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My keynote at GOTO Berlin 2013
The Epistemology of Software Engineering
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Presented at Data Day Texas on January 10th, 2015
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Talk given to Storm NYC meetup group on 3/18/2015
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My presentation of Storm at the Bay Area Hadoop User Group on January 18th, 2012.
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Storm: Distributed and fault tolerant realtime computation
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Introduction to Storm
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Over the last couple years, Apache Storm became a de-facto standard for developing real-time analytics and complex event processing applications. Storm enables to tackle real-time data processing challenges the same way Hadoop enables batch processing of Big Data. Storm enables companies to have "Fast Data" alongside with "Big Data". Some use cases where Storm can be used are Fraud Detection, Operation Intelligence, Machine Learning, ETL, Analytics, etc. In this meetup, Eugene Dvorkin, Architect @WebMD and NYC Storm User Group organizer will teach Apache Storm and Stream Processing fundamentals. While this meeting is geared toward new Storm users, experienced users may find something interesting as well. Following topics will be covered: • Why use Apache Storm? • Common use cases • Storm Architecture - components, concepts, topology • Building simple Storm topology with Java and Groovy • Trident and micro-batch processing • Fault tolerance and guaranteed message delivery • Running and monitoring Storm in production • Kafka • Storm at WebMD • Resources
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Empfohlen
My keynote at GOTO Berlin 2013
The Epistemology of Software Engineering
The Epistemology of Software Engineering
nathanmarz
Presented at Data Day Texas on January 10th, 2015
Using Simplicity to Make Hard Big Data Problems Easy
Using Simplicity to Make Hard Big Data Problems Easy
nathanmarz
Talk given to Storm NYC meetup group on 3/18/2015
The inherent complexity of stream processing
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nathanmarz
My presentation of Storm at the Bay Area Hadoop User Group on January 18th, 2012.
Storm
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nathanmarz
Storm: Distributed and fault tolerant realtime computation
Storm: Distributed and fault tolerant realtime computation
Ferran Galí Reniu
Introduction to Storm
Introduction to Storm
Eugene Dvorkin
Over the last couple years, Apache Storm became a de-facto standard for developing real-time analytics and complex event processing applications. Storm enables to tackle real-time data processing challenges the same way Hadoop enables batch processing of Big Data. Storm enables companies to have "Fast Data" alongside with "Big Data". Some use cases where Storm can be used are Fraud Detection, Operation Intelligence, Machine Learning, ETL, Analytics, etc. In this meetup, Eugene Dvorkin, Architect @WebMD and NYC Storm User Group organizer will teach Apache Storm and Stream Processing fundamentals. While this meeting is geared toward new Storm users, experienced users may find something interesting as well. Following topics will be covered: • Why use Apache Storm? • Common use cases • Storm Architecture - components, concepts, topology • Building simple Storm topology with Java and Groovy • Trident and micro-batch processing • Fault tolerance and guaranteed message delivery • Running and monitoring Storm in production • Kafka • Storm at WebMD • Resources
Learning Stream Processing with Apache Storm
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The talk I gave a while back on the work we did at Yahoo to make Apache Storm a secure multi-tenant hosted service.
