Patent Publication Number: US-8538936-B2

Title: System and method for data compression using compression hardware

Description:
CROSS REFERENCE TO RELATED APPLICATIONS 
     This application is a continuation of U.S. patent application Ser. No. 12/130,737 filed on May 30, 2008, now U.S. Pat. No. 7,987,161, issued 26 Jul. 2011, which claims the benefit of and priority to U.S. Provisional Patent Application No. 60/957,602 filed on Aug. 23, 2007, the disclosures of which are hereby incorporated in their entireties by reference herein. 
    
    
     BACKGROUND 
     1. Field 
     The field of the invention relates generally to data compression and more particularly relates to a system and method for compressing financial data using data compression hardware. 
     2. Brief Discussion of Related Art 
     Lossless data compression methods in streaming database systems reduce storage requirements, improve access performance to stored data, and minimize use of computational resources to perform data compression and restoration. An often-unstated but intrinsically assumed goal for such streaming database systems is to provide uninterrupted access to compressed data. The conflicting nature of these goals, in a practical implementation of a streaming database system, generally results in compromised solutions that achieve gains toward one goal at the expense of another. Storage requirements may be reduced by transformations on the data, such as ordering data in columns or implementing record- or field-level data compression, but the cost is usually reduced performance for data access and increased computational requirements to perform the transformations. 
     Since lossless data compression is a computationally expensive operation, software compression solutions are not practical for high-performance database systems and are only adequate for database systems that do not have stringent performance requirements. Hardware accelerated data compression is one practical solution suitable for performance-hungry database systems. However, data compression hardware, as in any hardware resources, is subject to malfunction and requires a fail-safe mechanism to guarantee the integrity of and access to compressed data in the event of partial or total hardware malfunction. 
     Streaming database systems require random access to data, whether compressed or uncompressed. Any attempt to retrofit compression into an existing database, or to design compression into a newly constructed database, must provide a mechanism guaranteeing efficient random access to compressed data. Moreover, since not all data is compressible, both uncompressed and compressed data coexist in the database and the data access mechanism must be efficient for both types of data. 
     SUMMARY 
     According to one embodiment, a computer-implemented method of compressing data is disclosed. The method includes receiving a data set in a data stream. The data set includes a set of data descriptor fields. The data set is partitioned into one or more data subsets using the set of data descriptor fields. Using the set of data descriptor fields included, one or more tabular slices and an index are generated. The one or more tabular slice are identified by the index. The one or more tabular slices are compressed into a compressed data block by a data compression scheme using a hardware compressor. A compression data file is generated in a database. The compression data file has a header that stores information about the data compression scheme. The compressed data block is stored in the data file. 
     According to another embodiment, a computer-readable storage medium is disclosed. The computer-readable storage medium stores a plurality of instructions that, when executed by a computing system, cause the computing system to perform the following operations. The instructions cause the computing system to receive a data set in a data stream. The data set includes a set of data descriptor fields. The instructions cause the computing system to partition the data set into one or more data subsets using the set of data descriptor fields. The instructions cause the computing system to generate one or more tabular slices and an index for at least one of the data subsets using the set of data descriptor fields. The one or more tabular slices are identified by the index. The instructions cause the computing system to compress the one or more tabular slices into a compressed data block by a data compression scheme using a hardware compressor. The instructions cause the computing system to generate a compression data file in a database. The compression data file has a header that stores information about the data compression scheme. The instructions cause the computing system to store the compressed data block in the compression data file. 
     The above and other preferred features, including various novel details of implementation and combination of elements, will now be more particularly described with reference to the accompanying drawings. It will be understood that the particular methods and systems described herein are shown by way of illustration only and not as limitations. As will be understood by those skilled in the art, the principles and features described herein may be employed in various and numerous embodiments without departing from the scope of the invention. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       The accompanying drawings, which are included as part of the present specification, illustrate the presently preferred embodiment and together with the general description given above and the detailed description of the preferred embodiment given below serve to explain and teach the principles of the present embodiments. 
