HPC Databases: The Data Ingest Challenge

By Steven Graves

June 1, 2007

While much attention, technology and consulting fees are dedicated to getting data out of database management systems (DBMSs) rapidly, with faster queries, much less interest is paid to optimizing the process of putting the data in.

This stems at least partly from the fact that most enterprise data is not loaded all at once. Instead, it is built up gradually through a company's everyday processes such as sales, shipping and manufacturing. The data is already in the database when someone needs it. But queries against this data are often intended to support tasks with exacting deadlines — hence the emphasis on optimizing query performance.

But for HPC, optimizing the process of filling up a database — also called database provisioning or “data ingest” — takes on greater importance, due to the fact that many HPC analytical tasks such as data mining and scientific research involve assembling very large databases (VLDBs) using previously collected data external to the database management system. This aggregation could require a database to ingest terabytes before queries can be performed to support a time-sensitive analytical goal, such as predicting a competitor's next move or discovering a new blockbuster drug.

The VLDB ingest process can stretch into days, which is a highly unwelcome delay for a competitive project. And the wait is exacerbated by a particular frustration of VLDB loading: as more data is added, the ingest rate slows down markedly, with the record-per-second insertion rate waning by more than half. This article explores the technological reasons for this dwindling performance, and a possible solution in the use of database technology that stores and manages records entirely in memory.

Certain techniques are available to accelerate ingest with traditional on-disk enterprise database systems. For example, when using the INSERT statement (part of the industry standard SQL database language) to load data row-by-row from a flat file into relational database tables, it can help to strip all indexes (data structures commonly used for quick access to records) from the tables, because the database system can create new indexes more quickly after the insert process is complete, than it can update indexes as records are inserted. This approach can be combined with special bulk load features of many database systems that insert multiple records simultaneously, for greater efficiency.

But a progressively dwindling ingest rate remains a problem, due to facts of enterprise database system structure. Traditional database systems are premised on storing data on permanent media. Because commonly-used hard disks operate mechanically, with spinning platters and heads that slew across the disk surface, such disks are thousands of times slower than processes working in solid state hardware, such as writing to a region of memory. In recognition of this performance burden, database systems provide a cache to hold frequently-used records in memory.

To speed data ingest, records are written to the cache. But eventually, memory buffers fill up, and the system writes the data to the file system (logical I/O). Each logical I/O requires some time interval (usually measured in microseconds). Eventually, the file system buffers also fill up, necessitating writing of the data to the hard disk (at which point logical I/O implicitly becomes physical I/O). Physical I/O is usually measured in milliseconds, therefore its performance burden is several orders of magnitude greater than logical I/O. Physical I/O may also be imposed by the DBMS, for instance to guarantee transactional integrity.

Figure 1Mounting I/O requirements — both logical and physical — impair performance more and more as a database grows larger, for several reasons. First, as database size increases, the tree indexes used to organize data grow deeper, and the average number of steps into the tree, to reach the storage location, expands. Each step imposes a logical disk I/O. Assuming cache size stays the same, then the percent of the of the database that is cached is smaller; therefore the likelihood that a logical I/O is actually physical I/O is greater.

Second, assuming that the cache size stays the same, the percent of the database that is cached is smaller. Therefore, it is likely that any logical disk I/O — not only from tree navigation, but also the abundant I/O from writing pages of data files, checkpointing, log files, and other operations — is actually physical disk I/O.

Third, not all instances of physical I/O are equal. As the database gets larger, it consumes more physical space on the disk platter, and the average time to move the head from position to position is greater. The greater the distance traversed by the head, the longer each physical I/O's time interval. Therefore each individual physical I/O can represent a greater performance burden as data ingest proceeds.

The VLDB data ingest slowdown is endemic to traditional disk-based database architecture. As it turns out, a way to avoid this problem with provisioning — and to obtain significantly faster queries as well — is to completely eliminate physical I/O and logical I/O, along with reliance on a file system and hard disk, from the database system.

Figure 2A relatively new database technology, the in-memory database system (IMDS), has been examined largely from the standpoint of extracting data. IMDSs store records entirely in memory — they never go to disk. Because an in-memory database is already in memory, a cache would be redundant. Accordingly, the DBMS cache logic is eliminated, as is all interaction with the file system and its separate cache, and with the physical media. A major consequence of this simplification is a dramatic reduction in the number of movements (copies) of the data as it makes its way from the application to the DBMS storage.

Querying is greatly accelerated via elimination of I/O, caching and related overhead. This accomplishment alone would merit consideration of in-memory database technology in high performance computing applications (for an idea of IMDS query performance, see the benchmark results presented below). But IMDS proponents have also suggested that by jettisoning both logical and physical I/O, their systems should be able to ingest very large amounts of data without experiencing the performance falloff inherent in disk-based systems.