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Spark vs storm
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Resource Aware Scheduling in Apache Storm
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Analysis big data by use php with storm
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Counters are one of the two core metric types in Prometheus, allowing for tracking of request rates, error ratios and other key measurements. Learn why are they designed the way they are, how client libraries implement them and how rate() works. If you'd like more information about Prometheus, contact us at prometheus@robustperception.io
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This slides are for a brief seminar that I give in a Ph.D. exam "Perspective in Parallel Computing" (held by prof. Marco Danelutto) at University of Pisa (Italy). They are a rapid introduction to Apache Storm and how it relates to classical algorithmic skeleton parallel frameworks
Introduction to Apache Storm
Introduction to Apache Storm
Tiziano De Matteis
Introduction to Apache Storm: - Storm Concept: topology, tuple, stream, spout, bolt, stream grouping - Storm Component: Master and Worker - Example: GitHub Commit Feed
Introduction to Apache Storm - Concept & Example
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1) Storm is a distributed, real-time computation system. 2) The input stream of a Storm cluster is handled by a component called a spout. The spout passes the data to a bolt, a bolt either persists the data in some sort of storage, or passes it to some other bolt. You can imagine a Storm cluster as a chain of bolt components that each make some kind of transformation on the data exposed by the spout. 1) Real-time systems must guarantee the data processing. 2) And also it should be horizontally scalable, means, just adding few nodes to improve the scalability of a cluster. 3) It should be fault-tolerance, means, if any error occurs or any node goes down, our system should work without any hesitation. 4) We need to get rid of all the intermediate message brokers, because they are complex, and slow, because, instead of sending messages directly from producer to consumers, it has to go through third party message brokers, moreover, those third party message brokers are persist the input data into the disk. This whole process will consume extra time to process the data. 5) In comparison with Storm, Hadoop is ok, because Hadoop also provides a high latency system, so if you take a few hours of down time, you still have high latency, but in real time systems, if you take few hours of down time. Then you no longer in real time, which means robustness requirements, are much harder. Storm satisfies all those properties without any hesitation. 1) Both Hadoop and Storm are distributed and fault-Tolerance systems, but, Hadoop mainly used for batch processing systems, whereas Storm used for Real-time computation systems. 2) Storm doesn’t have inbuilt Storage system, it mainly builds on “come and get some” strategy. In other side, Hadoop have HDFS as storage file system. 1) Both Storm and Flume used for real-time data processing, but Flume will not give you real-time computation systems. moreover flume depends on channel Message broker component, for, guaranteed data processing, here, channel always persist the data before sending it to Consumer, but for Storm, there is no intermediate message brokers concept, it Just Works like as lite as possible. Whatever business logic that you want to write, will goes under Bolt component of Storm.
Apache Storm and twitter Streaming API integration
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Slides from talk given at the NYC Cassandra Meetup. Discussing how Storm works and how it integrates well with Apache Cassandra. There is also a segway into a example project that uses Storm and Cassandra to implement a scalable reactive web crawler. http://github.com/tjake/stormscraper
Storm and Cassandra
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Created by Nathan Marz at Twitter, Storm promises to help companies augment their batch-based big data processing systems with real-time computation.
Storm: The Real-Time Layer - GlueCon 2012
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Storm makes it easy to write and scale complex realtime computations on a cluster of computers, doing for realtime processing what Hadoop did for batch processing. Storm guarantees that every message will be processed. And it’s fast — you can process millions of messages per second with a small cluster. Best of all, you can write Storm topologies using any programming language. Storm was open-sourced by Twitter in September of 2011 and has since been adopted by many companies around the world. Storm has a wide range of use cases, from stream processing to continuous computation to distributed RPC. In this talk I'll introduce Storm and show how easy it is to use for realtime computation.
Jan 2012 HUG: Storm
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Yahoo Developer Network
Presented by Matt Jacobs, Edge Platform engineer at Netflix, during DevNexus 2016 conference in Atlanta
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adoop plays a central role for Yahoo! to provide personalized experiences for our users and create value for our advertisers. In this talk, we will discuss the convergence of low-latency processing and Hadoop platform. To enable the convergence, we have developed Storm-on-YARN to enable Storm streaming/microbatch applications and Hadoop batch applications hosted in a single cluster. Storm applications could leverage YARN for resource management, and apply Hadoop style security to Hadoop datasets on HDFS and HBase. In Storm-on-YARN, YARN is used to launch Storm application master (Nimbus), and enable Nimbus to request resources for Storm workers (Supervisors). YARN resource manager and Storm scheduler work together to support multi-tenancy and high availability. HDFS enables Storm to achieve higher availability of Nimbus itself. We are introducing Hadoop style security into Storm through JAAS authentication (Kerberos and Digest). Storm servers (Nimbus and DRPC) will be configured with authorization plugins for access control and audit. The security context enables Storm applications to access authorized datasets only (including those created by Hadoop applications). Yahoo! is making our contribution on Storm and YARN available as open source. We will work with industry partners to foster the convergence of low-latency processing and big-data.
