Design data pipeline to gather log events and transform it to queryable data with HIVE ddl.
This covers Java applications with log4j and non-java unix applications using rsyslog.
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An introduction to Apache Flume that comes from Hadoop Administrator Training delivered by GetInData.
Apache Flume is a distributed, reliable, and available service for collecting, aggregating, and moving large amounts of log data. By reading these slides, you will learn about Apache Flume, its motivation, the most important features, architecture of Flume, its reliability guarantees, Agent's configuration, integration with the Apache Hadoop Ecosystem and more.
Gemini Mobile Technologies ("Gemini") released a Real-Time Log Processing System based on Flume and Cassandra ("Flume-Cassandra Log Processor") as open source. The Flume-Cassandra Log Processor enables massive volumes of production system logs to be collected and processed into graphical reports, in real-time. In addition, logs from multiple data centers can be simultaneously aggregated and analyzed in a single database.
First slide
1) Apache Flume is a distributed and available service, in which it can collect and move large amount of streaming data from one location to another.
2) Most frequently it will deliver the log data into HDFS.
Second slide
1) Event and Client are the logical components of flume.
2) An Event is a Singular unit of data which can be transported by Flume NG from its Source to destination.
3) Typically an Event will be composed of Zero or more headers and a body. Here the headers will be used for contextual routing. This means by using the Header definition we can rout the data to the next eligible destination.
4) Client is an Event generator. It will generate the events and send it to one or more agents.
Eg: Apache webservers, which generates continuously a huge amount of log data.
Third slide
1) Flume agent is a JVM Daemon service, which holds all Flume-NG components like Sources, Channels, Sinks...etc.
2) Here the Source will send the events to channel and channel will stored it, later the channel will send the events to sink.
Fourth slide
1) Source is an active component, which receives data from different locations and places it on one or more Channels.
2) The declaration of source component in “.conf” file of agent “a1” is listed here. In this s1 means Source component, a1 means agent.
a1.sources=s1
a1.sources.s1.type=netcat (netcat is one of the Source type)
3) There are different Source types are available like Pollable (Means Auto generating like “tail –F” command and sequencing command), event driven and Netcat.
4) Even we can write our won Source type and specify that Custom class name to source type parameter.
Fifth slide
1) A channel is a bridge between Source and Sink.
2) Channel will store the Source events and send it to Sink.
3) There are three different types of Channels like memory channel which is very fast but no guarantee for data loss. And file channel which will store the events in a file system before sending it to sink. And the third one is database channel which will store the events in database.
4) Single Channel can be connected to any number of Sources and Sinks.
Sixth slide
1) A sink receives events from one channel only.
If you want to stay up to date, subscribe to our newsletter here: https://bit.ly/3tiw1I8
An introduction to Apache Flume that comes from Hadoop Administrator Training delivered by GetInData.
Apache Flume is a distributed, reliable, and available service for collecting, aggregating, and moving large amounts of log data. By reading these slides, you will learn about Apache Flume, its motivation, the most important features, architecture of Flume, its reliability guarantees, Agent's configuration, integration with the Apache Hadoop Ecosystem and more.
Gemini Mobile Technologies ("Gemini") released a Real-Time Log Processing System based on Flume and Cassandra ("Flume-Cassandra Log Processor") as open source. The Flume-Cassandra Log Processor enables massive volumes of production system logs to be collected and processed into graphical reports, in real-time. In addition, logs from multiple data centers can be simultaneously aggregated and analyzed in a single database.
First slide
1) Apache Flume is a distributed and available service, in which it can collect and move large amount of streaming data from one location to another.
2) Most frequently it will deliver the log data into HDFS.
Second slide
1) Event and Client are the logical components of flume.
2) An Event is a Singular unit of data which can be transported by Flume NG from its Source to destination.
3) Typically an Event will be composed of Zero or more headers and a body. Here the headers will be used for contextual routing. This means by using the Header definition we can rout the data to the next eligible destination.
4) Client is an Event generator. It will generate the events and send it to one or more agents.
Eg: Apache webservers, which generates continuously a huge amount of log data.
Third slide
1) Flume agent is a JVM Daemon service, which holds all Flume-NG components like Sources, Channels, Sinks...etc.
