Dmitriy Setrakyan

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Position:

Founder & CPO, GridGain Systems

Bio:

As a founder and Chief Product Officer at GridGain, Dmitriy Setrakyan is responsible for leading product development, professional services, and customer support operations. Dmitriy has been designing, architecting and developing software and applications for over 15 years and has expertise in the development of distributed computing systems, middleware platforms, financial trading systems, CRM applications and similar systems.

Prior to GridGain, Dmitriy worked at eBay where he was responsible for the architecture of performance sensitive high-traffic components of an add-serving system processing several billion hits a day. Before that Dmitriy served as a Lead Architect at Fitech Labs, focusing on high-performance software for trading systems, where he jump-started a new distributed caching and grid computing product line scaling out to 100s computers.

Dmitriy holds a Bachelor of Science in Computer Science from University of California at Davis specializing in Networking and Algorithms.

Read Dmitriy’s personal blog on Blogspot.com.

Bio:

As a founder and Chief Product Officer at GridGain, Dmitriy Setrakyan is responsible for leading product development, professional services, and customer support operations. Dmitriy has been designing, architecting and developing software and applications for over 15 years and has expertise in the development of distributed computing systems, middleware platforms, financial trading systems, CRM applications and similar systems.

Prior to GridGain, Dmitriy worked at eBay where he was responsible for the architecture of performance sensitive high-traffic components of an add-serving system processing several billion hits a day. Before that Dmitriy served as a Lead Architect at Fitech Labs, focusing on high-performance software for trading systems, where he jump-started a new distributed caching and grid computing product line scaling out to 100s computers.

Dmitriy holds a Bachelor of Science in Computer Science from University of California at Davis specializing in Networking and Algorithms.

Read Dmitriy’s personal blog on Blogspot.com.

GridGain Confirms: Apache Ignite Performance 2x Faster than Hazelcast

GridGain Confirms: Apache Ignite Performance 2x Faster than Hazelcast

Dmitriy Setrakyan

February 19, 2016

I feel that I should clarify some confusion that arose from an inflammatory blog written by Hazelcast CEO, Greg Luck, accusing the Apache® Ignite™ community of "faking" the benchmark results. To be honest, we were very surprised to see this blog from Hazelcast. Why wouldn't Mr. Luck at least make an effort and reach out to the Ignite community or the GridGain team before making such outrageous…

GridGain In-Memory Data Fabric 7.5 is Released

GridGain In-Memory Data Fabric 7.5 is Released

Dmitriy Setrakyan

February 11, 2016

Today the GridGain team has announced the release of enterprise-grade GridGain In-Memory Data Fabric v. 7.5, based on Apache® Ignite™ v. 1.5. For those not familiar with GridGain or Apache Ignite, it provides the ability to distribute, cache, and compute on data in memory, including such features as in-memory data grid, compute grid, ANSI-99 in-memory SQL, real-time streaming, in-memory file…

Benchmarking Data Grids: Apache Ignite vs Hazelcast, Part II

Benchmarking Data Grids: Apache Ignite vs Hazelcast, Part II

Dmitriy Setrakyan

May 19, 2015

In my previous post I have demonstrated benchmarks for atomic JCache (JSR 107) operations and optimistic transactions between Apache Ignite™ data grid and Hazelcast. In this blog I will focus on benchmarking the pessimistic transactions. The difference between optimistic and pessimistic modes is in the lock acquisition. In pessimistic mode locks are acquired on first access, while in optimistic…

Benchmarking Data Grids: Apache Ignite (TM) (incubating) vs Hazelcast, Part I

Benchmarking Data Grids: Apache Ignite (TM) (incubating) vs Hazelcast, Part I

Dmitriy Setrakyan

April 14, 2015

Benchmarking Data Grids: Apache Ignite (TM) (incubating) vs Hazelcast, Part I

Recently I have been doing many benchmarks comparing the incubating Apache Ignite™ (incubating) project to other products. In this blog I will describe my experience in comparing Apache Ignite ™ (incubating) Data Grid vs Hazelcast Data Grid. Yardstick Framework I will be using Yardstick Framework for the benchmarks, specifically Yardstick-Docker extension. Yardstick is an open source…

Streaming and Transforming Data with Apache Ignite(TM) (incubating)

Streaming and Transforming Data with Apache Ignite(TM) (incubating)

Dmitriy Setrakyan

April 8, 2015

Streaming and Transforming Data with Apache Ignite(TM) (incubating)

