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Posted to commits@eagle.apache.org by ha...@apache.org on 2017/04/03 11:55:04 UTC
[56/84] eagle git commit: Merge site source code from
https://github.com/geteagle/eaglemonitoring.github.io
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+---
+layout: doc
+title: "How to stream hdfs log data into Kafka"
+permalink: /docs/import-hdfs-auditLog.html
+---
+
+As Apache Eagle consumes the data via Kafka[^KAFKA] topics in some topologies, such as HDFS audit log. To enable the full function of monitoring, a user needs to stream its data into a Kafka topic.
+
+There are two ways to do that. The first one is **Logstash**, which naturally supports Kafka as the output plugin; the second one is to
+install a **namenode log4j Kafka appender**.
+
+### Logstash-kafka
+
+* **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>
+ bin/kafka-topics.sh --create --zookeeper localhost:2181 --replication-factor 1 --partitions 1 --topic sandbox_hdfs_audit_log
+
+* **Step 2**: Install Logstash-kafka plugin
+
+ * For Logstash 1.5.x, logstash-kafka has been intergrated into [logstash-input-kafka](https://github.com/logstash-plugins/logstash-input-kafka) and [logstash-output-kafka](https://github.com/logstash-plugins/logstash-output-kafka),
+ and released with the 1.5 version of Logstash. So you can directly use it.
+
+ * For Logstash 1.4.x, a user should install [logstash-kafka](https://github.com/joekiller/logstash-kafka) firstly. Notice that this version **does not support partition\_key\_format**.
+
+* **Step 3**: Create a Logstash configuration file under ${LOGSTASH_HOME}/conf. Here is a sample.
+
+ input {
+ file {
+ type => "hdp-nn-audit"
+ path => "/path/to/audit.log"
+ start_position => end
+ sincedb_path => "/var/log/logstash/"
+ }
+ }
+
+ filter{
+ if [type] == "hdp-nn-audit" {
+ grok {
+ match => ["message", "ugi=(?<user>([\w\d\-]+))@|ugi=(?<user>([\w\d\-]+))/[\w\d\-.]+@|ugi=(?<user>([\w\d.\-_]+))[\s(]+"]
+ }
+ }
+ }
+
+ output {
+ if [type] == "hdp-nn-audit" {
+ kafka {
+ codec => plain {
+ format => "%{message}"
+ }
+ broker_list => "localhost:9092"
+ topic_id => "sandbox_hdfs_audit_log"
+ request_required_acks => 0
+ request_timeout_ms => 10000
+ producer_type => "async"
+ message_send_max_retries => 3
+ retry_backoff_ms => 100
+ queue_buffering_max_ms => 5000
+ queue_enqueue_timeout_ms => 5000
+ batch_num_messages => 200
+ send_buffer_bytes => 102400
+ client_id => "hdp-nn-audit"
+ partition_key_format => "%{user}"
+ }
+ # stdout { codec => rubydebug }
+ }
+ }
+
+* **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`
+
+### Log4j Kafka Appender
+
+> Notice that if you use Ambari[^AMBARI], such as in sandbox, you **must** follow below steps via Ambari UI. In addition, restarting namenode is required.