Until recently, this advantage existed only in theory. IMDS performance remained uncharted in the terabyte-plus size range, due partly to the technology's relative newness and partly to its origins in real-time embedded systems, which did not require management of very large databases.

But as in-memory databases catch HPC system architects' attention, and roll out in VLDB systems such as stock exchanges' automated trading applications, engineers are pushing the technology beyond previously known boundaries.

McObject helped move this software category forward in late 2006 when it tested its eXtremeDB-64 in-memory database beyond the one terabyte size boundary. The benchmark took place on a 64-bit Linux-based 160-core SGI Altix 4700 server housed at the Louisiana Immersive Technology Enterprise, a supercomputing research center housed at the Research Park of the University of Louisiana at Lafayette.

Testing ingest performance was an important goal — engineers sought to nail down whether the VLDB ingest rate would remain stable, without the performance slowdown seen in on-disk databases. (No IMDS vendor had, until then, published a benchmark involving a terabyte or more of data.)

For the benchmark, engineers created a simple database structure consisting of two tables: a PERSONS table and an ORDER table. These tables represent the two sides of any generic transaction in which there are two instances of a 'person' (one for each side of the transaction) and one instance of an 'order' that represents the transaction between the two entities. To populate the database, engineers created an array of 30,000 random strings and selected random elements from the array to populate the NAME and ADDRESS columns. Unique values for PERSON_ID and ORDER_ID were generated sequentially.

Engineers then created 3 billion PERSONS records (rows) and 12.54 billion ORDERS records (rows), resulting in a database size of 1.17 terabytes, and performed queries of varying degrees of complexity against this data store.

Benchmark results illustrate IMDSs' querying speed. For a simple SELECT statement, eXtremeDB processed 87.78 million query transactions per second using its native application programming interface (API) and 28.14 million queries per second using a SQL ODBC API. In a more complex JOIN operation, a result of 11.13 million queries per second was achieved with the native API, and 4.36 million queries per second using SQL ODBC. Comparing performance between different applications in different operating environments is notoriously tricky. But to put these results in perspective, consider that the “standard currency” of such comparisons is transactions per minute. From these results, it is understandable why IMDS technology could appeal to the designer of corporate data mining or pharmaceuticals research applications seeking to rapidly sift great volumes of data.

The benchmark also documented the predicted, but until now unproven, data ingest advantage: based on the results, IMDSs appear to avoid the performance plunge imposed by on-disk databases as they grow larger. Total provisioning time for the 1.17 terabyte, 15.54 billion row eXtremeDB-64 database was just over 33 hours. The per-row insert time for the first quartile of data was 6.9 microseconds, while the rate for the last quartile was a very respectable 8.3 microseconds. Ingest performance between first and last quartile decreased by just 20 percent — much less than the precipitous performance drop-off seen in the later stages of populating an on-disk very large database.

It is important not to confuse the time required to ingest a data set, with the time needed to back up the data once it has been loaded, or to restore it after potential failure. As part of this benchmark test, engineers backed up the fully provisioned in-memory database in 4.3 hours, and restored it in 4.76 hours. So, while initial ingest took 33 hours, the database could be saved and reloaded for subsequent use in a fraction of that time. (And, once reloaded, it can be extended with new data.) Backing up and restoring the provisioned database is a simple matter of streaming the in-memory image to persistent storage; there is no need to allocate pages, assign records to pages, maintain indexes, etc., so back up and restore is largely a function of the speed of the persistent media.

Backup and restore capabilities are especially important when working with in-memory database systems because these functions are the primary means to achieve data persistence. While IMDSs offer persistence mechanisms such as transaction logging, the types of applications served by IMDSs for data analysis have data persistence needs that differ from most mainstream enterprise systems. Data mining, modeling and other analytics applications exist to process data in its transient state, rather than provide long-term storage.

Querying will likely remain the “superstar” function of in-memory databases and the most talked about performance advantage. But backup and restore are important, and complement IMDSs' highly efficient ingest capability. They ensure data is available when needed to support time-sensitive, data-intensive tasks.

—–

About the Author

Steven Graves co-founded McObject (www.mcobject.com) in 2001. As the company's president and CEO, he has helped the company attain its goal of providing embedded database technology that makes embedded systems smarter, more reliable and more cost-effective to develop and maintain. Prior to McObject, Mr. Graves was president and chairman of Centura Solutions Corporation, and vice president of Worldwide Consulting for Centura Software Corporation; he also served as president and chief operating officer of Raima Corporation. Mr. Graves is a member of the advisory board for the University of Washington's certificate program in Embedded and Real Time Systems Programming.

