Click here to close now.

Welcome!

Big Data Journal Authors: Klaus Enzenhofer, Roger Strukhoff, Carmen Gonzalez, Leo Reiter, Michael Jannery

Related Topics: Big Data Journal, Java, Open Source, Virtualization, Red Hat, Cloud Expo

Big Data Journal: Blog Feed Post

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...

More Stories By 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.

@BigDataExpo Stories
The industrial software market has treated data with the mentality of “collect everything now, worry about how to use it later.” We now find ourselves buried in data, with the pervasive connectivity of the (Industrial) Internet of Things only piling on more numbers. There’s too much data and not enough information. In his session at @ThingsExpo, Bob Gates, Global Marketing Director, GE’s Intelligent Platforms business, to discuss how realizing the power of IoT, software developers are now focu...
Containers and microservices have become topics of intense interest throughout the cloud developer and enterprise IT communities. Accordingly, attendees at the upcoming 16th Cloud Expo at the Javits Center in New York June 9-11 will find fresh new content in a new track called PaaS | Containers & Microservices Containers are not being considered for the first time by the cloud community, but a current era of re-consideration has pushed them to the top of the cloud agenda. With the launch ...
Operational Hadoop and the Lambda Architecture for Streaming Data Apache Hadoop is emerging as a distributed platform for handling large and fast incoming streams of data. Predictive maintenance, supply chain optimization, and Internet-of-Things analysis are examples where Hadoop provides the scalable storage, processing, and analytics platform to gain meaningful insights from granular data that is typically only valuable from a large-scale, aggregate view. One architecture useful for capturing...
SYS-CON Events announced today that Vitria Technology, Inc. will exhibit at SYS-CON’s @ThingsExpo, which will take place on June 9-11, 2015, at the Javits Center in New York City, NY. Vitria will showcase the company’s new IoT Analytics Platform through live demonstrations at booth #330. Vitria’s IoT Analytics Platform, fully integrated and powered by an operational intelligence engine, enables customers to rapidly build and operationalize advanced analytics to deliver timely business outcomes ...
Thanks to Docker, it becomes very easy to leverage containers to build, ship, and run any Linux application on any kind of infrastructure. Docker is particularly helpful for microservice architectures because their successful implementation relies on a fast, efficient deployment mechanism – which is precisely one of the features of Docker. Microservice architectures are therefore becoming more popular, and are increasingly seen as an interesting option even for smaller projects, instead of bein...
The explosion of connected devices / sensors is creating an ever-expanding set of new and valuable data. In parallel the emerging capability of Big Data technologies to store, access, analyze, and react to this data is producing changes in business models under the umbrella of the Internet of Things (IoT). In particular within the Insurance industry, IoT appears positioned to enable deep changes by altering relationships between insurers, distributors, and the insured. In his session at @Things...
SYS-CON Events announced today that Open Data Centers (ODC), a carrier-neutral colocation provider, will exhibit at SYS-CON's 16th International Cloud Expo®, which will take place June 9-11, 2015, at the Javits Center in New York City, NY. Open Data Centers is a carrier-neutral data center operator in New Jersey and New York City offering alternative connectivity options for carriers, service providers and enterprise customers.
SYS-CON Media announced that IBM, which offers the world’s deepest portfolio of technologies and expertise that are transforming the future of work, has launched ad campaigns on SYS-CON’s numerous online magazines such as Cloud Computing Journal, Virtualization Journal, SOA World Magazine, and IoT Journal. IBM’s campaigns focus on vendors in the technology marketplace, the future of testing, Big Data and analytics, and mobile platforms.
Even as cloud and managed services grow increasingly central to business strategy and performance, challenges remain. The biggest sticking point for companies seeking to capitalize on the cloud is data security. Keeping data safe is an issue in any computing environment, and it has been a focus since the earliest days of the cloud revolution. Understandably so: a lot can go wrong when you allow valuable information to live outside the firewall. Recent revelations about government snooping, along...
In his session at DevOps Summit, Tapabrata Pal, Director of Enterprise Architecture at Capital One, will tell a story about how Capital One has embraced Agile and DevOps Security practices across the Enterprise – driven by Enterprise Architecture; bringing in Development, Operations and Information Security organizations together. Capital Ones DevOpsSec practice is based upon three "pillars" – Shift-Left, Automate Everything, Dashboard Everything. Within about three years, from 100% waterfall, C...