+
+* **Step 1**: Create a Kafka topic. Here is a example Kafka command for creating topic "sandbox_hdfs_audit_log"
+
+ cd <kafka-home>
+ bin/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
+ log4j.appender.KAFKA_HDFS_AUDIT.Topic=sandbox_hdfs_audit_log
+ log4j.appender.KAFKA_HDFS_AUDIT.BrokerList=sandbox.hortonworks.com:6667
+ log4j.appender.KAFKA_HDFS_AUDIT.KeyClass=org.apache.eagle.log4j.kafka.hadoop.AuditLogKeyer
+ log4j.appender.KAFKA_HDFS_AUDIT.Layout=org.apache.log4j.PatternLayout
+ log4j.appender.KAFKA_HDFS_AUDIT.Layout.ConversionPattern=%d{ISO8601} %p %c{2}: %m%n
+ log4j.appender.KAFKA_HDFS_AUDIT.ProducerType=async
+ #log4j.appender.KAFKA_HDFS_AUDIT.BatchSize=1
+ #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
+
+
+
+
+---
+
+#### *Footnotes*
+
+[^AMBARI]:*all mentions of "ambari" on this page represent Apache Ambari.*
+[^KAFKA]:*All mentions of "kafka" on this page represent Apache Kafka.*
http://git-wip-us.apache.org/repos/asf/eagle/blob/0ecb7c1c/eagle-site/index.html
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diff --git a/eagle-site/index.html b/eagle-site/index.html
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+<!DOCTYPE html>
+
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+<title>Apache Eagle - Analyze Big Data Platforms For Security and Performance</title>
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+ <div class="hometext">
+ <h2 style="font-weight:500;">Apache Eagle</h2>
+ <h3>Analyze Big Data Platforms For Security and Performance</h3>
+ </div>
+ </div>
+
+ <div class="download" style="margin-left:10px;" title="Documents"><a href="docs/">DOCS</a></div>
+ <div class="download"><a href="docs/download-latest.html" title="Downloads">DOWNLOAD</a></div>
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+ <h2 class="sectiontile">ABOUT APACHE EAGLE</h2>
+ <div class="col-md-12">
+ <p style="width:80%; margin-left:auto; margin-right:auto;"> Apache Eagle (called Eagle in the following) is an open source analytics solution for identifying security and performance issues instantly on big data platforms, e.g. Apache Hadoop, Apache Spark etc. It analyzes data activities, yarn applications, jmx metrics, and daemon logs etc., provides state-of-the-art alert engine to identify security breach, performance issues and shows insights. </p>
+ <br/>
+ <p style="width:80%; margin-left:auto; margin-right:auto;"> Big data platform normally generates huge amount of operational logs and metrics in realtime. Eagle is founded to solve hard problems in securing and tuning performance for big data platforms by ensuring metrics, logs always available and alerting immediately even under huge traffic.</p>
+ <div class="sepline"></div>
+ <P>Eagle is accounced to be a Top Level Project (TLP) of Apache Software Foundation (ASF) on Jan. 10, 2017.</p>
+ <!--
+ <p>Eagle has been accepted as an Apache Incubator Project on Oct 26, 2015.</P>
+ -->
+ <div class="sepline"></div>
+ <p>Eagle analyzes big data platforms and reports issues in 3 steps:</p>
+ </div>
+ </div>
+ <section id="cd-timeline" class="cd-container" style="margin-top:-3px;">
+ <div class="cd-timeline-block">
+ <div class="cd-timeline-img cd-picture"> <img src="images/step1.png" alt="Picture"> </div>
+ <div class="cd-timeline-content service-box-content">
+ <h3>Step 1 Data Integrations</h3>
+ <p>Streaming operational logs and data activities into Eagle platform, including but not limited to audit logs, map/reduce jobs, yarn resource usage, jmx metrics and various daemon logs etc.</p>
+ </div>
+ </div>
+ <div class="cd-timeline-block">
+ <div class="cd-timeline-img cd-movie"> <img src="images/step2.png" alt="Picture"> </div>
+ <div class="cd-timeline-content service-box-content">
+ <h3>Step 2 Realtime Analytics and Alerting</h3>
+ <p>Realtime stream data are normalized and then evaluated in alert engine with comprehensive policies including filter, slide window, batch window, and pattern match etc.</p>
+ </div>
+ </div>
+ <div class="cd-timeline-block">
+ <div class="cd-timeline-img cd-icon"> <img src="images/step3.png" alt="Picture"> </div>
+ <div class="cd-timeline-content service-box-content">
+ <h3>Step 3 Insights</h3>
+ <p>Generate alerts, show historical trend, and correlate alert with raw data</p>
+ </div>
+ </div>
+ <div class="cd-timeline-block">
