Storage Fusion Xcelerator

Transcription

1 Storage Fusion Xcelerator DDN Whitepaper Abstract: As organizations look for ways to increase the performance of their Big Data applications and file systems, they are increasingly turning to solid state drives for acceleration. However, replacing all spinning media with solid state is extremely expensive and drastically increases the total cost of ownership (TCO). What organizations would like is to extract solid state performance at the cost of spinning media. In this whitepaper, we provide an overview of DataDirect Networks' unique Storage Fusion Xcelerator (SFX) technology. SFX extends the functionality of the system cache using solid state media to accelerate both application and file system performance at a much lower TCO. This paper explains the 4 modes of acceleration offered by SFX and how they resolve today s Big Data challenges in an approach that is highly differentiated and much more cost effective than flash caching technologies offered by other vendors. Then, we will detail how SFX integrates with DDN s GRIDScaler & EXAScaler parallel file systems appliances and the new hscaler Apache Hadoop Big Data Appliance to truly accelerate performance, at the lowest TCO. Audience: The audience of this technical brief is an Operations/Architectural/ Systems Administrator level person who understands basic storage concepts and is considering the use of flash technology to accelerate their Big Data workflows. An introductory paper titled SFA Product Line: High Performance Solutions for Big Data can be used to become familiar with DDN's differentiated basic storage concepts DataDirect Networks. All Rights Reserved.

2 Introduction As organizations attempt to tackle the challenges of Big Data, they face a growing gap between the increasing CPU processing capabilities and the limited performance of disk-based storage - which cannot serve data quickly enough. This imbalance in performance represents a growing bottleneck, severely limiting an organization s ability to effectively handle Big Data workflows. Rotating media, such as SATA or SAS drives, have not kept pace with compute performance required to power these application sets. Pure, SSD-based approaches are simply too expensive and it is economically unfeasible to replace the large quantities of spinning disk with solid state storage. Flash caching is becoming a cost-effective way to deploy solid state storage, as it significantly improves performance by eliminating the rotational delay of a spinning platter. This dramatically reduces I/O bottlenecks by providing up to 300x more I/O Operations per Second (IOPS) than rotating media and performs at speeds much closer to RAM, helping to bridge the server-storage performance gap. However, traditional flash caching solutions in the marketplace are designed for small block I/O to accelerate databases like Oracle and MySQL, and applications like Exchange with a fairly structured I/O profile. They are not designed to handle Big Data workflows, which require the ability to handle mixed I/O (the combination of streaming and transactional) workloads. As a leader in Big Data, DDN designed SFX from the ground-up to handle such Big Data workloads. The SFX portfolio of flash acceleration tools includes SFX Read, SFX Write, SFX Instant Commit and SFX Context Commit, which are unique modes that help accelerate different IO patterns. These 4 modes work in conjunction with DDN s innovative Real Time Adaptive Caching Technology (ReACT ), which analyzes workloads in real-time to accelerate applications and file systems that require multi-dimensional storage performance. This makes SFX perfect for data-intensive applications such as Computational Simulation, Oil & Gas, Government Intelligence, Media, Web and Big Data analytics. This paper discusses the design imperative of SFX, and goes into detail about how it accelerates Big Data workflows, including file systems like GPFS, Lustre and HDFS DataDirect Networks. All Rights Reserved. 2