2) Here the Source will send the events to channel and channel will stored it, later the channel will send the events to sink.
Fourth slide
1) Source is an active component, which receives data from different locations and places it on one or more Channels.
2) The declaration of source component in “.conf” file of agent “a1” is listed here. In this s1 means Source component, a1 means agent.
a1.sources=s1
a1.sources.s1.type=netcat (netcat is one of the Source type)
3) There are different Source types are available like Pollable (Means Auto generating like “tail –F” command and sequencing command), event driven and Netcat.
4) Even we can write our won Source type and specify that Custom class name to source type parameter.
Fifth slide
1) A channel is a bridge between Source and Sink.
2) Channel will store the Source events and send it to Sink.
3) There are three different types of Channels like memory channel which is very fast but no guarantee for data loss. And file channel which will store the events in a file system before sending it to sink. And the third one is database channel which will store the events in database.
4) Single Channel can be connected to any number of Sources and Sinks.
Sixth slide
1) A sink receives events from one channel only.
Apache Flume is a simple yet robust data collection and aggregation framework which allows easy declarative configuration of components to pipeline data from upstream source to backend services such as Hadoop HDFS, HBase and others.
HBase Data Modeling and Access Patterns with Kite SDKHBaseCon
Speaker: Adam Warrington (Cloudera)
The Kite SDK is a set of libraries and tools focused on making it easier to build systems on top of the Hadoop ecosystem. HBase support has recently been added to the Kite SDK Data Module, which allows a developer to model and access data in HBase consistent with how they would model data in HDFS using Kite. This talk will focus on Kite's HBase support by covering Kite basics and moving through the specifics of working with HBase as a data source. This feature overview will be supplemented by specifics of how that feature is being used in production applications at Cloudera.
Deploying Apache Flume to enable low-latency analyticsDataWorks Summit
The driving question behind redesigns of countless data collection architectures has often been, ?how can we make the data available to our analytical systems faster?? Increasingly, the go-to solution for this data collection problem is Apache Flume. In this talk, architectures and techniques for designing a low-latency Flume-based data collection and delivery system to enable Hadoop-based analytics are explored. Techniques for getting the data into Flume, getting the data onto HDFS and HBase, and making the data available as quickly as possible are discussed. Best practices for scaling up collection, addressing de-duplication, and utilizing a combination streaming/batch model are described in the context of Flume and Hadoop ecosystem components.
HBaseCon 2015: Blackbird Collections - In-situ Stream Processing in HBaseHBaseCon
Blackbird is a large-scale object store built at Rocket Fuel, which stores 100+ TB of data and provides real time access to 10 billion+ objects in a 2-3 milliseconds at a rate of 1 million+ times per second. In this talk (an update from HBaseCon 2014), we will describe Blackbird's comprehensive collections API and various examples of how it can be used to model collections like sets, maps, and aggregates on these collections like counters, etc. We will also illustrate the flexibility and power of the API by modeling custom collection types that are unique to the Rocket Fuel context.
Scaling Cloud-Scale Translytics Workloads with Omid and PhoenixDataWorks Summit
Recently, Apache Phoenix has been integrated with Apache (incubator) Omid transaction processing service, to provide ultra-high system throughput with ultra-low latency overhead. Phoenix has been shown to scale beyond 0.5M transactions per second with sub-5ms latency for short transactions on industry-standard hardware. On the other hand, Omid has been extended to support secondary indexes, multi-snapshot SQL queries, and massive-write transactions.
These innovative features make Phoenix an excellent choice for translytics applications, which allow converged transaction processing and analytics. We share the story of building the next-gen data tier for advertising platforms at Verizon Media that exploits Phoenix and Omid to support multi-feed real-time ingestion and AI pipelines in one place, and discuss the lessons learned.
Apache Flume is a simple yet robust data collection and aggregation framework which allows easy declarative configuration of components to pipeline data from upstream source to backend services such as Hadoop HDFS, HBase and others.
