (In current guide, it should be
hadoop_jmx_metric_sandbox)\n\n
\n\n\n\n\nSetup metric collector for monitored Hadoop/HBase using
hadoop_jmx_collector and modify the configuration.\n\n\n\n\n\n\nCollector
scripts: \nhadoop_jmx_collector\n\n\n\n\n\n\nRename config-sample.json to
config.json: \nconfig-sample.json\n\n\n{\n env: {\n site:
\"sandbox\",\n name_node: {\n hosts: [\n
\"sandbox.hortonworks.com\"\n ],\n port: 50070,\n
https: false\n },\n resource_manager: {\n hosts:
[\n \"sandbox.hortonworks.com\"\n ],\n
port: 50030,\n https: false\n }\n },\n inputs: [{\n
component: \"namenode\",\n host: \"server.eagle.apache.org\",\n
port: \"50070\",\n https: false,\n kafka_topic:
\"nn_jmx_metric_sandbox\"\n }, {\n component: \"resourcemanager\",\n
host: \"server.eagle.apache.org\",\n port: \"8088\",\n
https: false,\n kafka_t
opic: \"rm_jmx_metric_sandbox\"\n }, {\n component: \"datanode\",\n
host: \"server.eagle.apache.org\",\n port: \"50075\",\n
https: false,\n kafka_topic: \"dn_jmx_metric_sandbox\"\n }],\n
filter: {\n monitoring.group.selected: [\n \"hadoop\",\n
\"java.lang\"\n ]\n },\n output: {\n kafka: {\n
brokerList: [\n \"localhost:9092\"\n ]\n
}\n }\n}\n\n\n\n\n\n\n\n\n\n\n\nClick \"Install\" button then you will see
the HADOOP_JMX_METRIC_STREAM_{SITE_ID} in Streams.\n\n\n\n\n\n\n\n\nDefine JMX
Alert Policy\n\n\n\n\n\n\nGo to \"Define Policy\".\n\n\n\n\n\n\nSelect
HADOOP_JMX_METRIC_MONITOR related streams.\n\n\n\n\n\n\nDefine SQL-Like policy,
for example\n\n\nfrom HADOOP_JMX_METRIC_STREAM_SANDBOX[metric==\"cpu.usage\"
and value \n 0.9]\nselect site,host,component,value\ninsert into
HADOOP_CPU_USAGE_GT_90_ALERT;\n\n\n\nAs seen in below screenshot:\n\n\n\n\n\n
\n\n\nStream Schema\n\n\n\n\n\n\nSchema\n\n\n\n\n\n\n\n\nStream
Name\n\n\nStream Schema\n\n\nTime
Series\n\n\n\n\n\n\n\n\n\n\nHADOOP_JMX_METRIC_MONITOR\n\n\nhost\n:
STRING\ntimestamp\n: LONG\nmetric\n: STRING\ncomponent\n: STRING\nsite\n:
STRING\nvalue\n: DOUBLE\n\n\nTrue\n\n\n\n\n\n\n\n\n\n\n\n\nMetrics
List\n\n\n\n\nPlease refer to the \nHadoop JMX Metrics List\n and see which
metrics you're interested in.\n\n\n\n\n\n\nJob Performance
Monitoring\n\n\nMonitor Requirements\n\n\n\n\nFinished/Running Job
Details\n\n\nJob Metrics(Job Counter/Statistics) Aggregation\n\n\nAlerts(Job
failure/Job slow)\n\n\n\n\nApplications\n\n\n\n\n\n\nApplication
Table\n\n\n\n\n\n\n\n\napplication\n\n\nresponsibility\n\n\n\n\n\n\n\n\n\n\nMap
Reduce History Job Monitoring\n\n\nparse mr history job logs from
hdfs\n\n\n\n\n\n\nMap Reduce Running Job Monitoring\n\n\nget mr running job
details from resource manager\n\n\n\n\n\n\nMap Reduce Metrics
Aggregation\n\n\naggregate metrics generated by applications ab
ove\n\n\n\n\n\n\n\n\n\n\n\n\nData Ingestion And Process\n\n\n\n\n\n\nWe build
storm topology to fulfill requirements for each
application.\n\n\n\n\n\n\n\n\nMap Reduce History Job Monitoring (Figure