The explosion of connected devices / sensors is creating an ever-expanding set of new and valuable data. In parallel the emerging capability of Big Data technologies to store, access, analyze, and react to this data is producing changes in business models under the umbrella of the Internet of Things (IoT). In particular within the Insurance industry, IoT appears positioned to enable deep changes by altering relationships between insurers, distributors, and the insured. In his session at @Things...
Data-intensive companies that strive to gain insights from data using Big Data analytics tools can gain tremendous competitive advantage by deploying data-centric storage. Organizations generate large volumes of data, the vast majority of which is unstructured. As the volume and velocity of this unstructured data increases, the costs, risks and usability challenges associated with managing the unstructured data (regardless of file type, size or device) increases simultaneously, including end-to-...
The excitement around the possibilities enabled by Big Data is being tempered by the daunting task of feeding the analytics engines with high quality data on a continuous basis. As the once distinct fields of data integration and data management increasingly converge, cloud-based data solutions providers have emerged that can buffer your organization from the complexities of this continuous data cleansing and management so that you’re free to focus on the end goal: actionable insight.
When it comes to the Internet of Things, hooking up will get you only so far. If you want customers to commit, you need to go beyond simply connecting products. You need to use the devices themselves to transform how you engage with every customer and how you manage the entire product lifecycle. In his session at @ThingsExpo, Sean Lorenz, Technical Product Manager for Xively at LogMeIn, will show how “product relationship management” can help you leverage your connected devices and the data th...
With several hundred implementations of IoT-enabled solutions in the past 12 months alone, this session will focus on experience over the art of the possible. Many can only imagine the most advanced telematics platform ever deployed, supporting millions of customers, producing tens of thousands events or GBs per trip, and hundreds of TBs per month. With the ability to support a billion sensor events per second, over 30PB of warm data for analytics, and hundreds of PBs for an data analytics arc...
The Internet of Things (IoT) is causing data centers to become radically decentralized and atomized within a new paradigm known as “fog computing.” To support IoT applications, such as connected cars and smart grids, data centers' core functions will be decentralized out to the network's edges and endpoints (aka “fogs”). As this trend takes hold, Big Data analytics platforms will focus on high-volume log analysis (aka “logs”) and rely heavily on cognitive-computing algorithms (aka “cogs”) to mak...
Operational Hadoop and the Lambda Architecture for Streaming Data Apache Hadoop is emerging as a distributed platform for handling large and fast incoming streams of data. Predictive maintenance, supply chain optimization, and Internet-of-Things analysis are examples where Hadoop provides the scalable storage, processing, and analytics platform to gain meaningful insights from granular data that is typically only valuable from a large-scale, aggregate view. One architecture useful for capturing...
One of the biggest impacts of the Internet of Things is and will continue to be on data; specifically data volume, management and usage. Companies are scrambling to adapt to this new and unpredictable data reality with legacy infrastructure that cannot handle the speed and volume of data. In his session at @ThingsExpo, Don DeLoach, CEO and president of Infobright, will discuss how companies need to rethink their data infrastructure to participate in the IoT, including: Data storage: Understand...
Since 2008 and for the first time in history, more than half of humans live in urban areas, urging cities to become “smart.” Today, cities can leverage the wide availability of smartphones combined with new technologies such as Beacons or NFC to connect their urban furniture and environment to create citizen-first services that improve transportation, way-finding and information delivery. In her session at @ThingsExpo, Laetitia Gazel-Anthoine, CEO of Connecthings, will focus on successful use c...
The true value of the Internet of Things (IoT) lies not just in the data, but through the services that protect the data, perform the analysis and present findings in a usable way. With many IoT elements rooted in traditional IT components, Big Data and IoT isn’t just a play for enterprise. In fact, the IoT presents SMBs with the prospect of launching entirely new activities and exploring innovative areas. CompTIA research identifies several areas where IoT is expected to have the greatest impac...