Subscribe to HPCwire's Weekly Update!

Be the most informed person in the room! Stay ahead of the tech trends with industy updates delivered to you every week!

Senegal Prepares to Take Delivery of Atos Supercomputer

January 16, 2019

In just a few months time, Senegal will be operating the second largest HPC system in sub-Saharan Africa. The Minister of Higher Education, Research and Innovation Mary Teuw Niane made the announcement on Monday (Jan. 14 Read more…

By Tiffany Trader

Google Cloud Platform Extends GPU Instance Options

January 16, 2019

If it's Nvidia GPUs you're after to power your AI/HPC/visualization workload, Google Cloud has them, now claiming "broadest GPU availability." Each of the three big public cloud vendors has by turn touted the latest and Read more…

By Tiffany Trader

A Big Data Journey While Seeking to Catalog our Universe

January 16, 2019

It turns out, astronomers have lots of photos of the sky but seek knowledge about what the photos mean. Sound familiar? Big data problems are often characterized as transforming data into insights – which is exactly wh Read more…

By James Reinders

HPE Extreme Performance Solutions

HPE Systems With Intel Omni-Path: Architected for Value and Accessible High-Performance Computing

Today’s high-performance computing (HPC) and artificial intelligence (AI) users value high performing clusters. And the higher the performance that their system can deliver, the better. Read more…

IBM Accelerated Insights

Resource Management in the Age of Artificial Intelligence

New challenges demand fresh approaches

Fueled by GPUs, big data, and rapid advances in software, the AI revolution is upon us. Read more…

STAC Floats ML Benchmark for Financial Services Workloads

January 16, 2019

STAC (Securities Technology Analysis Center) recently released an ‘exploratory’ benchmark for machine learning which it hopes will evolve into a firm benchmark or suite of benchmarking tools to compare the performanc Read more…

By John Russell

A Big Data Journey While Seeking to Catalog our Universe

January 16, 2019

It turns out, astronomers have lots of photos of the sky but seek knowledge about what the photos mean. Sound familiar? Big data problems are often characterize Read more…

By James Reinders

STAC Floats ML Benchmark for Financial Services Workloads

January 16, 2019

STAC (Securities Technology Analysis Center) recently released an ‘exploratory’ benchmark for machine learning which it hopes will evolve into a firm benchm Read more…

By John Russell

IBM Quantum Update: Q System One Launch, New Collaborators, and QC Center Plans

January 10, 2019

IBM made three significant quantum computing announcements at CES this week. One was introduction of IBM Q System One; it’s really the integration of IBM’s Read more…

By John Russell

IBM’s New Global Weather Forecasting System Runs on GPUs

January 9, 2019

Anyone who has checked a forecast to decide whether or not to pack an umbrella knows that weather prediction can be a mercurial endeavor. It is a Herculean task: the constant modeling of incredibly complex systems to a high degree of accuracy at a local level within very short spans of time. Read more…

By Oliver Peckham

The Case Against ‘The Case Against Quantum Computing’

January 9, 2019

It’s not easy to be a physicist. Richard Feynman (basically the Jimi Hendrix of physicists) once said: “The first principle is that you must not fool yourse Read more…

By Ben Criger

The Deep500 – Researchers Tackle an HPC Benchmark for Deep Learning

January 7, 2019

How do you know if an HPC system, particularly a larger-scale system, is well-suited for deep learning workloads? Today, that’s not an easy question to answer Read more…

By John Russell

HPCwire Awards Highlight Supercomputing Achievements in the Sciences

January 3, 2019

In November at SC18 in Dallas, HPCwire Readers’ and Editors’ Choice awards program commemorated its 15th year of honoring achievement in HPC, with categories ranging from Best Use of AI to the Workforce Diversity Leadership Award and recipients across a wide variety of industrial and research sectors. Read more…

By the Editorial Team

White House Top Science Post Filled After Two-Year Vacancy

January 3, 2019

Half-way into Trump's term, the Senate has confirmed a director for the Office of Science and Technology Policy (OSTP), the agency that coordinates science poli Read more…

By Tiffany Trader

Quantum Computing Will Never Work

November 27, 2018

Amid the gush of money and enthusiastic predictions being thrown at quantum computing comes a proposed cold shower in the form of an essay by physicist Mikhail Read more…

By John Russell

Cray Unveils Shasta, Lands NERSC-9 Contract

October 30, 2018

Cray revealed today the details of its next-gen supercomputing architecture, Shasta, selected to be the next flagship system at NERSC. We've known of the code-name "Shasta" since the Argonne slice of the CORAL project was announced in 2015 and although the details of that plan have changed considerably, Cray didn't slow down its timeline for Shasta. Read more…