+ <div class="cd-timeline-img cd-location"> <img src="images/step4.png" alt="Picture"> </div>
+ <div class="cd-timeline-content service-box-content">
+ <h3>Additional Bonus</h3>
+ <p>Eagle provides embedded, highly scalable alert engine for user to easily onboard any new data sources into Eagle platform and write comprehensive policies to alert for anomalies.</p>
+ </div>
+ </div>
+ </section>
+ </div>
+</div>
+
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+ <div class="row" style="margin-top:50px;">
+ <div class="col-md-4 workmargin">
+ <h4>Discussion and Contribute</h4>
+ <div style="text-align:left">
+ <p>Get help using Eagle or contribute to the project</p>
+ <ul>
+ <li>
+ <a href="/docs/community.html"><b>Mailing Lists</b></a>
+ </li>
+ <li>
+ <a href="https://issues.apache.org/jira/browse/EAGLE"><b>Issues Tracking</b></a>
+ </li>
+ <li>
+ <a href="https://cwiki.apache.org/confluence/display/EAG/Contributing+to+Eagle"><b>How to Contribute</b></a>
+ </li>
+ </ul>
+ </div>
+ </div>
+ <div class="col-md-4 workmargin">
+ <h4>Events and Meetups</h4>
+ <div style="text-align:left">
+ <p>Learn more about Eagle from Conferences</p>
+ <ul>
+ <li><a href="/docs/community.html"><b>Conferences</b></a></li>
+ <li><a href="/docs/community.html"><b>Meetups</b></a></li>
+ <li><a href="/docs/community.html"><b>News</b></a></li>
+ </ul>
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+ </div>
+ <div class="col-md-4 workmargin">
+ <h4>Find Us at</h4>
+ <div style="text-align:left">
+
+
+ <p>Learn latest updates about Eagle through:</p>
+ <div class="row">
+ <div class="col-md-6">
+<iframe src="https://ghbtns.com/github-btn.html?user=apache&repo=eagle&type=star&count=true" frameborder="0" scrolling="0" width="150px" height="20px"></iframe>
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+<br/>
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+</div>
+
+<!-- footer start -->
+<div class="footerwrapper">
+ <div class="container">
+ <div class="row">
+ <div class="col-md-12"><div style="margin-left:auto; margin-right:auto; text-align:center;font-size:12px;">
+<div>
+<a href="http://www.apache.org">
+<img id="asf-logo" alt="Apache Software Foundation" src="/images/apache-logo-small.gif">
+</a>
+</div>
+<div>
+Copyright � 2015 <a href="http://www.apache.org">The Apache Software Foundation</a>, Licensed under the <a href="http://www.apache.org/licenses/LICENSE-2.0">Apache License, Version 2.0</a>.
+</div>
+<div>
+Apache Eagle, Eagle, Apache, and the Apache feather logo are trademarks of The Apache Software Foundation.
+</div>
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+ var href = $(this).attr("href"),
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http://git-wip-us.apache.org/repos/asf/eagle/blob/0ecb7c1c/eagle-site/install.md
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+---
+layout: doc
+title: "Install Apache Eagle"
+permalink: /docs/installation.html
+---
+
+### Install Apache Eagle (called Eagle in the following) to Sandbox
+
+#### Pre-requisites
+
+> To insall eagle on a sandbox you need to have orcale virtual box and HDP sandbox image.
+
+1. [Oracle VirtualBox](https://www.virtualbox.org/wiki/Downloads).
+2. [Hortonworks Sandbox](http://hortonworks.com/products/hortonworks-sandbox/#install) v 2.2.4 or later.
+
+#### Register HDP sandbox
+
+1. [Register](http://127.0.0.1:8888/) Hortonworks sandbox.
+2. [Enable Ambari](http://127.0.0.1:8000/). Click on Enable Button.
+3. [Login](http://127.0.0.1:8080) as admin/admin.
+
+#### Install Eagle
+
+* **Step 1**: Clone stable version from [eagle github](https://github.com/apache/eagle/releases/tag/v0.4.0-incubating)
+> Build project mvn clean install -DskipTests=true
+
+* **Step 2**: Download eagle-bin-0.1.0.tar.gz package from successful build into your HDP sandbox.
+
+ * Option 1: `scp -P 2222 eagle/eagle-assembly/target/eagle-0.1.0-bin.tar.gz root@127.0.0.1:/usr/hdp/current/`
+
+
+ * Option 2: Create shared directory between host and Sandbox, and restart Sandbox. Then you can find the shared directory under /media in Sandbox.
+
+* **Step 3**: Extract eagle tarball package
+
+ $ cd /usr/hdp/current
+ $ tar -zxvf eagle-0.1.0-bin.tar.gz
+ $ mv eagle-0.1.0 eagle
+
+* **Step 4**: Add root as a HBase[^HBASE] superuser via [Ambari](http://127.0.0.1:8080/#/main/services/HBASE/configs) (Optional, a user can operate HBase by sudo su hbase, as an alternative).
