Reactive app using actor model & apache sparkRahul Kumar
Developing Application with Big Data is really challenging work, scaling, fault tolerance and responsiveness some are the biggest challenge. Realtime bigdata application that have self healing feature is a dream these days. Apache Spark is a fast in-memory data processing system that gives a good backend for realtime application.In this talk I will show how to use reactive platform, Actor model and Apache Spark stack to develop a system that have responsiveness, resiliency, fault tolerance and message driven feature.
Reactive dashboard’s using apache sparkRahul Kumar
Apache Spark's Tutorial talk, In this talk i explained how to start working with Apache spark, feature of apache spark and how to compose data platform with spark. This talk also explains about reactive platform, tools and framework like Play, akka.
Apache Mesos, Apache Hadoop, Apache Spark + Custom Enterprise Applications: This stack combined is greater than the sum of each of the pieces of this stack. Mesos can manage resources across an entire data center, Hadoop provides a distributed data store and scalable data processing, and Spark delivers great in-memory and disk-based performance of data processing as well as streaming capabilities. Couple all of that with custom enterprise applications, and the data center turns into a well-oiled machine. When combined, this software stack delivers unlimited flexibility for the entire data center.
Jim Scott, Director of Architecture and Enterprise Strategy | Strata + Hadoop World | Barcelona, Spain, November 2014
Reactive app using actor model & apache sparkRahul Kumar
Developing Application with Big Data is really challenging work, scaling, fault tolerance and responsiveness some are the biggest challenge. Realtime bigdata application that have self healing feature is a dream these days. Apache Spark is a fast in-memory data processing system that gives a good backend for realtime application.In this talk I will show how to use reactive platform, Actor model and Apache Spark stack to develop a system that have responsiveness, resiliency, fault tolerance and message driven feature.
Reactive dashboard’s using apache sparkRahul Kumar
Apache Spark's Tutorial talk, In this talk i explained how to start working with Apache spark, feature of apache spark and how to compose data platform with spark. This talk also explains about reactive platform, tools and framework like Play, akka.
Apache Mesos, Apache Hadoop, Apache Spark + Custom Enterprise Applications: This stack combined is greater than the sum of each of the pieces of this stack. Mesos can manage resources across an entire data center, Hadoop provides a distributed data store and scalable data processing, and Spark delivers great in-memory and disk-based performance of data processing as well as streaming capabilities. Couple all of that with custom enterprise applications, and the data center turns into a well-oiled machine. When combined, this software stack delivers unlimited flexibility for the entire data center.
Jim Scott, Director of Architecture and Enterprise Strategy | Strata + Hadoop World | Barcelona, Spain, November 2014
Business intelligence requirements are changing and business users are moving more and more from historical reporting into predictive analytics in an attempt to get both a better and deeper understanding of their data. Traditionally, building an analytical platform has required an expensive infrastructure and a considerable amount of time for setup and deployment. Here we look at a quick and simple alternative.
The Server Side of Responsive Web DesignDave Olsen
Responsive web design has become an important tool for front-end developers as they develop mobile-optimized solutions for clients. Browser-detection has been an important tool for server-side developers for the same task for much longer. Unfortunately, both techniques have certain limitations. Depending on project requirements, team make-up and deployment environment combining these two techniques might lead to intriguing solutions for your organization. We'll discuss when it makes sense to take this extra step and we'll explore techniques for combining server-side technology, like server-side feature-detection, with your responsive web designs to deliver the most flexible solutions possible.
Database Configuration for Maximum SharePoint 2010 PerformanceEdwin M Sarmiento
Database configuration has a direct impact on how SharePoint 2010 performs. This presentation looks at the SQL Server database and what configuration changes can be made to maximize performance for your SharePoint 2010 farms
All new computers have multicore processors. To exploit this hardware parallelism for improved
performance, the predominant approach today is multithreading using shared variables and locks. This
approach has potential data races that can create a nondeterministic program. This paper presents a
promising new approach to parallel programming that is both lock-free and deterministic. The standard
forall primitive for parallel execution of for-loop iterations is extended into a more highly structured
primitive called a Parallel Operation (POP). Each parallel process created by a POP may read shared
variables (or shared collections) freely. Shared collections modified by a POP must be selected from a
special set of predefined Parallel Access Collections (PAC). Each PAC has several Write Modes that
govern parallel updates in a deterministic way. This paper presents an overview of a Prototype Library
that implements this POP-PAC approach for the C++ language, including performance results for two
benchmark parallel programs.
All new computers have multicore processors. To exploit this hardware parallelism for improved
perf
ormance, the predominant approach today is multithreading using shared variables and locks. This
approach has potential data races that can create a nondeterministic program. This paper presents a
promising new approach to parallel programming that is both
lock
-
free and deterministic. The standard
forall primitive for parallel execution of for
-
loop iterations is extended into a more highly structured
primitive called a Parallel Operation (POP). Each parallel process created by a POP may read shared
variable
s (or shared collections) freely. Shared collections modified by a POP must be selected from a
special set of predefined Parallel Access Collections (PAC). Each PAC has several Write Modes that
govern parallel updates in a deterministic way. This paper pre
sents an overview of a Prototype Library
that implements this POP
-
PAC approach for the C++ language, including performance results for two
benchmark parallel programs.
