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Big Data – Storage Mediums and Data Structures
Big Data presents something of a storage dilemma. There is no one data store to rule them all.

My working title was Big Data, Storage Dilemma.

They say dilemma. I say dilemna. I'm serious. I spell it dilemna.

Big Data presents something of a storage dilemma. There is no one data store to rule them all.

Should different data structures be persisted to different storage mediums?

Storage Medium
Identifying the appropriate medium is a function of performance, cost, and capacity.

Random Access Memory
It's fast. Very. It's expense. Very.

If we are configuring an HP DL980 G7 server, it will cost $3,672 for 128GB of memory with 8x 16GB modules or $9,996 for 128GB of memory with 4x 32GB modules. That is $28-78 / GB.

We can configure an HP DL980 G7 server with up to 4TB of memory.

Solid State Drive
It's not as fast or as expensive as random access memory. It's faster than a hard disk drive. A lot faster. It's more expensive than a hard disk drive. A lot more expensive.

If we are configuring an HP DL980 G7 server, it will cost $4,199 for a 400GB SAS MLC drive or $7,419 for a 400GB SAS SLC drive. That is $10-18 / GB. It's a performance / size trade-off. While SLC drives often perform better, MLC drives are often available in larger sizes. Further, the read / write performance is either symmetric or asymmetric.

That, and SLC drives often have greater endurance.


Sequential
Read
(MB/s)
Sequential
Write
(MB/s)
Random
Read
(IOPS)
Random
Write
(IOPS)
Intel 520 (480GB) 550 520 50,000 50,000
Intel 520 (240GB) 550 520 50,000 80,000
Intel DC S3700 500 460 75,000 36,000

The performance of solid state drives can vary:

  • consumer / enterprise
  • MLC / eMLC / SLC
  • SATA / SAS / PCIe

The capacity of enterprise solid state drives is often less than that of enterprise hard disk drives (4TB). However, the capacity of PCIe drives such as the Fusion-io ioDrive Octal (5.12TB / 10.24TB) is greater than that of enterprise hard disk drives (4TB). In addition, the performance of PCIe drives is greater than that of SAS or SATA drives.


Sequential
Read
(MB/s)
Sequential
Write
(MB/s)
Random
Read
(IOPS)
Random
Write
(IOPS)
Intel 910 (800GB) 2,000 1,000 180,000 75,000

Hard Disk Drive

It may not be fast, but it is inexpensive.

If we are configuring an HP DL980 G7 server, it will cost $309 for a 300GB 10K drive or $649 for a 300GB 15K drive. That is $1-2 / GB. It's a performance / size trade-off. While a 15K drive will perform better than a 10K drive, it will often have less capacity. The sequential read / write performance of hard disk drives is often between 150-200MB/s. As such, a RAID configuration may be a cost effective alternative to a single solid state drive for sequential read / write access.

The capacity of enterprise hard disk drives (4TB) is often greater than that of enterprise solid state drives.

Data Structure
Hash Table

JBoss Data Grid is an in-memory data grid with the data stored in a hash table. However, JBoss Data Grid supports persistence via write-behind or write-through. For all intents and purposes (e.g. map / reduce), all of the data should fit in memory.

Access

  • Random Reads, Random Writes

Riak is a key / value store. If persistence has been configured with Bitcast, the data is stored in a log structured hash table. An in-memory hash table contains the key / value pointers. The value is a pointer to the data. As such, all of the key / value pointers must fit in memory. The data is persisted via append only log files.

Access

  • Complex Index in Memory
  • Random Reads, Sequential Writes

Point queries perform well in hash tables. Range queries do not. Though there is always map / reduce.

B-Tree
MongoDB is a document database with the data stored in a B-Tree. The data is persisted via memory mapped files.

Access

  • Partial Index (Internal Nodes) in Memory
  • Random Reads, Sequential Reads, Random Writes

CouchDB is a document database with the data stored in a B+Tree. The data is persisted via append only log files.

Access

  • Partial Index in Memory
  • Random Reads, Sequential Reads, Sequential Writes

In a B+ Tree, the data is only stored in the leaf nodes. In a B-Tree, the data is stored in both the internal nodes and the leaf nodes. The advantage of a B+ Tree is that the leaf nodes are linked. As a result, range queries perform better with a B+ Tree. However, point queries perform better with a B-Tree.