+
+* **Step 5**: Install Eagle Ambari[^AMBARI] service
+>
+ /usr/hdp/current/eagle/bin/eagle-ambari.sh install.
+
+* **Step 6**: Restart [Ambari](http://127.0.0.1:8000/) click on disable and enable Ambari back.
+
+* **Step 7**: Start HBase & Storm[^STORM] & Kafka[^KAFKA]
+From Ambari UI, restart any suggested components("Restart button on top") & Start Storm (Start "Nimbus" ,"Supervisor" & "Storm UI Server"), Kafka (Start "Kafka Broker") , HBase (Start "RegionServer" and " HBase Master")
+>
+
+
+* **Step 8**: Add Eagle Service To Ambari. (Click For Video)
+
+ * Click on "Add Service" under Actions button on Ambari Main page
+
+ 
+
+ * Select "Eagle" in list of services and proceed to install all eagle services.
+EagleServiceSuccess
+
+ 
+
+* **Step 9**: Add Policies and meta data required by running below script.
+
+ $ /usr/hdp/current/eagle/examples/sample-sensitivity-resource-create.sh
+ $ /usr/hdp/current/eagle/examples/sample-policy-create.sh
+
+
+---
+
+#### *Footnotes*
+
+[^HBASE]:*All mentions of "hbase" on this page represent Apache HBase.*
+[^AMBARI]:*All mentions of "ambari" on this page represent Apache Ambari.*
+[^KAFKA]:*All mentions of "kafka" on this page represent Apache Kafka.*
+[^STORM]:*All mentions of "storm" on this page represent Apache Storm.*
+
+
http://git-wip-us.apache.org/repos/asf/eagle/blob/0ecb7c1c/eagle-site/introduction.md
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diff --git a/eagle-site/introduction.md b/eagle-site/introduction.md
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+++ b/eagle-site/introduction.md
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+---
+layout: doc
+title: "Introduction"
+permalink: /docs/index.html
+---
+
+### Welcome to Apache Eagle
+
+> Apache Eagle (called Eagle in the following) is an open source analytics solution for identifying security and performance issues instantly on big data platforms e.g. Hadoop[^HADOOP], Spark[^SPARK], NoSQL etc. It analyzes data activities, yarn applications, jmx metrics, and daemon logs etc., provides state-of-the-art alert engine to identify security breach, performance issues and shows insights.
+
+### Basic Analytics and Monitoring Flow
+
+Normally 3 basic steps are involved in Eagle platform: Data Integration, Alerting Engine and Insights.
+
+* Data Integration normally is streaming process application, for example Apache Storm topology or Spark streaming application. During data integration, raw data can be transformed, normalized, analyzed etc. The output of data integration will be streamed into Alerting Engine and Insights.
+
+* Alerting Engine is highly scalable rule execution engine, which is embedded into Data Integration application as of Eagle 0.4.0. With alerting engine, user can create/update comprehensive policies on the fly.
+
+* Insights is for correlating alerts with raw data for user to explore root causes.
+
+Depending on data source, data integrations may use pull or push methods to stream data into Eagle platform, for example HDFS audit log can be pushed by logstash, but Hive[^HIVE] query log can be pulled by Hive Integration application.
+
+### Key Qualities
+
+* **Real Time**: We understand the importance of timing and acting fast in case of a security breach or performance issue. So we designed Eagle to make sure the alerts are generated immediately even for very high volume streaming data. This is achieved by running Eagle alert engine on top of streaming framework, e.g. Apache Storm with high performance CEP engine e.g. WSO2 Siddhi etc.
+
+* **Scalability**: At eBay we operate one of world's largest big data platforms Hadoop, Spark etc. The volume of operational logs and user activities generated by big data platforms is very huge. Eagle alert engine is well designed to evaluate polices against data in motion at cloud scale.This is achieved by dynamically partitioning data and policies based on policy semantics.
+
+* **Metadata Driven**: Eagle understands what policies are evaluated for what data source in what physical boxes. This thanks to Eagle metadata design and master/slave distributed computing architecture. Eagle alert engine's coordinator listens to metadata change, calculates snapshot for mapping policy to resource and then dynamically deploys snapshot onto storm spouts and bolts.