1)\n\n\n\n\nRead Spout\n\n\nread/parse history job logs from HDFS and flush to
eagle service(storage is Hbase)\n\n\n\n\n\n\nSink Bolt\n\n\nconvert parsed jobs
to streams and write to data sink\n\n\n\n\n\n\n\n\n\n\nMap Reduce Running Job
Monitoring (Figure 2)\n\n\nRead Spout\n\n\nfetch running job list from resource
manager and emit to Parse Bolt\n\n\n\n\n\n\nParse Bolt\n\n\nfor each running
job, fetch job detail/job counter/job configure/tasks from resource
manager\n\n\n\n\n\n\n\n\n\n\nMap Reduce Metrics Aggregation (Figure
3)\n\n\nDivide Spout\n\n\ndivide time period(need to be aggregated) to small
pieces and emit to Aggregate Bolt\n\n\n\n\n\n\nAggregate Bolt\n\n\naggregate
metrics for given time period received from Divide
Spout\n\n\n\n\n\n\n\n\n\n\n\n\nSetup \n Installation\n\n\n\n\n\n
\nMake sure already setup a site (here use a demo site named
\"sandbox\").\n\n\n\n\n\n\nInstall \"Map Reduce History Job\" app in eagle
server(Take this application as an example).\n\n\n\n\n\n\nConfigure Application
settings\n\n\n\n\n\n\n\n\nEnsure a kafka topic named
{SITE_ID}_map_reduce_failed_job (In current guide, it should be
sandbox_map_reduce_failed_job) will be created.\n\n\n\n\n\n\nClick \"Install\"
button then you will see the MAP_REDUCE_FAILED_JOB_STREAM_{SITE_ID} in
Alert-\nStreams.\n \n\n This application will write stream data to kafka
topic(created by last step)\n\n\n\n\n\n\nIntegration With Alert Engine\n\n\nIn
order to integrate applications with alert engine and send alerts, follow below
steps(Take Map Reduce History Job application as an
example):\n\n\n\n\n\n\ndefine stream and configure data sink\n\n\n\n\ndefine
stream in resource/META-INF/providers/xxxProviders.xml\nFor example,
MAP_REDUCE_FAILED_JOB_STREAM_{SITE_ID}\n\n\nconfigure data sink\nFor example, cr
eate kafka topic {SITE_ID}_map_reduce_failed_job\n\n\n\n\n\n\n\n\ndefine
policy\n\n\n\n\n\n\nFor example, if you want to receive map reduce job failure
alerts, you can define policies (SiddhiQL) as the following:\n\n\nfrom
map_reduce_failed_job_stream[site==\nsandbox\n and
currentState==\nFAILED\n]\nselect site, queue, user, jobType, jobId,
submissionTime, trackingUrl, startTime, endTime\ngroup by jobId insert into
map_reduce_failed_job_stream_out\n\n\n\n\n\n\n\n\nview alerts\n\n\n\n\nYou can
view alerts in Alert-\nalerts page.\n\n\nStream Schema\n\n\nAll columns above
are predefined in stream map_reduce_failed_job_stream defined
in\n\n\neagle-jpm/eagle-jpm-mr-history/src/main/resources/META-INF/providers/org.apache.eagle.jpm.mr.history.MRHistoryJobApplicationProvider.xml\n\n\n\nThen,
enable the policy in web ui after it's created. Eagle will schedule it
automatically.\n\n\n\n\nTopology Health Check\n\n\n\n\n\n\nApplication
\"TOPOLOGY HEALTH CHECK\" aims to monior those servies with
a master-slave structured topology and provide metrics at host