In its 1.0 release Apache Ignitetm added much better streaming support with ability to perform various data transformations, as well as query the streamed data using standard SQL queries. Streaming in Ignite is generally used to ingest continuous large volumes of data into Ignite distributed caches (possibly configured with sliding windows). Streamers can also be used to simply preload large…

Apache Ignite(TM) (incubating) Word Count Streaming Example

Apache Ignite(TM) (incubating) Word Count Streaming Example

Dmitriy Setrakyan

April 5, 2015

Apache Ignite(TM) (incubating) Word Count Streaming Example

In this example we will stream text into Apache Ignite and count each individual word. We will also issue periodic SQL queries into the stream to query top 10 most popular words. The example will work as follows: We will setup up a cache to hold the words as they come from a stream. We will setup a 1 second sliding window to keep the words only for the last 1 second. StreamWords program…

Asynchronous APIs Done Right

Asynchronous APIs Done Right

Dmitriy Setrakyan

March 9, 2015

Ever seen a product which has duplicated mirrored APIs for synchronous and asynchronous processing? I never liked such APIs as they introduce extra noise to what otherwise could be considered a clean design. There is really no point to have myMethod() and myMethodAsync() methods while all you are trying to do is to change the mode of method execution from synchronous to asynchronous.

One-Phase-Commit - Fast Transactions For In-Memory Caches

One-Phase-Commit - Fast Transactions For In-Memory Caches

Dmitriy Setrakyan

September 16, 2014

In this blog we cover a very important optimization that can be utilized for in-memory caches, specifically for cases where data is partitioned across the network.

Two-Phase-Commit for In-Memory Caches - Part II

Two-Phase-Commit for In-Memory Caches - Part II

Dmitriy Setrakyan

September 9, 2014

In this blog we will cover a case when an in-memory cache serves as a layer on top of a persistent database. In this case the database serves as a primary system of records, and distributed in-memory cache is added for performance and scalability reasons to accelerate reads and (sometimes) writes to the data.

Two-Phase-Commit for Distributed In-Memory Caches

Two-Phase-Commit for Distributed In-Memory Caches

Dmitriy Setrakyan

September 2, 2014

2-Phase-Commit is probably one of the oldest consensus protocols and is known for its deficiencies when it comes to handling failures, as it may indefinitely block the servers waiting in prepare state. To mitigate this, a 3-Phase-Commit protocol was introduced which adds better fault tolerance at the expense of extra network round-trip message and higher latencies.

GridGain 6.2.0 Released with Portable Objects and Distributed Services

GridGain 6.2.0 Released with Portable Objects and Distributed Services

Dmitriy Setrakyan

August 27, 2014

We are pleased to announce the release of GridGain Open Source In-Memory Computing Platform 6.2.0. The main components of the platform are: compute grid, data grid (or in-memory distributed cache), and CEP streaming. This release revolves primarily around Portable Object functionality as well as Distributed (or Guaranteed) Services.

GridGain 6.1.9 - One Click Installation - One Jar Dependency

GridGain 6.1.9 - One Click Installation - One Jar Dependency

Dmitriy Setrakyan

July 3, 2014

With release 6.1.9, GridGain significantly simplified its installation and deployment. GridGain now allows for: One Click Installation: The product simply has to be downloaded and unzipped. After that it is ready to be used. One Jar Dependency: GridGain now has only one mandatory dependency - gridgain-6.1.9.jar. All other jars are optional.

G1 Garbage Collector Thrashes On Larger Heaps

G1 Garbage Collector Thrashes On Larger Heaps

Dmitriy Setrakyan

June 16, 2014

At GridGain, having worked on a distributed caching (data grid) product for many years, we constantly benchmark with various Garbage Collectors to find the optimal configuration for larger heap sizes. From conducting numerous tests, we have concluded that unless you are utilizing some off-heap technology (e.g. GridGain OffHeap), no Garbage Collector provided with JDK will render any kind of…

Distributed Caching In 5 Minutes

Distributed Caching In 5 Minutes

Dmitriy Setrakyan

June 10, 2014

If you prefer a video demo with coding examples, visit the original blog post at gridgain.blogspot.com. Distributed In-Memory Caching generally allows you to replicate or partition your data in memory across your cluster. Memory provides a much faster access to the data, and by utilizing multiple cluster nodes the performance and scalability of the application increases significantly. Majority…

Getting Started With Clustering

Getting Started With Clustering

Dmitriy Setrakyan

May 1, 2014

What do Clustering frameworks really do? More often than not clustering frameworks will provide capability to auto-discover servers on the network, share resources, and schedule tasks. Some will also add distributed messaging and distributed event notification capabilities.