3 Introducing SFX SFX is a suite of storage acceleration tools that combine spinning and solid state disk storage media with application aware technology to intelligently accelerate block and file-based data access. It is part of DDN s Storage Fusion Architecture (SFA) Operating System and extends the functionality of the storage system s cache by selectively frontending traditional rotating media with some amount of flash memory. This yields acceleration in the context of the application. SFX consists of a pool of SSD flash-based drives that actually become an extension of the DRAM cache. SFX cache can be allocated to a Logical Unit Number (LUN), which refers to a logical disk created from a group of real disks, or can be shared between multiple LUNs. It has the effect of front-ending the LUN with some very fast and large cache, without having to dedicate expensive SSD drives to a single LUN. There are currently four modes of SFX cache, which determine how data is served, and when data gets flushed-out of cache based on available headroom. SFX Read Cache This mode is designed for read-intensive workloads. It caches frequently accessed data sets in the faster SFX tier to significantly speed up application performance. SFX Write Cache* This mode is designed for write-intensive workloads. It allows large writes to burst at full speed to the SFX tier - and then groups the data and writes it down to disk over time, eliminating the need to deploy petabytes of rotating media to meet extreme performance requirements. SFX Instant Commit This mode is designed for read after writes. It populates the SFX tier with new writes to warm-up cache as well and accelerates subsequent reads. SFX Content Commit This mode allows applications and file systems to send down application "hints" to the storage system, delivering the best storage acceleration possible by eliminating the need to guess what the IO pattern will be upon deployment. SFX Read How It Works SFX Read is designed to accelerate read operations by caching frequently accessed data. When a request for a block of data comes in, that block is read from rotating media and copied into the DRAM buffers from where it is served up to the host. This DRAM cache is relatively small and can saturate quickly from read and write operations. As the cache becomes full, and space for new data becomes necessary, a cache flush operation is performed to make room for new data. Traditionally, these cache flush operations throw the existing data away. In the case of SFX READ, the data from the cache flush operations is stored in the secondary flash SFX Read cache. Because the SFX Read cache is orders of magnitude larger than the DRAM cache, this allows entire data sets to be stored in read cache. When a read operation is requested, DRAM is checked to see if the data is resident. Next, the SFX Read cache is checked and data is satisfied from there. If the data is not found in the SFX Read cache, it is served from spinning media. *Future Implementation 2014 DataDirect Networks. All Rights Reserved. 3

4 Ideal Workloads - Accelerating random access reads, where the dataset being accessed exceeds traditional cache. It is perfect for applications that read hot files that are frequently accessed. Sample Use Cases Workflow WorkflOw Challenge SFX Read Enables... Post Production During Non Linear Editing (NLE), multiple editors work on a piece of media, viewing and editing content of the frames in sequence. This process is called scrubbing and suffers from high latency when working with high resolution video and audio The lowest latency scrubbing, even with multiple, very high resolution sources Quicker backward seeks of Post Production media Improves the ability of NLE systems to work with greater number and higher resolution sources Much smoother and responsive editing Genomics Transcoding Broadcast CDN and Web Applications Oil and Gas Search applications like BLAST compare whole genomes of smaller datasets with a lot of small IO for search and compare A single, high resolution file (called the mezzanine file), needs to be read with low latency by a farm of servers that encode it in parallel to multiple formats. The read performance of that file is typically a bottleneck and slows down the transcoding operation Play-to-air broadcasters rely on flawless execution of file-based workflows delays can result in dead air In hosting a web application, every millisecond counts. The end customer may click away if content does not load or takes too long, due to a slow storage system In the processing stage of an Oil and Gas workflow, applications like Promax face a bottleneck consisting of file metadata coupled with a small % of transactions which are small random read/write files Significantly reduced time to complete the search function Caching of the mezzanine file, delivers more consistent, faster file reads More transcoding now can be done by fewer servers with higher success rates, resulting in fewer errors and retries Transcoding can be performed with less equipment at a much lower TCO Faster, more consistent reads from cache, with improved quality of broadcast service Speeds up the performance of the highest demand content Improved end-user experience, lower customer abandonment rates and higher revenues for Web Application providers Significantly reduces pre & post processing times, by holding frequently accessed small files in cache Faster and more accurate drilling decisions leading to faster time to oil 2014 DataDirect Networks. All Rights Reserved. 4