Business intelligence requirements are changing and business users are moving more and more from historical reporting into predictive analytics in an attempt to get both a better and deeper understanding of their data. Traditionally, building an analytical platform has required an expensive infrastructure and a considerable amount of time for setup and deployment. Here we look at a quick and simple alternative.
The Server Side of Responsive Web DesignDave Olsen
Responsive web design has become an important tool for front-end developers as they develop mobile-optimized solutions for clients. Browser-detection has been an important tool for server-side developers for the same task for much longer. Unfortunately, both techniques have certain limitations. Depending on project requirements, team make-up and deployment environment combining these two techniques might lead to intriguing solutions for your organization. We'll discuss when it makes sense to take this extra step and we'll explore techniques for combining server-side technology, like server-side feature-detection, with your responsive web designs to deliver the most flexible solutions possible.
Database Configuration for Maximum SharePoint 2010 PerformanceEdwin M Sarmiento
Database configuration has a direct impact on how SharePoint 2010 performs. This presentation looks at the SQL Server database and what configuration changes can be made to maximize performance for your SharePoint 2010 farms
All new computers have multicore processors. To exploit this hardware parallelism for improved
performance, the predominant approach today is multithreading using shared variables and locks. This
approach has potential data races that can create a nondeterministic program. This paper presents a
promising new approach to parallel programming that is both lock-free and deterministic. The standard
forall primitive for parallel execution of for-loop iterations is extended into a more highly structured
primitive called a Parallel Operation (POP). Each parallel process created by a POP may read shared
variables (or shared collections) freely. Shared collections modified by a POP must be selected from a
special set of predefined Parallel Access Collections (PAC). Each PAC has several Write Modes that
govern parallel updates in a deterministic way. This paper presents an overview of a Prototype Library
that implements this POP-PAC approach for the C++ language, including performance results for two
benchmark parallel programs.
All new computers have multicore processors. To exploit this hardware parallelism for improved
perf
ormance, the predominant approach today is multithreading using shared variables and locks. This
approach has potential data races that can create a nondeterministic program. This paper presents a
promising new approach to parallel programming that is both
lock
-
free and deterministic. The standard
forall primitive for parallel execution of for
-
loop iterations is extended into a more highly structured
primitive called a Parallel Operation (POP). Each parallel process created by a POP may read shared
variable
s (or shared collections) freely. Shared collections modified by a POP must be selected from a
special set of predefined Parallel Access Collections (PAC). Each PAC has several Write Modes that
govern parallel updates in a deterministic way. This paper pre
sents an overview of a Prototype Library
that implements this POP
-
PAC approach for the C++ language, including performance results for two
benchmark parallel programs.
3. As
software
engineer
we
are
inevitably
affected
by
the
tools
we
surrounded
ourself
with
Process
all
act
to
shape
the
software
we
build.
Language
Frameworks
4. Likewise
database,
which
have
trodden
a
very
specific
path,
inevitably
affect
the
way
we
treat
mutability
and
share
state
in
our
application.
5. 5
Today’s data platforms range greatly in complexity.
From simple caching layers or Polyglot Persistence right through to
wholly
integrated data pipelines.
There are many paths.
They go to many different places.
So the aim for this talk is to explain how and why some of these popular approaches work.
http://www.benstopford.com/2015/04/28/elements-‐of-‐scale-‐composing-‐and-‐scaling-‐data-‐platforms/
This
talk
is
based
on
Ben
Stopford’s
actual
presentation.
6. 6
Computer
work
best
with
sequential
workload
When we’re dealing with data, we’re really just arranging locality.
Locality to the CPU.
Locality to the other data we need.
7. 7
Accessing
data
sequentially
is
an
important
component
of
this.
Computers
are
just
good
at
sequential
operations.
Sequential
operations
can
be
predicted.
8. 8
Random
vs
Sequential
Addressing
If
you’r
taking
data
from
disk
sequentially
it
will
be
pre-‐fetched
in
to
the
disk
buffer,
the
page
cache
and
the
different
levels
of
CPU
caching.
But it does little to help the addressing of data at random, be it in main memory,
on disk or over the network.
In fact pre-fetching actually hinders random workloads as the various
caches and frontside bus fill with data which is unlikely to be used.
9. 9
Streaming
data
sequentially
from
disk
can
actually
outperform
randomly
addressed
main
memory.
So
disk
may
not
always
be
quite
the
tortoise
we
think
it
is,
at
least
not
if
we
can
arrange
sequential
access.
10. 10
We
want
to
keep
writes
and
reads
sequential,
as
it
works
well
with
the
hardware.
We
can
append
writes
to
the
end
of
the
file
efficiently.
We
can
read
by
scanning
the
the
file
in
its
entirety.
Any
processing
we
wish
to
do
can
happen
as
the
data
streams
through
the
CPU.
We
might
filter,
aggregate
or
even
do
something
more
complex.