level.\n\n\n\n\n\n\n\n\nFields\n\n\n\n\n\n\n\n\n\n\n\n\nType\n\n\nTOPOLOGY_HEALTH_CHECK\n\n\n\n\n\n\nVersion\n\n\n0.5.0-version\n\n\n\n\n\n\nDescription\n\n\nCollect
MR,HBASE,HDFS node status and cluster
ratio\n\n\n\n\n\n\nStreams\n\n\nTOPOLOGY_HEALTH_CHECK_STREAM\n\n\n\n\n\n\nConfiguration\n\n\nTopology
Health Check Topic (default: topology_health_check)\nKafka Broker List
(default: sandobox.hortonworks.com:6667)\n\n\n\n\n\n\n\n\n\n\n\n\nSetup \n
Installation\n\n\n\n\n\n\nMake sure already setup a site (here use a demo site
named \"sandbox\").\n\n\n\n\n\n\nInstall \"Topology Health Check\" app in eagle
server.\n\n\n\n\n\n\n\n\nConfigure Application
settings.\n\n\n\n\n\n\n\n\nEnsure the existence of a kafka topic named
topology_health_check (In current guide, it should be
topology_health_check).\n\n\n\n\n\n\nClick \"Install\" button then you will see
the TOPOLOGY_HEALTH_CHECK_STREAM_{SITE_ID} on \"Streams\" page (Stream
s could be navigated in left-nav).\n\n\n\n\n\n\n\n\nDefine Health Check Alert
Policy\n\n\n\n\n\n\nGo to \"Define Policy\".\n\n\n\n\n\n\nSelect
TOPOLOGY_HEALTH_CHECK related streams.\n\n\n\n\n\n\nDefine SQL-Like policy, for
example\n\n\nfrom TOPOLOGY_HEALTH_CHECK_STREAM_SANDBOX[status=='dead'] select *
insert into topology_health_check_stream_out;\n\n\n\n\n\n\n\n\n\n\n\nHadoop
Queue Monitoring\n\n\n\n\n\n\nThis application collects metrics of Resource
Manager in the following aspects:\n\n\n\n\n\n\nScheduler Info of the cluster:
http://{RM_HTTP_ADDRESS}:{PORT}/ws/v1/cluster/scheduler\n\n\n\n\n\n\nApplications
of the cluster:
http://{RM_HTTP_ADDRESS}:{PORT}/ws/v1/cluster/apps\n\n\n\n\n\n\nOverall metrics
of the cluster: http://{RM_HTTP_ADDRESS}:{PORT}/ws/v1/cluster/metrics\n\n\nby
version 0.5-incubating, mainly focusing at metrics\n - `appsPending`\n -
`allocatedMB`\n - `totalMB`\n - `availableMB`\n - `reservedMB`\n -
`allocatedVirtualCores`.\n\n\n\n\n\n\n\n\n\n\n\nSetup \n Installatio
n\n\n\n\n\n\n\nMake sure already setup a site (here use a demo site named
\"sandbox\").\n\n\n\n\n\n\nFrom left-nav list, navigate to application managing
page by \"\nIntegration\n\" \n \"\nSites\n\", and hit link \"\nsandbox\n\" on
right.\n\n\n\n\n\n\n\n\nInstall \"Hadoop Queue Monitor\" by clicking
\"install\" button of the application.\n\n\n\n\n\n\n\n\nIn the pop-up layout,
select running mode as \nLocal\n or \nCluster\n.\n\n\n\n\n\n\n\n\nSet the
target jar of eagle's topology assembly that has existed in eagle server,
indicating the absolute path ot it. As in the following
screenshot:\n\n\n\n\n\n\n\n\nSet Resource Manager endpoint urls field, separate
values with comma if there are more than 1 url (e.g. a secondary node for
HA).\n\n\n\n\n\n\n\n\nSet fields \"\nStorm Worker Number\n\", \"\nParallel
Tasks Per Bolt\n\", and \"\nFetching Metric Interval in Seconds\n\", or leave
them as default if they fit your needs.\n\n\n\n\n\n\n\n\nFinally, hit
\"\nInstall\n\" button to complete it
.\n\n\n\n\n\n\nUse of the application\n\n\n\n\n\n\nThere is no need to define