+
+* **Extensibility**: Eagle is designed with extensibility in mind. You can integrate many different data sources into Eagle platform with a few clicks.
+
+
+---
+
+#### *Footnotes*
+
+[^HADOOP]:*All mentions of "hadoop" on this page represent Apache Hadoop.*
+[^HIVE]:*All mentions of "hive" on this page represent Apache HIVE.*
+[^SPARK]:*All mentions of "spark" on this page represent Apache Spark.*
http://git-wip-us.apache.org/repos/asf/eagle/blob/0ecb7c1c/eagle-site/jmx-metric-monitoring.md
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+---
+layout: doc
+title: "JMX Metric Monitoring"
+permalink: /docs/jmx-metric-monitoring.html
+---
+
+JMX metric for Apache Hadoop namenode url [http://127.0.0.1:50070/jmx](http://127.0.0.1:50070/jmx) can be monitored using Apache Eagle (called Eagle in the following). Follow below steps to enable this feature in Eagle.
+
+1. Install Python script (To populate JMX metric values to Kafka[^KAFKA] topic periodically).
+2. Deploy "hadoopjmx" Storm[^STORM] topology.
+3. Create new site and policy in UI.
+4. Validate policy alert.
+
+<br/>
+
+
+### **Prerequisite**
+* Complete the setup from [Setup Environment](/docs/deployment-env.html)
+
+<br/>
+
+
+### **Setup**
+From Hortonworks sandbox just run below setup script to Install Pyton JMX script, Create Kafka topic, update Apache Hbase tables and deploy "hadoopjmx" Storm topology.
+
+ $ /usr/hdp/current/eagle/examples/hadoop-metric-sandbox-starter.sh
+ $ /usr/hdp/current/eagle/examples/hadoop-metric-policy-create.sh
+
+<br/>
+
+
+### **Application Setup in UI**
+1. Login to Eagle UI [http://localhost:9099/eagle-service/](http://localhost:9099/eagle-service/) using username and password as "admin" and "secret"
+2. Click on "Admin" from top right and click "Management" button.
+3. On Admin management page add "New Site" name "hadoopJmxMetricDataSource", by clicking on "New Site" link.
+
+4. Save the changes.
+5. On eagle home page you should see new tab called "METRIC", beside "DAM".
+6. Click on "JmxMetricMonitor" under "METRIC".
+
+You should see policy with name "safeModePolicy".
+
+<br/>
+
+
+### **Demo**
+
+* First make sure that Kafka topic "nn_jmx_metric_sandbox" is populated with JMX metric data periodically.(To make sure that python script is running)
+
+ $ /usr/hdp/2.2.4.2-2/kafka/bin/kafka-console-consumer.sh --zookeeper sandbox.hortonworks.com:2181 --topic nn_jmx_metric_sandbox
+
+* Genrate Alert by producing alert triggering message into Kafka topic.
+
+
+ $ /usr/hdp/2.2.4.2-2/kafka/bin/kafka-console-producer.sh --broker-list sandbox.hortonworks.com:6667 --topic nn_jmx_metric_sandbox
+ $ {"host": "localhost", "timestamp": 1457033916718, "metric": "hadoop.namenode.fsnamesystemstate.fsstate", "component": "namenode", "site": "sandbox", "value": 1.0}
+
+
+---
+
+#### *Footnotes*
+
+[^STORM]:*All mentions of "storm" on this page represent Apache Storm.*
+[^KAFKA]:*All mentions of "kafka" on this page represent Apache Kafka.*
+
http://git-wip-us.apache.org/repos/asf/eagle/blob/0ecb7c1c/eagle-site/js/custom.js
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diff --git a/eagle-site/js/custom.js b/eagle-site/js/custom.js
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+++ b/eagle-site/js/custom.js
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+jQuery(document).ready(function($) {
+
+ /************** Scroll Navigation *********************/
+ $('.navigation').singlePageNav({
+ currentClass: 'active'
+ });
+
+
+ /************** FlexSlider *********************/
+ $('.flexslider').flexslider({
+ animation: "fade",
+ directionNav: false
+ });
+
+
+ /************** Responsive Navigation *********************/
+
+ $('.menu-toggle-btn').click(function() {
+ $('.responsive-menu').stop(true, true).slideToggle();