Storm-on-YARN: Convergence of Low-Latency and Big-Data
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DataWorks Summit
Real-Time Analytics with Apache Storm
Real-Time Analytics with Apache Storm
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Clojure at BackType
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A commissioned study conducted by Forrester Consulting on behalf of EnterpriseDB, published in January 2015, presents a case study for the evolution of relational database management systems. The study, Relational Databases are Evolving to Support New Data Capabilities, found that the majority—78%—of database decisions makers wanted one solution that could handle relational and NoSQL data types. The study finds that relational databases are evolving to address the needs of end users seeking to link unstructured and structured data types and that decision makers should look to invest in these solutions. EDB’s Postgres Plus Advanced Server, for example, addresses these needs with such capabilities as support for unstructured data types, non-durable tables, tools for large-scale data loads, and integration technologies that connect standalone NoSQL solutions with Postgres.
Relational Databases are Evolving To Support New Data Capabilities
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Introduction to Apache Storm
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Introduction to Apache Storm: - Storm Concept: topology, tuple, stream, spout, bolt, stream grouping - Storm Component: Master and Worker - Example: GitHub Commit Feed
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1) Storm is a distributed, real-time computation system. 2) The input stream of a Storm cluster is handled by a component called a spout. The spout passes the data to a bolt, a bolt either persists the data in some sort of storage, or passes it to some other bolt. You can imagine a Storm cluster as a chain of bolt components that each make some kind of transformation on the data exposed by the spout. 1) Real-time systems must guarantee the data processing. 2) And also it should be horizontally scalable, means, just adding few nodes to improve the scalability of a cluster. 3) It should be fault-tolerance, means, if any error occurs or any node goes down, our system should work without any hesitation. 4) We need to get rid of all the intermediate message brokers, because they are complex, and slow, because, instead of sending messages directly from producer to consumers, it has to go through third party message brokers, moreover, those third party message brokers are persist the input data into the disk. This whole process will consume extra time to process the data. 5) In comparison with Storm, Hadoop is ok, because Hadoop also provides a high latency system, so if you take a few hours of down time, you still have high latency, but in real time systems, if you take few hours of down time. Then you no longer in real time, which means robustness requirements, are much harder. Storm satisfies all those properties without any hesitation. 1) Both Hadoop and Storm are distributed and fault-Tolerance systems, but, Hadoop mainly used for batch processing systems, whereas Storm used for Real-time computation systems. 2) Storm doesn’t have inbuilt Storage system, it mainly builds on “come and get some” strategy. In other side, Hadoop have HDFS as storage file system. 1) Both Storm and Flume used for real-time data processing, but Flume will not give you real-time computation systems. moreover flume depends on channel Message broker component, for, guaranteed data processing, here, channel always persist the data before sending it to Consumer, but for Storm, there is no intermediate message brokers concept, it Just Works like as lite as possible. Whatever business logic that you want to write, will goes under Bolt component of Storm.
Apache Storm and twitter Streaming API integration
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Slides from talk given at the NYC Cassandra Meetup. Discussing how Storm works and how it integrates well with Apache Cassandra. There is also a segway into a example project that uses Storm and Cassandra to implement a scalable reactive web crawler. http://github.com/tjake/stormscraper
Storm and Cassandra
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T Jake Luciani
Created by Nathan Marz at Twitter, Storm promises to help companies augment their batch-based big data processing systems with real-time computation.