policies for this applicatoin to work, it could be integrated with \"\nJob
Performance Monitoring Web\n\" application and consequently seen on cluster
dashboard, as long as the latter application is installed too. See an exmple in
the following screenshot:",
+ "title": "Applications"
+ },
+ {
+ "location": "/applications/#hdfs-data-activity-monitoring",
+ "text": "",
+ "title": "HDFS Data Activity Monitoring"
+ },
+ {
+ "location": "/applications/#monitor-requirements",
+ "text": "This application aims to monitor user activities on HDFS
via the hdfs audit log. Once any abnormal user activity is detected, an alert
is sent in several seconds. The whole pipeline of this application is Kafka
ingest: this application consumes data from Kafka. In other words, users have
to stream the log into Kafka first. Data re-procesing, which includes raw
log parser, ip zone joiner, sensitivity information joiner. Kafka sink:
parsed data will flows into Kafka again, which will be consumed by the alert
engine. Policy evaluation: the alert engine (hosted in Alert Engine app)
evaluates each data event to check if the data violate the user defined policy.
An alert is generated if the data matches the policy.",
+ "title": "Monitor Requirements"
+ },
+ {
+ "location": "/applications/#setup-installation",
+ "text": "Choose a site to install this application. For example
'sandbox' Install \"Hdfs Audit Log Monitor\" app step by step",
+ "title": "Setup & Installation"
+ },
+ {
+ "location": "/applications/#how-to-collect-the-log",
+ "text": "To collect the raw audit log on namenode servers, a log
collector is needed. Users can choose any tools they like. There are some
common solutions available: logstash , filebeat , log4j appender, etcs. For
detailed instruction, refer to: How to stream audit log into Kafka",
+ "title": "How to collect the log"
+ },
+ {
+ "location": "/applications/#sample-policies",
+ "text": "",
+ "title": "Sample policies"
+ },
+ {
+ "location": "/applications/#1-monitor-filefolder-operations",
+ "text": "Delete a file/folder on HDFS. from
HDFS_AUDIT_LOG_ENRICHED_STREAM_SANDBOX[str:contains(src,'/tmp/test/subtest')
and ((cmd=='rename' and str:contains(dst, '.Trash')) or cmd=='delete')] select
* group by user insert into hdfs_audit_log_enriched_stream_out
HDFS_AUDIT_LOG_ENRICHED_STREAM_SANDBOX is the input stream name, and
hdfs_audit_log_enriched_stream_out is the output stream name, the content
between [] is the monitoring conditions. cmd , src and dst is the fields
of hdfs audit logs.",
+ "title": "1. monitor file/folder operations"
+ },
+ {
+ "location": "/applications/#2-classify-the-filefolder-on-hdfs",
+ "text": "Users may want to mark some folders/files on HDFS as
sensitive content. For example, by marking '/sys/soj' as \"SOJ\", users can
monitor any operations they care about on 'sys/soj' and its subfolders/files.