By Tiffany Trader

Summit Supercomputer is Already Making its Mark on Science

September 20, 2018

Summit, now the fastest supercomputer in the world, is quickly making its mark in science – five of the six finalists just announced for the prestigious 2018 Read more…

By John Russell

AMD Sets Up for Epyc Epoch

November 16, 2018

It’s been a good two weeks, AMD’s Gary Silcott and Andy Parma told me on the last day of SC18 in Dallas at the restaurant where we met to discuss their show news and recent successes. Heck, it’s been a good year. Read more…

By Tiffany Trader

US Leads Supercomputing with #1, #2 Systems & Petascale Arm

November 12, 2018

The 31st Supercomputing Conference (SC) - commemorating 30 years since the first Supercomputing in 1988 - kicked off in Dallas yesterday, taking over the Kay Ba Read more…

By Tiffany Trader

The Case Against ‘The Case Against Quantum Computing’

January 9, 2019

It’s not easy to be a physicist. Richard Feynman (basically the Jimi Hendrix of physicists) once said: “The first principle is that you must not fool yourse Read more…

By Ben Criger

Contract Signed for New Finnish Supercomputer

December 13, 2018

After the official contract signing yesterday, configuration details were made public for the new BullSequana system that the Finnish IT Center for Science (CSC Read more…

By Tiffany Trader

House Passes $1.275B National Quantum Initiative

September 17, 2018

Last Thursday the U.S. House of Representatives passed the National Quantum Initiative Act (NQIA) intended to accelerate quantum computing research and developm Read more…

By John Russell

Leading Solution Providers

SC 18 Virtual Booth Video Tour

Advania @ SC18 AMD @ SC18
ASRock Rack @ SC18
DDN Storage @ SC18
HPE @ SC18
IBM @ SC18
Lenovo @ SC18 Mellanox Technologies @ SC18
NVIDIA @ SC18
One Stop Systems @ SC18
Oracle @ SC18 Panasas @ SC18
Supermicro @ SC18 SUSE @ SC18 TYAN @ SC18
Verne Global @ SC18

Nvidia’s Jensen Huang Delivers Vision for the New HPC

November 14, 2018

For nearly two hours on Monday at SC18, Jensen Huang, CEO of Nvidia, presented his expansive view of the future of HPC (and computing in general) as only he can do. Animated. Backstopped by a stream of data charts, product photos, and even a beautiful image of supernovae... Read more…

By John Russell

HPE No. 1, IBM Surges, in ‘Bucking Bronco’ High Performance Server Market

September 27, 2018

Riding healthy U.S. and global economies, strong demand for AI-capable hardware and other tailwind trends, the high performance computing server market jumped 28 percent in the second quarter 2018 to $3.7 billion, up from $2.9 billion for the same period last year, according to industry analyst firm Hyperion Research. Read more…

By Doug Black

HPC Reflections and (Mostly Hopeful) Predictions

December 19, 2018

So much ‘spaghetti’ gets tossed on walls by the technology community (vendors and researchers) to see what sticks that it is often difficult to peer through Read more…

By John Russell

Intel Confirms 48-Core Cascade Lake-AP for 2019

November 4, 2018

As part of the run-up to SC18, taking place in Dallas next week (Nov. 11-16), Intel is doling out info on its next-gen Cascade Lake family of Xeon processors, specifically the “Advanced Processor” version (Cascade Lake-AP), architected for high-performance computing, artificial intelligence and infrastructure-as-a-service workloads. Read more…

By Tiffany Trader

Germany Celebrates Launch of Two Fastest Supercomputers

September 26, 2018

The new high-performance computer SuperMUC-NG at the Leibniz Supercomputing Center (LRZ) in Garching is the fastest computer in Germany and one of the fastest i Read more…

By Tiffany Trader

Houston to Field Massive, ‘Geophysically Configured’ Cloud Supercomputer

October 11, 2018

Based on some news stories out today, one might get the impression that the next system to crack number one on the Top500 would be an industrial oil and gas mon Read more…

By Tiffany Trader

Microsoft to Buy Mellanox?

December 20, 2018

Networking equipment powerhouse Mellanox could be an acquisition target by Microsoft, according to a published report in an Israeli financial publication. Microsoft has reportedly gone so far as to engage Goldman Sachs to handle negotiations with Mellanox. Read more…

By Doug Black

The Deep500 – Researchers Tackle an HPC Benchmark for Deep Learning

January 7, 2019

How do you know if an HPC system, particularly a larger-scale system, is well-suited for deep learning workloads? Today, that’s not an easy question to answer Read more…

By John Russell

  • arrow
  • Click Here for More Headlines
  • arrow
Do NOT follow this link or you will be banned from the site!
Share This