         FIG. 1  illustrates an exemplary block diagram of a data compression system for processing financial tick data streams, according to one embodiment; 
         FIG. 2A  shows exemplary financial tick data, according to one embodiment; 
         FIG. 2B  illustrates exemplary tabular slices, according to one embodiment; 
         FIG. 2C  illustrates exemplary U.S. equities tick data stream partitioned into three sets of stock tables, according to one embodiment; 
         FIG. 2D  illustrates exemplary U.S. equities tick data tables partitioned into five tabular slices, according to one embodiment; 
         FIG. 3  illustrates an exemplary mapping process for tick data and index files, according to one embodiment; 
         FIG. 4  illustrates exemplary index and data files, according to one embodiment; 
         FIG. 5  illustrates an exemplary process for reading and writing slice buffers from and to a persistent database, according to one embodiment; 
         FIG. 6  illustrates an exemplary block diagram of a data compression system integrating data compression hardware, according to one embodiment; 
         FIG. 7A  illustrates an exemplary process for constructing a compressed block from a slice buffer, according to one embodiment; 
         FIG. 7B  illustrates an exemplary process for constructing a compressed block from several slice buffers, according to one embodiment; 
         FIG. 8  illustrates an exemplary process for accessing both uncompressed and compressed data files, according to one embodiment; 
         FIG. 9  illustrates an exemplary process for accessing a compressed data file that stores compressed tabular slices in a compressed block format, according to one embodiment; 
         FIG. 10A  illustrates an exemplary process for adding a new tabular slice into an uncompressed data file, according to one embodiment; and 
         FIG. 10B  illustrates an exemplary process for adding a new tabular slice into a compressed data file, according to one embodiment. 
     
    
    
     It should be noted that the figures are not necessarily drawn to scale and that elements of similar structures or functions are generally represented by like reference numerals for illustrative purposes throughout the figures. It also should be noted that the figures are only intended to facilitate the description of the various embodiments described herein. The figures do not describe every aspect of the teachings disclosed herein and do not limit the scope of the claims. 
     DETAILED DESCRIPTION 
     A system and method for data compression using compression hardware is disclosed. Each of the features and teachings disclosed herein can be utilized separately or in conjunction with other features and teachings to provide a method and system for vision-based interaction in a virtual environment. Representative examples utilizing many of these additional features and teachings, both separately and in combination, are described in further detail with reference to the attached drawings. This detailed description is merely intended to teach a person of skill in the art further details for practicing preferred aspects of the present teachings and is not intended to limit the scope of the claims. Therefore, combinations of features disclosed in the following detailed description may not be necessary to practice the teachings in the broadest sense, and are instead taught merely to describe particularly representative examples of the present teachings. 
     In the following description, for the purposes of explanation, specific nomenclature is set forth to facilitate an understanding of the various inventive concepts disclosed herein. However, it will be apparent to one skilled in the art that these specific details are not required in order to practice the various inventive concepts disclosed herein. 
     The present invention also relates to an apparatus for performing the operations herein. This apparatus may be specially constructed for the required purposes, or it may comprise a general-purpose computer selectively activated or reconfigured by a computer program stored in the computer. Such a computer program may be stored in a computer-readable storage medium, such as, but is not limited to, any type of disk including floppy disks, optical disks, CD-ROMs, and magnetic-optical disks, read-only memories, random access memories, EPROMs, EEPROMs, magnetic or optical cards, or any type of media suitable for storing electronic instructions, and each coupled to a computer system bus. 
     The methods presented herein are not inherently related to any particular computer or other apparatus. Various general-purpose systems may be used with programs in accordance with the teachings herein, or it may prove convenient to construct more specialized apparatus to perform the required method steps. The required structure for a variety of these systems will appear from the description below. In addition, the present invention is not described with reference to any particular programming language. It will be appreciated that a variety of programming languages may be used to implement the teachings of the invention as described herein. 