from HDFS_AUDIT_LOG_ENRICHED_STREAM_SANDBOX[sensitivityType=='SOJ' and
cmd=='delete')] select * group by user insert into
hdfs_audit_log_enriched_stream_out The example policy monitors the 'delete'
operation on files/subfolders under /sys/soj.",
+ "title": "2. classify the file/folder on HDFS"
+ },
+ {
+ "location": "/applications/#3-classify-the-ip-zone",
+ "text": "In some cases, the ips are classified into different
zones. For some zone, it may have higher secrecy. Eagle providers ways to
monitor user activities on IP level. from
HDFS_AUDIT_LOG_ENRICHED_STREAM_SANDBOX[securityZone=='SECURITY' and
cmd=='delete')] select * group by user insert into
hdfs_audit_log_enriched_stream_out The example policy monitors the 'delete'
operation on hosts in 'SECURITY' zone.",
+ "title": "3. Classify the IP Zone"
+ },
+ {
+ "location": "/applications/#questions-on-this-application",
+ "text": "",
+ "title": "Questions on this application"
+ },
+ {
+ "location": "/applications/#jmx-monitoring",
+ "text": "Application \" HADOOP_JMX_METRIC_MONITOR \" provide
embedded collector script to ingest hadoop/hbase jmx metric as eagle stream and
provide ability to define alert policy and detect anomaly in real-time from
metric. Fields Type HADOOP_JMX_METRIC_MONITOR Version
0.5.0-version Description Collect JMX Metric and monitor in real-time
Streams HADOOP_JMX_METRIC_STREAM Configuration JMX Metric Kafka Topic
(default: hadoop_jmx_metric_{SITE_ID}) Kafka Broker List (default:
localhost:6667)",
+ "title": "JMX Monitoring"
+ },
+ {
+ "location": "/applications/#setup-installation_1",
+ "text": "Make sure already setup a site (here use a demo site
named \"sandbox\"). Install \"Hadoop JMX Monitor\" app in eagle server.
Configure Application settings. Ensure a kafka topic named
hadoop_jmx_metric_{SITE_ID} (In current guide, it should be
hadoop_jmx_metric_sandbox) Setup metric collector for monitored Hadoop/HBase
using hadoop_jmx_collector and modify the configuration. Collector scripts:
hadoop_jmx_collector Rename config-sample.json to config.json:
config-sample.json {\n env: {\n site: \"sandbox\",\n
name_node: {\n hosts: [\n
\"sandbox.hortonworks.com\"\n ],\n port: 50070,\n
https: false\n },\n resource_manager: {\n hosts:
[\n \"sandbox.hortonworks.com\"\n ],\n
port: 50030,\n https: false\n }\n },\n inputs: [{\n
component: \"namenode\",\n host: \"server.eagle.
apache.org\",\n port: \"50070\",\n https: false,\n
kafka_topic: \"nn_jmx_metric_sandbox\"\n }, {\n component:
\"resourcemanager\",\n host: \"server.eagle.apache.org\",\n port:
\"8088\",\n https: false,\n kafka_topic:
\"rm_jmx_metric_sandbox\"\n }, {\n component: \"datanode\",\n
host: \"server.eagle.apache.org\",\n port: \"50075\",\n https:
false,\n kafka_topic: \"dn_jmx_metric_sandbox\"\n }],\n filter:
{\n monitoring.group.selected: [\n \"hadoop\",\n
\"java.lang\"\n ]\n },\n output: {\n kafka: {\n
brokerList: [\n \"localhost:9092\"\n ]\n }\n
}\n} Click \"Install\" button then you will see the
HADOOP_JMX_METRIC_STREAM_{SITE_ID} in Streams.",
+ "title": "Setup & Installation"
+ },
+ {
+ "location": "/applications/#define-jmx-alert-policy",
+ "text": "Go to \"Define Policy\". Select
HADOOP_JMX_METRIC_MONITOR related streams. Define SQL-Like policy, for
example from HADOOP_JMX_METRIC_STREAM_SANDBOX[metric==\"cpu.usage\" and value
0.9]\nselect site,host,component,value\ninsert into
HADOOP_CPU_USAGE_GT_90_ALERT; As seen in below screenshot:",
+ "title": "Define JMX Alert Policy"
+ },
+ {
+ "location": "/applications/#stream-schema",
+ "text": "Schema Stream Name Stream Schema Time Series
HADOOP_JMX_METRIC_MONITOR host : STRING timestamp : LONG metric : STRING