HBase Data Modeling and Access Patterns with Kite SDKHBaseCon
Speaker: Adam Warrington (Cloudera)
The Kite SDK is a set of libraries and tools focused on making it easier to build systems on top of the Hadoop ecosystem. HBase support has recently been added to the Kite SDK Data Module, which allows a developer to model and access data in HBase consistent with how they would model data in HDFS using Kite. This talk will focus on Kite's HBase support by covering Kite basics and moving through the specifics of working with HBase as a data source. This feature overview will be supplemented by specifics of how that feature is being used in production applications at Cloudera.
Deploying Apache Flume to enable low-latency analyticsDataWorks Summit
The driving question behind redesigns of countless data collection architectures has often been, ?how can we make the data available to our analytical systems faster?? Increasingly, the go-to solution for this data collection problem is Apache Flume. In this talk, architectures and techniques for designing a low-latency Flume-based data collection and delivery system to enable Hadoop-based analytics are explored. Techniques for getting the data into Flume, getting the data onto HDFS and HBase, and making the data available as quickly as possible are discussed. Best practices for scaling up collection, addressing de-duplication, and utilizing a combination streaming/batch model are described in the context of Flume and Hadoop ecosystem components.
HBaseCon 2015: Blackbird Collections - In-situ Stream Processing in HBaseHBaseCon
Blackbird is a large-scale object store built at Rocket Fuel, which stores 100+ TB of data and provides real time access to 10 billion+ objects in a 2-3 milliseconds at a rate of 1 million+ times per second. In this talk (an update from HBaseCon 2014), we will describe Blackbird's comprehensive collections API and various examples of how it can be used to model collections like sets, maps, and aggregates on these collections like counters, etc. We will also illustrate the flexibility and power of the API by modeling custom collection types that are unique to the Rocket Fuel context.
Scaling Cloud-Scale Translytics Workloads with Omid and PhoenixDataWorks Summit
Recently, Apache Phoenix has been integrated with Apache (incubator) Omid transaction processing service, to provide ultra-high system throughput with ultra-low latency overhead. Phoenix has been shown to scale beyond 0.5M transactions per second with sub-5ms latency for short transactions on industry-standard hardware. On the other hand, Omid has been extended to support secondary indexes, multi-snapshot SQL queries, and massive-write transactions.
These innovative features make Phoenix an excellent choice for translytics applications, which allow converged transaction processing and analytics. We share the story of building the next-gen data tier for advertising platforms at Verizon Media that exploits Phoenix and Omid to support multi-feed real-time ingestion and AI pipelines in one place, and discuss the lessons learned.
WarsawITDays_ ApacheNiFi202
Apache NiFi 202: Integration and Best Practices
Timothy Spann, Principal Developer Advocate, Cloudera
APACHE NIFI, BEST PRACTICES, STREAMING, KAFKA, PULSAR, FLINK
MNIEJ
https://www.datainmotion.dev/2020/06/no-more-spaghetti-flows.html https://github.com/tspannhw/EverythingApacheNiFi https://www.datainmotion.dev/2020/12/basic-understanding-of-cloudera-flow.html https://www.datainmotion.dev/2020/10/top-25-use-cases-of-cloudera-flow.html In this talk, we will walk step by step through Apache NiFi from the first load to the first application. I will include slides, articles, and examples to take away as a Quick Start to utilizing Apache NiFi in your real-time dataflows. I will help you get up and running locally on your laptop or Docker. We will also show how to connect NiFi to Kafka, Pulsar, Flink, MQTT, and databases. I will cover: Advanced Record Processing Provenance Scheduling and Cron Relationships Routing Basic Cluster Architecture Listeners Controller Services Handling Errors Sources and Sinks
Timothy Spann,
Principal Developer Advocate, Cloudera
Introduction to the management of data persistency in FIWARE with the different approach adopted by the FIWARE Community. What is a Time Series Database. What are the different between the different solutions adopted.
In this session you will learn:
Flume Overview
Flume Agent
Sinks
Flume Installation
What is Netcat & Telnet?
For more information, visit: https://www.mindsmapped.com/courses/big-data-hadoop/hadoop-developer-training-a-step-by-step-tutorial/
This is the talk I gave at the Big Data Meetup in Seattle in March. In this talk, I discuss the fundamentals of Spark Streaming and Flume, and how they integrate with each other.