Storm: The Real-Time Layer - GlueCon 2012
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Storm makes it easy to write and scale complex realtime computations on a cluster of computers, doing for realtime processing what Hadoop did for batch processing. Storm guarantees that every message will be processed. And it’s fast — you can process millions of messages per second with a small cluster. Best of all, you can write Storm topologies using any programming language. Storm was open-sourced by Twitter in September of 2011 and has since been adopted by many companies around the world. Storm has a wide range of use cases, from stream processing to continuous computation to distributed RPC. In this talk I'll introduce Storm and show how easy it is to use for realtime computation.
Jan 2012 HUG: Storm
Jan 2012 HUG: Storm
Yahoo Developer Network
Presented by Matt Jacobs, Edge Platform engineer at Netflix, during DevNexus 2016 conference in Atlanta
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Using Hystrix to Build Resilient Distributed Systems
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There is often a considerable delay between the discovery of a vulnerability and the issue of a patch. One mitigation strategy for this window of vulnerability is to use a configuration workaround, which prevents the vulnerable code from being executed at the cost of some lost functionality -- but if one is available. Since application configurations are not specifically designed to mitigate software vulnerabilities, we find that they only cover 25.2% of vulnerabilities. To minimize patch delay vulnerabilities and address the limitations of configuration workarounds, we propose Security Workarounds for Rapid Response (SWRRs), which are designed to neutralize security vulnerabilities in a timely, secure, and unobtrusive manner. Similar to configuration workarounds, SWRRs neutralize vulnerabilities by preventing vulnerable code from being executed at the cost of some lost functionality. However, the key difference is that SWRRs use existing error-handling code within applications, which enables them to be mechanically inserted with minimal knowledge of the application and minimal developer effort. This allows SWRRs to achieve high coverage while still being fast and easy to deploy. We designed and implemented Talos, a system that mechanically instrument SWRRs into a given application, and evaluate it on five popular Linux server applications. We run exploits against 11 real-world software vulnerabilities and show that SWRRs neutralize the vulnerabilities in all cases. Quantitative measurements on 320 SWRRs indicate that SWRRs instrumented by Talos can neutralize 75.1% of all potential vulnerabilities and incur a loss of functionality similar to configuration workarounds in 71.3% of those cases. Our overall conclusion is that automatically generated SWRRs can safely mitigate 2.1x times more vulnerabilities, while only incurring a loss of functionality comparable to that of traditional configuration workarounds.
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https://irjet.net/archives/V4/i2/IRJET-V4I2261.pdf
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The Hacking Games - Operation System Vulnerabilities Meetup 29112022
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This is an interview with Dmitriy Vyukov - the author of Relacy Race Detector (RRD) tool intended for verifying parallel applications. In this article you will learn about the history of creating RRD, its basic abilities and also about some other similar tools and the way they differ from RRD.
Interview with Dmitriy Vyukov - the author of Relacy Race Detector (RRD)
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Secure programming with php
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Hacking android apps by srini0x00
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Your Code is Wrong
1.
Your Code is
Wrong Nathan Marz @nathanmarz 1
2.
Let’s start with
an example
3.
Storm’s “reportError” method
4.
(Storm is a
realtime computation system, like Hadoop but for realtime)
5.
Storm architecture
6.
Storm architecture Master node
(similar to Hadoop JobTracker)
7.
Storm architecture Used for
cluster coordination
8.
Storm architecture Run worker
processes
9.
Storm’s “reportError” method
10.
Used to show
errors in the Storm UI
11.
Error info is
stored in Zookeeper
12.
What happens when
a user deploys code like this?
13.
Denial-of-service on Zookeeper and
cluster goes down
14.
Robust! Designed input space
Actual input space
15.
Your code is
wrong
16.
Your code is
literally wrong
17.
Your code is
wrong
18.
19.
Why do you
believe your code is correct?
20.
Your code Dependency 1 Dependency
2 Dependency 3
21.
Dependency 1 Dependency 4 Dependency
5
22.