CouchDB has implemented an append only B+ Tree. An alternative is the copy-on-write (CoW) B+ Tree. A write optimization for a B-Tree is buffering. First, the data written to an internal buffer in an internal node. Second, the buffer is flushed to a leaf node. As a result, random writes are turned in to sequential writes. The cost of random writes is thus amortized.  However, I am not aware of any open source data stores that have implemented a CoW B+ Tree or have implemented buffering with a B-Tree.

Range queries perform well with a B/B+ Tree. Point queries, not as well as hash tables.

Log Structured Merge Tree
Apache HBase and Apache Cassandra are both column oriented data stores, and they have both store data in an LSM-Tree. Apache HBase has implemented a cache oblivious lookahead array (COLA). Apache Cassandra has implemented a sorted array merge tree (SAMT).

In both implementations, a write is first written to a write-ahead log. Next, it is written to a memtable. A memtable is an in-memory sorted string table (SSTable). Later, the memtable is flushed and persisted as an SSTable. As result, random writes are turned in to sequential writes. However, a point query with an LSM-Tree may require multiple random reads.

Apache HBase implemented a single-level index with HFile version 1. However, because every index was cached, it resulted in high memory usage.

Apache HBase implemented a multi-level index a la a B+ Tree with HFile (SSTable) version 2. The SSTable contains a root index, a root index with leaf indexes, or a root index with an intermediate index and leaf indexes. The root index is stored in memory. The intermediate and leaf indexes may be stored in the block cache. In addition, the SSTable contains a compound (block level) bloom filter. The bloom filter is used to determine if the data is not in the data block.

Recommendation / Access

  • Partial Index / Bloom Filter in Memory
  • Random Reads, Sequential Reads, Sequential Writes

A read optimization for an LSM-Tree is fractional cascading. The idea is that each level contains both data and pointers to data in the next level. However, I am not aware of any open source data stores that have implemented fractional cascading.

I like the idea of a hybrid / tiered storage solution for an LSM-Tree implementation with the first level in memory, second level on solid state drives, and third level on hard disk drives. I've seen this solution described in academia, but I am not aware of any open source implementations.

Conclusion
I find it interesting that key / value stores often store data in a hash table, that document stores often store data in a B+/B-Tree, and that column oriented stores often store data in an LSM-Tree. Perhaps it is because key / value stores focus on the performance of point queries, document stores support secondary indexes (MongoDB) / views  (CouchDB), and column oriented stores support range queries. Perhaps it because document stores were not created with distribution (shards / partitions) in mind and thus assume that the complete index can not be stored in memory.

Notes
I found this paper to be very helpful in examining data structures as it covers most of the ones highlighted in this post:

Efficient, Scalable, and Versatile Application and System Transaction Management for Direct Storage Layers (link)

Read the original blog entry...

About Daniel Thompson
I curate the content on this page, but the credit goes to my talented colleagues for the posts that you see here. Much of what you read on this page is the work of friends at How to JBoss, and I encourage you to drop by the site at http://www.howtojboss.com for some of the best JBoss technical and non-technical content for developers, architects and technology executives on the Web.