5 SFX Write* How it Works SFX Write is designed for write intensive workloads and acts as a shock absorber for incoming writes. When a large block transfer comes in from the host, it is streamed onto the SFX Write pool at maximum data rate. When the burst write operation stops, the off-duty cycle period is used to pull the data from SFX Write cache and write it to traditional spinning media. Since this is being done on the back-end, it allows the system to coalesce the operations to spinning media, in order to reduce any seek operations and get the absolute maximum media transfer rate from the rotating media, making it much more efficient. With SFX Write, the SFX pool is configured for redundancy so that failures can be handled seamlessly. This caching mode increases the overall effective write bandwidth of rotating media. This is accomplished by using flash media to ingest random host I/O at or near full speed; then regrouping the data for a write operation to rotating media efficiently, such that the I/O profile is more of a sequential operation, with locality of reference. SFX Write has the ability to increase the rotating transfer rate of a typical SATA drive from a purely large block random I/O profile yielding about 35 MB/S per drive, to a more sequential I/O profile which can obtain as high as 150 MB/S. This effectively increases the average write data rate capability of the drives and lowers the overall system cost. Ideal workloads - Write-heavy applications that would benefit from smoothing-out bursty writes and/or minimizing latency for small writes. *Future Implementation 2014 DataDirect Networks. All Rights Reserved. 5

6 Sample Use Cases Workflow WorkflOw Challenge SFX write Enables... Generic HPC Application Film Scanning Post Production Transcoding Manufacturing/ CFD In HPC applications, disk activity is characterized by long periods of computation, followed by significant bursts of write checkpoint data and results. The sooner that this synchronous write data can be ingested by the storage subsystem, the sooner the next iteration of calculations can begin. Film scanning is write-intensive process. Scanning is done at the highest possible resolutions and retries are not acceptable if storage cannot keep up with the ingest rates of data. The files produced are usually very large and traditional caching techniques don t work. The Post Production process is interspersed with multiple read and then heavy writes phases. Writes can create mixed workloads and traditional caching techniques don t work well for such mixed workloads. Transcoding is a Write-intensive application that writes multiple files simultaneously. The performance is always hardware bound and more hardware is added to the workflows to accelerate transcoding. Applications like Fluent face a serious bottleneck during the checkpointing phase, which results in 100s of GBs of large writes that need to be processed by storage before the workflow can proceed. The storage system to ingest data at full speed Write the data to rotating media during low activity periods, while more computation is being performed Higher resolution scanning with improved consistency in scanning process and results Eliminates retries and errors Reduces the time required to perform film scanning Accelerates the write phases Delivers a smoother Post Production process with consistent performance, less errors & retries More transcoding to be done with less hardware and lower TCO Significantly reduces the overall time for a job by accelerating the checkpointing phase Faster simulation runs and higher ROI on the compute infrastructure SFX Instant Commit How it Works SFX Instant Commit accelerates read-after-write operations by copying new data into the SFX Tier, as it is written. This serves to warm-up cache and accelerates any subsequent reads for that same data. In traditional storage systems, when small block write operations come in, they are read into DRAM and mirrored to the secondary controller for redundancy. Once a copy has been created, the host is given an acknowledgement and then the data is scheduled to be redundantly written to rotating media. Anytime that same data is reread, it needs to be fetched from rotating media, which is relatively slow. With SFX Instant Commit, a copy of the data is also written to the SFX Read cache. This allows subsequent reads of the data to be served up very quickly, since it is already resident in the SFX Read Cache DataDirect Networks. All Rights Reserved. 6