     Moreover, the various features of the representative examples and the dependent claims may be combined in ways that are not specifically and explicitly enumerated in order to provide additional useful embodiments of the present teachings. It is also expressly noted that all value ranges or indications of groups of entities disclose every possible intermediate value or intermediate entity for the purpose of original disclosure, as well as for the purpose of restricting the claimed subject matter. It is also expressly noted that the dimensions and the shapes of the components shown in the figures are designed to help to understand how the present teachings are practiced, but not intended to limit the dimensions and the shapes shown in the examples. 
       FIG. 1  illustrates an exemplary block diagram of a data compression system  100  for processing financial tick data streams, according to one embodiment. Database server  101  runs database software  102  and connects to database  110  to capture, store and retrieve ticks in a financial tick data stream. Financial tick data includes data records for equities, options, bars and other configurable financial instruments. Financial data may be dynamically derived from computational combinations of ticks in the tick data stream. Derived ticks are dynamically constructed in real time. Each tick is a data record associated with a uniquely named financial instrument having one or more data fields, such as price, volume, exchange, etc. Each tick has a record type and an associated format that is either predefined in database  110  or defined in a configurable record schema file. Each tick also has an associated time that identifies its precise location in the tick data stream. 
       FIG. 2A  shows exemplary financial tick data, according to one embodiment. A tick data stream is a time-ordered series of ticks representing information regarding financial instruments, such as price, volume, etc. A tick contains a set of descriptor fields and a variable set of configurable or predefined data fields. Ticks in a tick data stream are partitioned into and uniquely stored in mutually exclusive tables such that the union of all ticks in the tables constitutes the tick data stream. The descriptor fields used to partition ticks into a set of tables are symbol  201 , record type  202  and time stamp  203 . The data field  204  includes price, volume and exchange identification. It is noted that other descriptor fields and data fields may be used. 
       FIG. 2C  illustrates exemplary U.S. equities tick data partitioned into three sets of stock tables, according to one embodiment. All trade ticks received in the tick data stream for the symbol MSFT are stored in a time order sequence in the table for MSFT trades, and similarly for CSCO and GOOG.  FIGS. 2C and 2D  show that each tick has a fixed number of columns; however, the present method and system supports ticks that have a variable number of columns. For convenience, the time stamps are explicitly shown (e.g., time−1, time−2) while the configurable fields are shown as “Cx”, where x is a column number. Configurable fields are processed as opaque binary data by a compression engine in database software  102 . 
     According to one embodiment, database software  102  runs as an application on a standard Windows Server platform. Database software  102  supports input interface  123  for an incoming tick data stream, output interface  122  for outgoing ticks to satisfy client queries and database  110  interface  124  to read/write data from/to database  110 . Different operating systems and server platforms may be used without deviating from the scope of the present subject matter. 
     Database software  102  receives streaming financial tick data from an incoming tick data stream. Database software  102  processes and stores tick data in a tabular form in persistent database  110 . When a client queries a tick data, database software  102  retrieves the tick data from persistent database  110  and presents it to the client. According to one embodiment, database software  102  maintains recently accessed tick data in an internal soft-cache  121  to improve retrieval performance for frequently accessed tick data. 
     Database software  102  further partitions each tick data table into a set of tabular slices and stores the set in database  110  while the tick data stream is received. In one embodiment, tabular slices are constructed to be mutually exclusive, index addressable and non-overlapping. 
     According to one embodiment, tick data is bulk-loaded by database software  102  in the same manner as used for processing tick data streams in real time. All tick data may be stored in the same format in database  110  regardless of the input data format, and database software  102  employs a unified method to retrieve tick data from database  1   10 , whether the tick data was bulk-loaded or inputted from tick data streams. 