Open Source Big Data Ingestion - Without the Heartburn!Pat Patterson
Big Data tools such as Hadoop and Spark allow you to process data at unprecedented scale, but keeping your processing engine fed can be a challenge. Upstream data sources can 'drift' due to infrastructure, OS and application changes, causing ETL tools and hand-coded solutions to fail, inducing heartburn in even the most resilient data scientist. This session will survey the big data ingestion landscape, focusing on how open source tools such as Sqoop, Flume, Nifi and StreamSets can keep the data pipeline flowing.
Apache Big Data EU 2016: Building Streaming Applications with Apache ApexApache Apex
Stream processing applications built on Apache Apex run on Hadoop clusters and typically power analytics use cases where availability, flexible scaling, high throughput, low latency and correctness are essential. These applications consume data from a variety of sources, including streaming sources like Apache Kafka, Kinesis or JMS, file based sources or databases. Processing results often need to be stored in external systems (sinks) for downstream consumers (pub-sub messaging, real-time visualization, Hive and other SQL databases etc.). Apex has the Malhar library with a wide range of connectors and other operators that are readily available to build applications. We will cover key characteristics like partitioning and processing guarantees, generic building blocks for new operators (write-ahead-log, incremental state saving, windowing etc.) and APIs for application specification.
Apache Solr on Hadoop is enabling organizations to collect, process and search larger, more varied data. Apache Spark is is making a large impact across the industry, changing the way we think about batch processing and replacing MapReduce in many cases. But how can production users easily migrate ingestion of HDFS data into Solr from MapReduce to Spark? How can they update and delete existing documents in Solr at scale? And how can they easily build flexible data ingestion pipelines? Cloudera Search Software Engineer Wolfgang Hoschek will present an architecture and solution to this problem. How was Apache Solr, Spark, Crunch, and Morphlines integrated to allow for scalable and flexible ingestion of HDFS data into Solr? What are the solved problems and what's still to come? Join us for an exciting discussion on this new technology.
Presenter: Robert Metzger
Video Link: https://www.youtube.com/watch?v=GWxyiTY-1uQ
Flink.tw Meetup Event (2016/07/19):
"Stream Processing with Apache Flink w/ Flink PMC Robert Metzger"
Real time cloud native open source streaming of any data to apache solrTimothy Spann
Real time cloud native open source streaming of any data to apache solr
Utilizing Apache Pulsar and Apache NiFi we can parse any document in real-time at scale. We receive a lot of documents via cloud storage, email, social channels and internal document stores. We want to make all the content and metadata to Apache Solr for categorization, full text search, optimization and combination with other datastores. We will not only stream documents, but all REST feeds, logs and IoT data. Once data is produced to Pulsar topics it can instantly be ingested to Solr through Pulsar Solr Sink.
Utilizing a number of open source tools, we have created a real-time scalable any document parsing data flow. We use Apache Tika for Document Processing with real-time language detection, natural language processing with Apache OpenNLP, Sentiment Analysis with Stanford CoreNLP, Spacy and TextBlob. We will walk everyone through creating an open source flow of documents utilizing Apache NiFi as our integration engine. We can convert PDF, Excel and Word to HTML and/or text. We can also extract the text to apply sentiment analysis and NLP categorization to generate additional metadata about our documents. We also will extract and parse images that if they contain text we can extract with TensorFlow and Tesseract.
Get acquainted with a distributed, reliable tool/service for collecting a large amount of streaming data to centralized storage with their architecture.
Let me know if anything is required. Happy to help.
Ping me google #bobrupakroy.
take care!
Introduction to Apache Apex and writing a big data streaming application Apache Apex
Introduction to Apache Apex - The next generation native Hadoop platform, and writing a native Hadoop big data Apache Apex streaming application.
This talk will cover details about how Apex can be used as a powerful and versatile platform for big data. Apache apex is being used in production by customers for both streaming and batch use cases. Common usage of Apache Apex includes big data ingestion, streaming analytics, ETL, fast batch. alerts, real-time actions, threat detection, etc.