7 Ideal workloads - This mode is designed to warm up cache as well as for small block read-after-write operations, such as when applications write data to a file, then immediately read the data back to do small modifications, then write the data again to storage. Sample Use Cases Workflow WorkflOw Challenge SFX instant commit Enables... CDN When the customer uploads new content into the CDN, it takes awhile for them to verify that the content has been uploaded successfully, and often initial performance is not good because content is not yet distributed and cached. Keeping a copy of the uploaded data in the SFX Tier, making the performance of content verification happen faster and the delivery performance better Increased customer satisfaction with upload process and CDN performance, in general Transcoding Oil and Gas In these workflows, a mezzanine file is uploaded and then immediately transcoded. Slow time-to-market for the assets being transcoded instantly reduces the assets value in most cases. In the processing stage in an Oil and Gas workflow, applications like Promax create a large number of small log files during different points in the workflow, e.g. Verification. These files then need to be read back with low latency, and bottlenecks here can slow the entire workflow. Immediate transcoding and processing Faster time-to-market and increased asset value Files that are created are stored in the faster SFX Tier and can be read back quickly Accelerating the verification phase SFX Context Commit SFX Context Commit is designed to make the cache application aware, minimizing the use of expensive Flash media to lower the TCO of hybrid systems. It integrates with applications and file systems by allowing a mechanism to send hints down to the storage controller to pre-fetch or pin critical data in the faster SFX tier, in order to accelerate application performance. These hints can be sent either in band or out of band. - In Band - The In Band mechanism allows hints to be sent down in real-time, embedded in the SCSI (which is a set of standards to transfer data between the application and storage) command block, on a per I/O basis. In Band works well for cases where an application requires the highest level of real-time control - and can send hints down during application file read and write operations. In addition, the In Band mechanism can be used to do some application transparent acceleration in open source file systems, such as EXT4 or XFS. Customers using such file systems can use SFX in Band Context Commit to send file system hints to differentiate file system metadata vs. user data. This greatly accelerates the file system, as the metadata remains in the SFX cache. - Out of Band The Out of Band mechanism uses the SFA (DDN storage system s) API, which is sent to the controller over Ethernet; providing hints on a static or semi-real-time basis. Out of Band works well for cases like databases, that have known areas of indexes and data structures on disk and can be statically allocated to SFX cache. It can also be used in cases where users want data pre-fetched into SFX. The hint mechanism sends down the priority level of either the data in the SCSI request for the In Band method, or can send down a list containing the LUN, LBA (Logical Block Addressing - a standard scheme for specifying the location of data blocks), length and priority for the Out of Band mechanism. More details are available in the SFX API Guide DataDirect Networks. All Rights Reserved. 7

8 Workflow WorkflOw Challenge SFX context commit Enables... Web Applications Broadcast CDN Genomics In hosting a web application, every millisecond counts. The end customer may click away if content does not load or takes too long due to a slow storage system. An end user's experience can be enhanced if available web acceleration technologies can be used to help indicate upcoming requests. Play-to-air broadcasts rely on flawless execution of filebased workflows. Any delay in a broadcast frame due to slow storage can lead to dead air. Similar to Web Applications, Content Delivery Networks are sensitive to latency and every millisecond counts. The end customer may click away if content does not load or takes too long due to a slow storage system. Most CDNs have the ability to preposition content in cache but not within their storage systems. Search applications like BLAST compare whole genomes of smaller datasets with a lot of small IO for search and compare. The compare is performed from a known quantity (template) vs sample with the known quantity always being used for every compare. Faster delivery of content to the end consumer with prepopulated internet content caching Significantly less hardware required to achieve the same performance Lower TCO Reduces latencies and increases performance as content is placed in SFX tier based on hints given by the playout system Supports error free playout with improved quality and consistency of service Improves delivery speeds and decreases delivery times Extends push all the way into the origin storage Increases revenue and the perceived value of using CDN origin vs. customer Pins the entire human genome or sequence being compared in cache Accelerates the BLAST application, as the template is always in memory and is not flushed back to disk Sample SFX API Use Case There are multiple workflows, such as Life Sciences, where the application requires constant access to a certain set of files in a particular directory. Such applications can get accelerated if these files are pinned in faster memory, so that they are served quickly - and not flushed down to rotating media. SFX Context Commit with APIs can be used to pin files and directories in cache, so when applications are accessing those files and directories, latency is significantly reduced. SFX API is flexible, as application performance requirements, users and SLAs vary DataDirect Networks. All Rights Reserved. 8