+ });
+
+ /************** Menu Content Opening *********************/
+ $(".main_menu a, .responsive_menu a").click(function() {
+ var id = $(this).attr('class');
+ id = id.split('-');
+ $("#menu-container .content").hide();
+ $("#menu-container #menu-" + id[1]).addClass("animated fadeInDown").show();
+ $("#menu-container .homepage").hide();
+ $(".support").hide();
+ $(".testimonials").hide();
+ return false;
+ });
+
+ $(window).load(function() {
+ $("#menu-container .products").hide();
+ });
+
+ $(".main_menu a.home").addClass('active');
+
+ $(".main_menu a.home, .responsive_menu a.home").click(function() {
+ $("#menu-container .homepage").addClass("animated fadeInDown").show();
+ $(this).addClass('active');
+ $(".main_menu a.page2, .responsive_menu a.page2").removeClass('active');
+ $(".main_menu a.page3, .responsive_menu a.page3").removeClass('active');
+ $(".main_menu a.page5, .responsive_menu a.page5").removeClass('active');
+ return false;
+ });
+
+ $(".main_menu a.page2, .responsive_menu a.page2").click(function() {
+ $("#menu-container .service").addClass("animated fadeInDown").show();
+ $(this).addClass('active');
+ $(".main_menu a.home, .responsive_menu a.home").removeClass('active');
+ $(".main_menu a.page3, .responsive_menu a.page3").removeClass('active');
+ $(".main_menu a.page5, .responsive_menu a.page5").removeClass('active');
+ return false;
+ });
+
+ $(".main_menu a.page3, .responsive_menu a.page3").click(function() {
+ $("#menu-container .portfolio").addClass("animated fadeInDown").show();
+ $(".our-services").show();
+ $(this).addClass('active');
+ $(".main_menu a.page2, .responsive_menu a.page2").removeClass('active');
+ $(".main_menu a.home, .responsive_menu a.home").removeClass('active');
+ $(".main_menu a.page5, .responsive_menu a.page5").removeClass('active');
+ return false;
+ });
+
+ $(".main_menu a.page5, .responsive_menu a.page5").click(function() {
+ $("#menu-container .contact").addClass("animated fadeInDown").show();
+ $(this).addClass('active');
+ $(".main_menu a.page2, .responsive_menu a.page2").removeClass('active');
+ $(".main_menu a.page3, .responsive_menu a.page3").removeClass('active');
+ $(".main_menu a.home, .responsive_menu a.home").removeClass('active');
+
+ return false;
+ });
+
+
+
+
+ /************** LightBox *********************/
+
+
+ $("a.menu-toggle-btn").click(function() {
+ $(".responsive_menu").stop(true, true).slideToggle();
+ return false;
+ });
+
+ $(".responsive_menu a").click(function() {
+ $('.responsive_menu').hide();
+ });
+
+
+ var $timeline_block = $('.cd-timeline-block');
+
+ //hide timeline blocks which are outside the viewport
+ $timeline_block.each(function() {
+ if ($(this).offset().top > $(window).scrollTop() + $(window).height() * 0.75) {
+ $(this).find('.cd-timeline-img, .cd-timeline-content').addClass('is-hidden');
+ }
+ });
+
+ //on scolling, show/animate timeline blocks when enter the viewport
+ $(window).on('scroll', function() {
+ animationPage();
+ $timeline_block.each(function() {
+ if ($(this).offset().top <= $(window).scrollTop() + $(window).height() * 0.75 && $(this).find('.cd-timeline-img').hasClass('is-hidden')) {
+ $(this).find('.cd-timeline-img, .cd-timeline-content').removeClass('is-hidden').addClass('bounce-in');
+ }
+ });
+
+
+
+
+ });
+
+
+
+});
+
+$(".navbar-collapse").css({
+ maxHeight: $(window).height() - $(".navbar-header").height() + "px"
+});
+
+
+function animationPage() {
+
+ var scrollT = $(window).scrollTop();
+ var currentPosition = scrollT + 480;
+ var imageIcon = $('.usecaseimage').offset().top;
+ if (imageIcon < currentPosition) {
+ $(".usecaseimage").addClass('animated bounceIn');
+ } else {
+ $(".usecaseimage").removeClass('animated bounceIn');
+ }
+};
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