     According to one embodiment, a key is computed from the descriptor fields of the ticks stored in a tabular slice. The key is added to an index used for rapid random access to ticks in a tabular data set. A data portion of the key contains the address of the tabular slice in database  110  and is used to locate the tabular slice for query and update access.  FIG. 2D  illustrates exemplary U.S. equities tick data tables partitioned into five tabular slices, according to one embodiment; tick data for MSFT and CSCO are partitioned into two slices each and tick data for GOOG is partitioned into a single tabular slice. 
       FIG. 2B  illustrates exemplary tabular slices, according to one embodiment. Each tabular slice is consolidated into a buffer stored in contiguous memory locations and referred to as a slice buffer. Each slice buffer is index addressable. The address of each slice buffer is shown as “uTSn”, where n is a slice number. Five keys are created in a B-Tree index—one for each of the five tabular slices—and each key contains the address of the tabular slice buffer in database  110 . 
     Tabular data may be partitioned into tabular slices in any number and in many ways. Each tabular slice is index addressable and non-overlapping with other tabular slices, and the union of the tabular slices forms a set of tabular data. Each row in a tabular slice may contain a variable number of columns and different descriptor fields. A tabular slice is stored in a slice buffer in a contiguous sequence. 
     Streaming data is received and added into memory using the descriptor fields in the time series. For each unique combination of descriptor fields, separate tabular slices are created. In database  110 , partitioning of tabular data into tabular slices is accomplished by a dynamic algorithm. When a tabular slice for a particular table already exists in database  110  and the tabular slice has reached its size, input to the tabular slice is closed and the next received tick data is stored in a new tabular slice with a new access key created in reference to the descriptor fields with a new time stamp. In this manner, a set of non-overlapping tabular slices is created for all the ticks in a tick data stream and the union of the tabular slices is used to reconstruct a complete set of tabular data in the tick data stream. 
     Due to the dynamic nature of streaming financial tick data, a tabular slice is created and closed based on the number of ticks stored in the tabular slice. For example, the three ticks: MSFT.trade.9:00 AM, CSCO.quote.9:00 AM and GOOG.trade.9:00 AM are received at 9:00 AM, and three tabular slices: MSFT.trade, CSCO.quote and GOOG.trade are created. The next tick at 9:01 AM is received: MSFT.trade.9:01 AM, MSFT.trade.9:01 AM and CSCO.quote.9:01 AM. The first two MSFT.trade ticks are added as rows  2  and  3  in the MSFT.trade tabular slice created earlier, and the CSCO.quote is added in row  2  in the CSCO.quote tabular slice. As more tick data arrive, database  110  monitors the number of tabular slices and the size of each tabular slice. When a new tick data for an existing tabular slice arrives, database  110  determines whether or not the tabular slice reached its size. For instance, tabular slices in  FIG. 2D  contain two, three and five ticks. Depending on the size of tick data, I/O bus speed and other system specifications affecting query performance, the size of tick slices varies. When the tabular slice has reached its size, database software  102  determines to close input to the tabular slice and stores the tabular slice from soft-cache  121  to a data file in database  110 . When the next tick data arrives, a new tabular slice is created in soft-cache  121  and the new tick data is stored as the first row in the new tabular slice. 
     Tabular slices may vary in size and several criteria may be used to determine the size of a tabular slice, according to one embodiment. For example, the streaming rate of tick data determines the size of tabular slices. Alternatively, the amount of storage required to hold the tabular, historical information regarding the expected size for tabular slices, or historical information regarding the most efficient size of tabular slices for query access, may be used to determine the size of tabular slices. The size of each tabular slice derived from the tick data stream may be different from the other tabular slices derived from the same stream, and the size for tabular slices for each instance is set to yield the best query performance. 