Presenter : <b>Pramod Immaneni</b> Apache Apex PPMC member and senior architect at DataTorrent Inc, where he works on Apex and specializes in big data applications. Prior to DataTorrent he was a co-founder and CTO of Leaf Networks LLC, eventually acquired by Netgear Inc, where he built products in core networking space and was granted patents in peer-to-peer VPNs. Before that he was a technical co-founder of a mobile startup where he was an architect of a dynamic content rendering engine for mobile devices.
This is a video of the webcast of an Apache Apex meetup event organized by Guru Virtues at 267 Boston Rd no. 9, North Billerica, MA, on <b>May 7th 2016</b> and broadcasted from San Jose, CA. If you are interested in helping organize i.e., hosting, presenting, community leadership Apache Apex community, please email apex-meetup@datatorrent.com
State of Artificial intelligence Report 2023kuntobimo2016
Artificial intelligence (AI) is a multidisciplinary field of science and engineering whose goal is to create intelligent machines.
We believe that AI will be a force multiplier on technological progress in our increasingly digital, data-driven world. This is because everything around us today, ranging from culture to consumer products, is a product of intelligence.
The State of AI Report is now in its sixth year. Consider this report as a compilation of the most interesting things we’ve seen with a goal of triggering an informed conversation about the state of AI and its implication for the future.
We consider the following key dimensions in our report:
Research: Technology breakthroughs and their capabilities.
Industry: Areas of commercial application for AI and its business impact.
Politics: Regulation of AI, its economic implications and the evolving geopolitics of AI.
Safety: Identifying and mitigating catastrophic risks that highly-capable future AI systems could pose to us.
Predictions: What we believe will happen in the next 12 months and a 2022 performance review to keep us honest.
ViewShift: Hassle-free Dynamic Policy Enforcement for Every Data LakeWalaa Eldin Moustafa
Dynamic policy enforcement is becoming an increasingly important topic in today’s world where data privacy and compliance is a top priority for companies, individuals, and regulators alike. In these slides, we discuss how LinkedIn implements a powerful dynamic policy enforcement engine, called ViewShift, and integrates it within its data lake. We show the query engine architecture and how catalog implementations can automatically route table resolutions to compliance-enforcing SQL views. Such views have a set of very interesting properties: (1) They are auto-generated from declarative data annotations. (2) They respect user-level consent and preferences (3) They are context-aware, encoding a different set of transformations for different use cases (4) They are portable; while the SQL logic is only implemented in one SQL dialect, it is accessible in all engines.
#SQL #Views #Privacy #Compliance #DataLake
Global Situational Awareness of A.I. and where its headedvikram sood
You can see the future first in San Francisco.
Over the past year, the talk of the town has shifted from $10 billion compute clusters to $100 billion clusters to trillion-dollar clusters. Every six months another zero is added to the boardroom plans. Behind the scenes, there’s a fierce scramble to secure every power contract still available for the rest of the decade, every voltage transformer that can possibly be procured. American big business is gearing up to pour trillions of dollars into a long-unseen mobilization of American industrial might. By the end of the decade, American electricity production will have grown tens of percent; from the shale fields of Pennsylvania to the solar farms of Nevada, hundreds of millions of GPUs will hum.
The AGI race has begun. We are building machines that can think and reason. By 2025/26, these machines will outpace college graduates. By the end of the decade, they will be smarter than you or I; we will have superintelligence, in the true sense of the word. Along the way, national security forces not seen in half a century will be un-leashed, and before long, The Project will be on. If we’re lucky, we’ll be in an all-out race with the CCP; if we’re unlucky, an all-out war.
Everyone is now talking about AI, but few have the faintest glimmer of what is about to hit them. Nvidia analysts still think 2024 might be close to the peak. Mainstream pundits are stuck on the wilful blindness of “it’s just predicting the next word”. They see only hype and business-as-usual; at most they entertain another internet-scale technological change.
Before long, the world will wake up. But right now, there are perhaps a few hundred people, most of them in San Francisco and the AI labs, that have situational awareness. Through whatever peculiar forces of fate, I have found myself amongst them. A few years ago, these people were derided as crazy—but they trusted the trendlines, which allowed them to correctly predict the AI advances of the past few years. Whether these people are also right about the next few years remains to be seen. But these are very smart people—the smartest people I have ever met—and they are the ones building this technology. Perhaps they will be an odd footnote in history, or perhaps they will go down in history like Szilard and Oppenheimer and Teller. If they are seeing the future even close to correctly, we are in for a wild ride.