9 SFX Context Commit uses a middleware application from DDN. This middleware application works with SFX Out of Band APIs to send hints from applications to specify the location of hot files and directories. Once the hint of the hot files and directories is given to SFX, it pre-fetches those files and directories and puts them in the SFX Cache. Policybased priorities can be set so that the application receives the appropriate QoS. Once the files and directories are no longer needed, the priority of the file and directories can be reduced or ejected from cache, depending on the user s preference. The first implementation of this middleware will be for the GPFS and Lustre file systems. SFX and ReACT Flash Caching for Big Data Big Data workloads are extremely varied and unpredictable, requiring storage systems to deliver extreme levels of performance (in both streaming and random workloads). Traditional storage systems built with caching technologies are designed for small block random workloads - but get congested by mixed workload I/Os competing for cache. This results in application bottlenecks, when exposed to real world, mixed application workloads. ReACT is designed to accelerate applications by delivering the highest levels of performance for high-throughput streaming, as well as random or transactional workloads. It does this by analyzing the workload s data composition in real-time to: Write Through: Preventing cache congestion and eliminating write mirroring penalties during sequential I/O Mirror: Preserves precision cache during random, unaligned IOs to accelerate small block, random I/O operations When cache-mirroring is enabled on a pair of active-active SFA (DDN storage system) controllers, all data received to a storage controller is transferred to the cache of the second controller through the Inter Controller Link (ICL), before an acknowledgement is returned to the storage host. This mechanism is complimented by a battery-backed process, which enables systems to gracefully persist their cache in the event of a failure. In conjunction, the combination of cache mirroring and battery-backed power-loss protection ensures that data is persisted - in the event of a controller failure where the data has not yet been transferred to redundant media. When enough data is collected in the cache, it is scheduled to be written to redundant media. Large block bandwidth operations tend to fill the caches, slowing down operations as a result of the mirroring activity. ReACT overrides this setting. When high bandwidth write operations (full stripes) are written to the storage system, ReACT immediately recognizes these and schedules the data to be written to media as a full stripe write, which is faster because parity can be calculated on the fly. Since ReACT is immediately scheduling the write, and it is being done in a high bandwidth mode, the need to mirror the data to the alternate controller is eliminated. An acknowledgement is not returned until the data has safely been written on the redundant media. ReACT has the effect of allowing large block bandwidth data to stream directly to rotating media and bypass cache all while caching the small block I/O to improve performance. Overall, this increases the bandwidth of the controller by preventing the ICL from becoming a bottleneck DataDirect Networks. All Rights Reserved. 9

10 For example, SFX write works in conjunction with ReACT to optimize streaming performance without congesting cache. When a LUN is front-ended with SFX Write Cache, ReACT recognizes that this is a full stripe, high bandwidth operation and schedules the write operation to go directly to the SFX Write Cache, rather than to rotating media. ReACT operations are written to the SFX Write Cache redundantly and the acknowledgement is returned to the host system. In the background, after the bursty write operations have subsided, SFX Write Cache intelligently groups the data in the SFX Tier and begins highly optimized, streaming operations to the redundant rotating media, maximizing the rotating media rate. This has the effect of taking any bursty, high bandwidth write operations, and allowing the storage system to ingest them at full speed, eliminating the requirement to deploy hundreds of spinning drives behind the controllers to achieve full ingest rates. SFX vs. Alternate Flash Caching Solutions There are multiple flash vendors who offer full-flash and hybrid (flash+disk) solutions to accelerate high performance applications. However, it is important to recognize that the primary market for these vendors are databases, and other enterprise applications where the I/O profile is always small block. DDN s extensive experience in Big Data recognized that a fundamentally different approach to Flash Caching is required for Big Data workloads. The following table identifies how DDN s approach to Flash Caching is specialized and highly differentiated from other industry vendors: Market Focus Other vendors Designed for Enterprise IT e.g. Oracle, SAP, Databases for OLTP Workloads, MS Exchange DDN SFX Designed for Big Data e.g. Life Sciences, Rich Media, Financial Services, Oil & Gas Type of Workload Acceleration Modes Application Awareness Designed for small block I/O cache becomes congested and performance drops under a mix of real world streaming and random workloads. Typically these vendors only offer a Read Cache, and their Write Cache is a Write Back cache into DRAM, not SSDs. No Application awareness. Promotes/demotes data to flash based on best guess algorithms like Least Recently Used (LRU) schemes. DDN believes this is not an optimal use of expensive flash media and increases both acquisition costs and TCO. SFX offers industry leading IOPS & Bandwidth. It works in conjunction with ReACT to handle an extreme range of streaming and random workloads efficiently, with minimal performance impact DDN recognizes that Big Data needs a more comprehensive approach and has designed 4 modes that work for Reads, Writes, Read after Writes and Application Integration. These modes work in conjunction with ReACT to truly accelerate Big Data. SFX is application aware with an extensive hinting mechanism to promote/demote data to flash exactly when required. Customers can make the optimal use of expensive cache media and lower TCO DataDirect Networks. All Rights Reserved. 10