component : STRING site : STRING value : DOUBLE True",
+ "title": "Stream Schema"
+ },
+ {
+ "location": "/applications/#metrics-list",
+ "text": "Please refer to the Hadoop JMX Metrics List and see
which metrics you're interested in.",
+ "title": "Metrics List"
+ },
+ {
+ "location": "/applications/#job-performance-monitoring",
+ "text": "",
+ "title": "Job Performance Monitoring"
+ },
+ {
+ "location": "/applications/#monitor-requirements_1",
+ "text": "Finished/Running Job Details Job Metrics(Job
Counter/Statistics) Aggregation Alerts(Job failure/Job slow)",
+ "title": "Monitor Requirements"
+ },
+ {
+ "location": "/applications/#applications",
+ "text": "Application Table application responsibility
Map Reduce History Job Monitoring parse mr history job logs from hdfs Map
Reduce Running Job Monitoring get mr running job details from resource manager
Map Reduce Metrics Aggregation aggregate metrics generated by applications
above",
+ "title": "Applications"
+ },
+ {
+ "location": "/applications/#data-ingestion-and-process",
+ "text": "We build storm topology to fulfill requirements for each
application. Map Reduce History Job Monitoring (Figure 1) Read Spout
read/parse history job logs from HDFS and flush to eagle service(storage is
Hbase) Sink Bolt convert parsed jobs to streams and write to data sink
Map Reduce Running Job Monitoring (Figure 2) Read Spout fetch running job
list from resource manager and emit to Parse Bolt Parse Bolt for each
running job, fetch job detail/job counter/job configure/tasks from resource
manager Map Reduce Metrics Aggregation (Figure 3) Divide Spout divide
time period(need to be aggregated) to small pieces and emit to Aggregate Bolt
Aggregate Bolt aggregate metrics for given time period received from Divide
Spout",
+ "title": "Data Ingestion And Process"
+ },
+ {
+ "location": "/applications/#setup-installation_2",
+ "text": "Make sure already setup a site (here use a demo site
named \"sandbox\"). Install \"Map Reduce History Job\" app in eagle
server(Take this application as an example). Configure Application settings
Ensure a kafka topic named {SITE_ID}_map_reduce_failed_job (In current
guide, it should be sandbox_map_reduce_failed_job) will be created. Click
\"Install\" button then you will see the MAP_REDUCE_FAILED_JOB_STREAM_{SITE_ID}
in Alert- Streams.\n \n This application will write stream data to kafka
topic(created by last step)",
+ "title": "Setup & Installation"
+ },
+ {
+ "location": "/applications/#integration-with-alert-engine",
+ "text": "In order to integrate applications with alert engine and
send alerts, follow below steps(Take Map Reduce History Job application as an
example): define stream and configure data sink define stream in
resource/META-INF/providers/xxxProviders.xml\nFor example,
MAP_REDUCE_FAILED_JOB_STREAM_{SITE_ID} configure data sink\nFor example,
create kafka topic {SITE_ID}_map_reduce_failed_job define policy For
example, if you want to receive map reduce job failure alerts, you can define
policies (SiddhiQL) as the following: from map_reduce_failed_job_stream[site==
sandbox and currentState== FAILED ]\nselect site, queue, user, jobType, jobId,
submissionTime, trackingUrl, startTime, endTime\ngroup by jobId insert into
map_reduce_failed_job_stream_out view alerts You can view alerts in Alert-
alerts page.",
+ "title": "Integration With Alert Engine"
+ },
+ {
+ "location": "/applications/#stream-schema_1",
[... 247 lines stripped ...]
Propchange: eagle/site/docs/v0.5.0/mkdocs/search_index.json
------------------------------------------------------------------------------
svn:eol-style = native