Dependency 4 Dependency 6 Dependency
9 Dependency 7 Dependency 8
23.
Dependency 3,000,000 Hardware
24.
Electronics
25.
Chemistry
26.
Atomic physics
27.
Quantum mechanics
28.
I think I
can safely say that nobody understands quantum mechanics. Richard Feynman
29.
Your code is
wrong
30.
Your code ...
31.
All the software
you’ve used has had bugs in it
32.
Including the software you’ve
written
33.
Your code is sometimes
correct
34.
That’s good enough!
35.
36.
Treat code as
nondeterministic
37.
Embrace “your code
is wrong” to design better software
38.
Robust! Designed input space
Actual input space
39.
Robust! Designed input space
Actual input space
40.
An example
41.
Learning from Hadoop Jobtracker Job Job Job
42.
Learning from Hadoop Jobtracker Job Job Job
43.
Learning from Hadoop Jobtracker Job Job Job
44.
Your code is
wrong
45.
So your processes
will crash
46.
Storm’s daemons are process
fault-tolerant
47.
Storm Nimbus Topology Topology Topology
48.
Storm Nimbus Topology Topology Topology
49.
Storm Nimbus Topology Topology Topology
50.
Storm Nimbus Topology Topology Topology
51.
Storm Nimbus Topology Topology Topology
52.
Robust! Designed input space
Actual input space
53.
Robust! Designed input space
Actual input space
54.
The impact of
code being wrong
55.
Robust! Designed input space
Actual input space Failures! Bad performance! Security holes! Irrelevant!
56.
Design principle #1 Measuring
and monitoring are the foundation of solid engineering
57.
Measuring: Under what range
of inputs does my software function well?
58.
Monitoring: What’s the actual
input space of my software?
59.
Measure & Monitor Latency Throughput Stack
traces Buffer sizes Memory usage CPU usage #threads spawned ...
60.
How you monitor
your software is as important as its functionality
61.
Design principle #2 Embrace
immutability
62.
Read/write database Application
63.
MySQLApplication
64.
MongoDBApplication
65.
RiakApplication
66.
CassandraApplication
67.
HBaseApplication
68.
Your code is
wrong
69.
So data will
be corrupted
70.
And you may
not know why
71.
Views Immutable, ever-growing data Application Architecture based on
immutability
72.
Views Immutable, ever-growing data Application Lambda architecture
73.
Design principle #3 Minimize
dependencies
74.
The less that
can go wrong, the less that will go wrong
75.
Example: Storm’s usage of
Zookeeper
76.
Worker locations stored
in Zookeeper
77.
All workers must
know locations of other workers to send messages
78.
Two ways to
get location updates
79.
1. Poll Zookeeper Worker
Zookeeper
80.
2. Use Zookeeper
“watch” feature to get push notifications Worker Zookeeper
81.
Method 2 is
faster but relies on another feature
82.
Storm uses both
methods Worker Zookeeper
83.
If watch feature
fails, locations still propagate via polling
84.
Eliminating dependence justified by
small amount of code required
85.
Design principle #4 Explicitly
respect functional input ranges
86.
Storm’s “reportError” method
87.
Implement self-throttling to avoid
overloading other systems
88.
Design principle #5 Embrace
recomputation
89.
“Your code is
wrong” meanings 1. Design input space differs from actual input space 2. The logic of your code is wrong 3. Requirements are constantly changing
90.
You must be
able to change your code to match shifting requirements
91.
Example: blogging software
92.
New requirement: search
93.
Have to build
a search index
94.
95.
Recomputation gives you so
much more
96.
Views Immutable, ever-growing data Application
97.
Building software no
different than any other engineering
98.
The underlying challenges are
the same
99.
100.
101.
What will break
it?
102.
What are limits
of my dependencies?
103.
How can I
add redundancy to increase robustness?
104.
Can I isolate
failures?
105.
Our raw materials
are ideas instead of matter
106.
Thank you
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