11 File System Integration SFX is designed to integrate with file systems and applications to truly lower the TCO for customer trying to tackle the challenges of Big Data. The following sections describe how SFX integrates with 3 DDN solutions GRIDScaler, EXAScaler and hscaler. GRIDScaler GRIDScaler is a massively scalable parallel file system and NAS appliance that utilizes GPFS as the underlying file system. It scales diagonally to any performance and capacity requirement, by leveraging both scale-out and scaleup concepts. With scalable data and metadata technology, GRIDScaler eliminates all bottlenecks to achieve true parallelism and maximize application performance. It can be configured to separate the metadata from the data and each can be stored on separate media. A file system s performance is typically determined by the performance of its metadata. In order to increase the performance of GRIDScaler, many customers use SSDs in 1+1 RAID 1 configuration to accelerate metadata performance. This increases the overall performance but results in a higher TCO since the usable space of their expensive flash media is only 50%. When GRIDScaler is configured with SFX, customers can get higher levels of performance at a much lower TCO. The two modes that work together to achieve this are: SFX Instant Commit and SFX Read. An SFX Flash Tier is configured for the GPFS metadata, and that SFX Tier is linked to a LUN configured from rotating media. The SFX Tier is configured with priority level 3, which means that metadata is never flushed out. Normal write operations to the file system are served by DRAM in the SFX Tier and Instant Commit keep a copy of the metadata in the SFX Tier, if flushed out from DRAM. Any subsequent read requests for that same data is always served from the SFX cache, instead of the rotating media. This combination of SFX Instant Commit and SFX Read allows DDN customers to get higher performance when compared to pure HDD implementations, and same performance at a lower acquisition costs and TCO, when compared to pure SSD implementations DataDirect Networks. All Rights Reserved. 11

12 Workloads that will get accelerated Workloads that get accelerated when SFX is configured with GPFS include: - Metadata reads like listing of files using commands ls al, find operations and stat operations that read metadata - GPFS ILM scan operations (mmapplypolicy) to find candidate files matching a particular rule. o GPFS mmapplypolicy deletes files or migrates file data between storage pools within a single file system, in accordance with policy rules - File-system backup operations, where files inodes are scanned for update times, modify times etc., prior to incremental backup - File-system maintenance operations like - o mmaddisk, mmrestripefs, which balance capacity across VD o mmfsck, for consistency check and repair o mmdefragfs, to defragment a file system and involves lots of metadata + data operations. Metadata in cache can accelerate the initial passes of the maintenance operation. EXAScaler EXAScaler is a massively scalable, high performance, open source file storage appliance based on the Lustre File System. Built by HPC experts, and supported by the world s most skilled parallel I/O team at DDN, the EXAScaler blueprint is known worldwide as the gold standard in HPC storage clustering and powers the largest number of Top500 Supercomputer Sites, worldwide. In a typical EXAScaler implementation, an external RAID system with 16 drives in a RAID 10 configuration, is used to store the metadata (MDT). In addition, the metadata content of the Object Storage Target (OST) is spread-out in the storage system and shared with actual data stored in rotating media. However, the rotating media, as well as the metadata server, can be a bottleneck in the overall performance of the EXAScaler systems. With SFX, the metadata content of both MDT and OST can be put in an SFX Tier to accelerate the file system. When EXAScaler is configured with SFX, the content of MDT and the metadata content of OST is separated from the actual data and stored separately. SFX Instant Commit and SFX Read modes work together to accelerate EXAScaler systems. Normal write operations to the file system are served by DRAM in the SFX Tier and Instant Commit keeps a copy of the metadata in the SFX Tier, if flushed out from DRAM. Any subsequent read for that same data is always served from the SFX cache instead of the rotating media, accelerating metadata operations. In a similar manner, the metadata content of the OSTs is accelerated by Instant Commit and SFX Read cache working in conjunction. This combination of SFX Instant Commit and SFX Read delivers significant performance advantages over traditional pure HDD or SSD implementations. It provides much greater performance at a significantly lower TCO. Workloads that get accelerated when SFX is configured with EXAScaler include metadata reads like listing of files using commands ls al and find operations DataDirect Networks. All Rights Reserved. 12