Let me tell you what we see.
Analysis insight about a Flyball dog competition team's performanceroli9797
Insight of my analysis about a Flyball dog competition team's last year performance. Find more: https://github.com/rolandnagy-ds/flyball_race_analysis/tree/main
Learn SQL from basic queries to Advance queriesmanishkhaire30
Dive into the world of data analysis with our comprehensive guide on mastering SQL! This presentation offers a practical approach to learning SQL, focusing on real-world applications and hands-on practice. Whether you're a beginner or looking to sharpen your skills, this guide provides the tools you need to extract, analyze, and interpret data effectively.
Key Highlights:
Foundations of SQL: Understand the basics of SQL, including data retrieval, filtering, and aggregation.
Advanced Queries: Learn to craft complex queries to uncover deep insights from your data.
Data Trends and Patterns: Discover how to identify and interpret trends and patterns in your datasets.
Practical Examples: Follow step-by-step examples to apply SQL techniques in real-world scenarios.
Actionable Insights: Gain the skills to derive actionable insights that drive informed decision-making.
Join us on this journey to enhance your data analysis capabilities and unlock the full potential of SQL. Perfect for data enthusiasts, analysts, and anyone eager to harness the power of data!
#DataAnalysis #SQL #LearningSQL #DataInsights #DataScience #Analytics
STATATHON: Unleashing the Power of Statistics in a 48-Hour Knowledge Extravag...sameer shah
"Join us for STATATHON, a dynamic 2-day event dedicated to exploring statistical knowledge and its real-world applications. From theory to practice, participants engage in intensive learning sessions, workshops, and challenges, fostering a deeper understanding of statistical methodologies and their significance in various fields."
2. Agenda
•Why Centralized Logging on Hadoop
•Flume Introduction
•Simple Flume Logging
•Centralized and Scalable Flume Logging
•Leveraging log data
•Example
2
3. •There are tons of logs generated from Applications
•These logs are stored on local disks on individual nodes.
•Log files containing records are required to archive in near real time to
create some value.
•Enable analytics on logs for diagnosing issues on Hadoop platform.
3
Use Case: Centralized Logging Requirements
4. Centralized Log Management & Analytics : Goals
•Have a central repository to store large volume of machine generated data
from all sources and tiers of applications and infrastructures
•Feed log data from multiple sources to the common repository in a non-
intrusive way and in near real time
•Enable analytics on log data using standard analytical solutions
•Provide capability to search and correlate information across different sources
for quick problem isolation and resolution.
•Improve operational intelligence and
•Be centralized without redundancy of multiple agents on all hosts for log
collections
4
5. Solution Components for centralized logging
Flume
•Flume is a streaming service, distributed as part of Apache Hadoop ecosystem, and
primarily a reliable way of getting stream and log data into HDFS. Its pluggable
architecture supports any consumer. A correctly configured pipeline of Flume is
guaranteed to not lose data, provided durable channels are used.
•Each Flume agent consists of three major components: sources, channels, and sinks.
Sources
An active component that receives events from a specialized location or mechanism
and places it on one or Channels.
Different Source types:
Specialized sources for integrating with well-known systems. Example:
Syslog, Netcat
AvroSource NetcatSource SpoolDirectorySource
ExecSource JMSSource SyslogTcpSource SyslogUDPSource
5
6. Channels
A passive component that buffers the incoming events until they are drained by
Sinks.
Different Channels offer different levels of persistence:
Memory Channel: volatile
Data lost if JVM or machine restarts
File Channel: backed by WAL implementation Data not lost unless the disk dies.
Eventually, when the agent comes back data can be accessed.
Channels are fully transactional
Provide weak ordering guarantees (in case of failures / rollbacks )
Can work with any number of Sources and Sinks.
Handles upstream bursts
Upstream or downstream buffers
7. Sinks
An active component that removes events from a Channel and transmits them
to their next hop destination.
Different types of Sinks:
Terminal sinks that deposit events to their final destination. For example:
HDFS, HBase, Kite-Solr, Elastic Search
Sinks support serialization to user’s preferred formats.