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@CloudExpo Blogs
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The cloud computing infrastructure and platform market is expected to be a $43 billion market by 2018 and account for 11% of enterprise IT spending. At this point in the war we can say that AWS is the winner of the cloud infrastructure market segment only. The platform market (and what I believe is the more lucrative segment) is still up for grabs. The recent revenue announcement by AWS demonstrates what they mean for the industry and their focus on cloud since the beginning has given AWS leadership status. Microsoft and Google, of course, have been working hard, albeit lagging, in the battle ...
DoubleDown Interactive, a provider of online free-to-play casino games, needs to process huge amounts of streaming event data from their games and make that data rapidly available to data scientists and analysts. Hear how Amazon S3 and Snowflake’s Elastic Data Warehouse have made it possible for DoubleDown to deploy a reliable, scalable data architecture for their analytics needs. You’ll learn how Amazon S3 and Snowflake technology help address key challenges in building data pipelines and processing data and how DoubleDown combines Amazon Kinesis, S3, and Snowflake to ingest, store, and proc...
Mobile, social, Big Data, and cloud have fundamentally changed the way we live. “Anytime, anywhere” access to data and information is no longer a luxury; it’s a requirement, in both our personal and professional lives. For IT organizations, this means pressure has never been greater to deliver meaningful services to the business and customers. New tools, technologies, and frameworks have evolved recently to help manage rapid shifts in the technology landscape, but they’re only as good as the underlying processes that exist within the organization. As lines of business change or add requiremen...
It was recently revealed that Linux-powered drones were released which has unveiled a new frontier for open source software. Drone control, once limited to proprietary software, has now entered the open source realm. This opens up new possibilities of more flexible platforms for many Internet-connected objects as well as sparking conversation among the open source community about its potential relationship with the Internet of Things (IoT). Open source software has allowed drone builders and programmers to create a platform using a free open source software development kit. In some cases, i...
In “Thinking Like a Data Scientist – Part I”, we examined the challenges for getting the business users to think like data scientists when contemplating where and how to leverage big data to drive business value. We introduced a “Thinking Like a Data Scientist” process that starts with identifying and understanding the organization’s top-level strategic business initiatives, then uses a “Strategic Nouns” technique to create potential business questions that were descriptive, predictive or in nature.
Do you think development teams really update those BMC Remedy tickets with all the changes contained in a release? They don't. Most of them just "check the box" and move on. They rose a Risk Level that won't raise questions from the Change Control managers and they work around the checks and balances. The alternative is to stop and wait for a department that still thinks releases are rare events. When a release happens every day there's just not enough time for people to attend CAB meetings and file never-ending streams of CR tickets. Change control can't keep up with development. To ...
The Internet of Things is already changing the way we track fitness, manage our homes, and drive our cars. But while there is considerable discussion around how we securely provision our devices and who will have access to the data they capture, an important topic no one seems to be talking much about is the de-provisioning of smart objects. What happens when I ditch my Fitbit, trade in my connected car, or sell my house with its Nest thermostat, smart fridge and next-generation home security system? How do I manage to remove these smart devices from my life and make sure that no one has acces...
Providing the needed data for application development and testing is a huge headache for most organizations. The problems are often the same across companies – speed, quality, cost, and control. Provisioning data can take days or weeks, every time a refresh is required. Using dummy data leads to quality problems. Creating physical copies of large data sets and sending them to distributed teams of developers eats up expensive storage and bandwidth resources. And, all of these copies proliferating the organization can lead to inconsistent masking and exposure of sensitive data.
I read an insightful article this morning from Bernard Golden on DZone discussing the DevOps conundrum facing many enterprises today – is it better to build your own DevOps tools or go commercial? For Golden, the question arose from his observations at a number of DevOps Days events he has attended, where typically the audience is composed of startup professionals: “I have to say, though, that a typical feature of most presentations is a recitation of the various open source products and components and how they integrated them to implement their solution. In a word, how they created their ho...
What winning sales organizations do to separate themselves from the competition by creating market advantage through improved user experiences and better information services. The next BriefingsDirect thought-leader interview focuses on what winning sales organizations do to separate themselves from the competition by creating market advantage through improved user experiences and better information services.
One reason a lot of entrepreneurs flock to cloud-based services for their business is the cost. Native applications require licenses that are either too expensive or require elaborate set-up across all devices. In a recent article on the Entrepreneur, Bask Iyer, the CIO and Senior Vice President of Technology at Juniper Networks responded to the question, 'what entrepreneurs should put in the cloud?' His answer: "Everything." Bask elaborates by pointing out the ability of cloud to scale-up and scale-down at ease, the ease with which you can deploy plug-and-play cloud solutions for almost ever...
In this Power Panel at DevOps Summit, moderated by Jason Bloomberg, president of Intellyx, panelists Roberto Medrano, Executive Vice President at Akana; Lori MacVittie, IoT_Microservices Power PanelEvangelist for F5 Networks; and Troy Topnik, ActiveState’s Technical Product Manager; will peel away the buzz and discuss the important architectural principles behind implementing IoT solutions for the enterprise. As remote IoT devices and sensors become increasingly intelligent, they become part of our distributed cloud environment, and we must architect and code accordingly. At the very least, yo...
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Past SYS-CON Events
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cloudcomputingexpo
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Cloud Expo 2011 Allstar Conference Faculty

S.F.S.
Dell

Singer
NRO

Pereyra
Oracle

Ryan
OpSource

Butte
PwC

Leone
Oracle

Riley
AWS

Varia
AWS

Lye
Oracle

O'Connor
AppZero

Crandell
RightScale

Nucci
Dell Boomi

Hillier
CiRBA

Morrison
Layer 7 Tech

Robbins
NYT

Schwarz
Oracle

What The Enterprise IT World Says About Cloud Expo
 
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Navisite
 


 
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Univa UD
 


 
"Good crowd, good questions. The event looked very successful."
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CTO
Citrix Systems
 


 
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CTO
Brick Group