13 hscaler Big Data Analytics Appliance Integration hscaler is an Apache Hadoop appliance for Big Data, designed specifically for the enterprise to dramatically simplify apache Hadoop deployments. At the core is the SFA12K storage system, which eliminates bottlenecks for Hadoop compute nodes and provides a much denser, more efficient storage and processing architecture. This is combined with commodity processing, high-speed networking and a full Apache Hadoop distribution to deliver a factory configured, easy to use Hadoop appliance. The Apache job scheduler can be integrated with SFX Instant Commit, providing the ability to pre-fetch data into the SFX Read cache for an upcoming Hadoop job. Select data can be staged into the SFX cache for access by nodes. The SFX read cache extends the file system read ahead, allowing for faster access to data. SFX can also be used to store temporary data files produced by Hadoop Mappers and Reducers. This improves performance and allows the Hadoop job to complete faster. The Data is deleted from the cache on job completion. SFX can also be used with the HDFS file system, to trade host-local cache for network-cache, allowing applications to benefit from having additional cache to accelerate performance. HBase and other Hadoop processes can leverage SFX cache, which is cheaper than having local PCI based SSD cache in each node. This allows hscaler to accelerate Hadoop jobs and provide a higher ROI for Big Data analytics. Summary Organizations are under more and more pressure to accelerate the performance of their Big Data workflows, while cutting costs. Solid State drives have the potential to solve this problem. However, replacing all spinning media with solid state is expensive. Flash caching is becoming a cost effective way to deploy solid state storage. SFX is a flash caching technology designed for Big Data and extends the functionality of the storage system cache. This is done by selectively front-ending traditional rotating media with some amount of flash memory, accelerating application performance at a much lower acquisition cost and TCO. The SFX portfolio of flash acceleration tools includes SFX Read, SFX Write, SFX Instant Commit and SFX Context Commit, which are unique modes that help accelerate different I/O patterns. They work in conjunction with ReACT technology to analyze workloads in real-time and accelerate applications and file systems that require multi-dimensional storage performance. SFX accelerates Big Data workflows including File Systems like DDN s GRIDScaler, EXAScaler, and hscaler appliances at a much lower TCO DataDirect Networks. All Rights Reserved. 13

14 DDN About Us DataDirect Networks (DDN) is the world leader in massively scalable storage. Our data storage and processing solutions and professional services enable content-rich and high growth IT environments to achieve the highest levels of systems scalability, efficiency and simplicity. DDN enables enterprises to extract value and deliver business results from their information. Our customers include the world s leading online content and social networking providers, high performance cloud and grid computing, life sciences, media production, and security and intelligence organizations. Deployed in thousands of mission critical environments worldwide, DDN s solutions have been designed, engineered and proven in the world s most scalable data centers to ensure competitive business advantage for today s information powered enterprise. For more information, go to www. or call , DataDirect Networks, Inc. All Rights Reserved. DataDirect Networks, EXAScaler, GRIDScaler, hscaler, ReACT, SFA12K, SFA, SFX, and Storage Fusion Xcelerator are trademarks of DataDirect Networks. All other trademarks are the property of their respective owners. Version-3 8/ DataDirect Networks. All Rights Reserved. 14