HDFS sink supports time-based and arbitrary bucketing of data while writing to
HDFS.
IPC sink for Agent-to-Agent communication: Avro
Require exactly one channel to function
8. Flume Multi Tier Setup
[Client]+ Agent [ Agent]* Destination_______
9.
10. Interceptors
Interceptor
Flume has the capability to modify/drop events in-flight. This is done with the help of
interceptors. An interceptor can modify or even drop events based on any criteria
chosen by the developer of the interceptor.
Built-in Interceptors allow adding headers such as timestamps, hostname, static
markers etc.
Custom interceptors can introspect event payload to create specific headers where
necessary
12. Contextual Routing with Interceptors
Achieved using Interceptors and Channel Selectors
Terminal Sinks can directly use Headers to make destination selections
HDFS Sink can use headers values to create dynamic path for files that the event
will be added to.
# channel selector configuration
agent_foo.sources.avro-AppSrv-source1.selector.type = multiplexing
agent_foo.sources.avro-AppSrv-source1.selector.header = State
agent_foo.sources.avro-AppSrv-source1.selector.mapping.CA = mem-channel-1
agent_foo.sources.avro-AppSrv-source1.selector.mapping.AZ = file-channel-2
agent_foo.sources.avro-AppSrv-source1.selector.mapping.NY = mem-channel-1 file-channel-2
agent_foo.sources.avro-AppSrv-source1.selector.optional.CA = mem-channel-1 file-channel-2
agent_foo.sources.avro-AppSrv-source1.selector.mapping.AZ = file-channel-2
agent_foo.sources.avro-AppSrv-source1.selector.default = mem-channel-1
13. Flume Client
An entity that generates events and sends them to one or more Agents.
• Example
• Flume/Syslog log4j Appender
• Custom Client using Client SDK (org.apache.flume.api)
• Embedded Agent – An agent embedded within your application
• Decouples Flume from the system where event data is consumed from
• Not needed in all cases
15. For Non log4j Applications
Rsyslog
•Rsyslog is an open-source software utility used on UNIX and Unix-like computer systems for
forwarding log messages in an IP network. It implements the basic syslog protocol, extends it
with content-based filtering, rich filtering capabilities, flexible configuration options and adds
features such as using TCP for transport.
● Used in most of the Linux distros as standard logger
● Has multiple facilities for application use local0-local7 (avoid local7)
● Can poll any file on system and send new events over the network to syslog destinations
● service rsyslog restart
$ModLoad imfile
$InputFileName /var/log/NEWAPP/NEWAPP.log
$InputFileTag TYPE:_NEWAPP
$WorkDirectory /var/spool/rsyslog/NEWAPP
$InputFileStateFile NEWAPP-log
$InputFileFacility local7
$InputFilePersistStateInterval 10
$InputFileSeverity info
$RepeatedMsgReduction off
$InputRunFileMonitor
local7.* @@flumehost:4444
16. Solution: Near Real Time Log Archive to Hadoop Platform
16
Event Flow :: Simple Flume Logging
17. Solution: Near Real Time Log Archive to Hadoop Platform
17
•Less centralized , avoiding single point of failure.
•In case collector fails , events are still not lost.
•Scope for further scalability , with minimum configuration.
18. Configuration Example: Flume Multi tier Config
●Flume Listener Agents
■ This agent gathers events from multiple applications.
■ can also perform event inspections using interceptors in this tier.
■ Each event is analyzed and sent forward with appropriate header(only) updates so next agent
can make sense of it.
■ We can use filechannel or any other durable channel here.
■ Events aggregated for next tier
●Flume Writer Tier
■ Minimum connections to HDFS
■ This agent gets events from aggregator and reads headers.
■ According to header events are sent to relevant location on HDFS.
18
19. DDL for creating a Hive table with log data,
CREATE TABLE logData_H2 (
Ltype STRING,
event_time STRING,
porder STRING,
SEVERITY STRING,
SCLASS STRING,
PHO STRING ,
MESG STRING
) ROW FORMAT
DELIMITED FIELDS TERMINATED BY ','
STORED AS TEXTFILE
LOCATION '/data/logmgmt/_DUMMY/raz-XPS14/150703/';