     According to one embodiment, each tick data in a tabular slice is transformed or compressed before the tabular slice is transformed into a slice buffer. In a preferred embodiment, each tick data in a tabular slice is transformed into an opaque binary object, often referred to as a “blob.” For example, null fields are removed from the tick data during the transformation of the tick data into a blob. Other field-level transformations may be performed to replace large field values with smaller encoded values to compact the size of the tick data. Since this record- or field-level transformation of tick data is performed prior to data compression of tabular slices into slice buffers, the data compression is transformation independent from any record- or field-level transformations applied to the tick data. 
     According to one embodiment, database  110  is implemented as a set of data files in the Windows file system. A user-configurable mapping algorithm, such as “round-robin by symbol” or “symbol directed,” directs the storage of slice buffers and index keys into data files. Tabular slices are stored in data files and the associated keys are stored in B-Trees that are mapped into the Windows file system. 
     In one embodiment, when database  110  builds data files using a software utility to bulk-load tick data, the software utility creates one data file and one B-Tree file and maps all the tabular slices into the one data file and all the associated keys into the one B-Tree file. In another embodiment, the number of data files is configured from a database configuration file that stores a list of data file names and a user-configurable set of instructions. By this “symbol directed” mapping, all the tabular slices for the symbol MSFT, CSCO and GOOG are stored in “file1.dat,” “file2.dat” and “file3.dat,” respectively, as shown in  FIG. 3 . 
     In the event that a symbol mapping direction is not provided, database software  102  writes all the tabular slices for one symbol to one of the configured data files. Database  110  selects which data file to use for a symbol on a “round-robin” basis, and once a data file is selected for a symbol, all the tabular slices for that particular symbol are stored in the data file. This mapping algorithm is referred to as “round-robin by symbol” mapping. 
     The inputs to the data mapping algorithm include a set of file paths and optional user-configurable mapping instructions. Tabular slices and the associated indices are mapped to data files configured by symbol directions in the user-configurable mapping instructions. According to one embodiment, “round-robin by symbol” is used. If only one file path path is configured, all tabular slices are stored in a data file with the name “path.dat,” and all keys are stored in an index file, “path.idx.” If multiple file paths are configured and no user-configurable mapping instruction is provided, the symbols are distributed among the files according to the “round-robin” algorithm, resulting in an even distribution of symbols to data files. If user-configurable mapping instructions are provided, all symbols are distributed by the symbol directions and the remaining symbols are mapped using the “round-robin” algorithm. 
       FIG. 3  illustrates an exemplary mapping process for tick data and index files, according to one embodiment. The three tick data sets are mapped into three data and index files distributed across three data files in database  110 . This mapping method provides superior I/O performance for parallel access to the three tables since each access traverses a dedicated I/O path. Although  FIG. 3  illustrates only three tabular data sets, database  110  can handle millions of tabular data sets and symbols. 
       FIG. 4  illustrates exemplary index and data files, according to one embodiment. Data file, “file.dat,” contains a set of uncompressed index-addressable tabular slices (uTS 1 , uTS 2 , . . . , uTSn) and is associated with an index file, “file.idx.” Each of the slice buffers for the tabular slices, uTSx, are stored in a Windows file at random locations. For example, uTS 1  is stored in the file, followed by uTS 4 , followed by uTS 5 , followed by uTS 2 . Access to the uncompressed tabular slices requires an index lookup by using a key, including a symbol, record type and time, etc., to identify the location of a tabular slice in the data file. The key “MSFT.trade.time−1” is used to search the B-Tree index to identify the location for the tabular slice uTS 1 . The use of the B-Tree index provides rapid random access to the uncompressed tabular slices stored in database  110 . 
     According to one embodiment, database software  102  implements an internal soft-cache  121  to encapsulate access to tick data. Soft-cache  121  efficiently manages the transfer of tabular slice buffers to and from database  1   10 , synchronizes update/query access to slice buffers and improves retrieval performance for frequently accessed tick data. Slice buffers are written into soft-cache  121  after being created, and the associated key is written directly to the B-Tree index in the index file (.idx). According to one embodiment, soft-cache  121  provides access to the slice buffers using a least-recently-used cache replacement policy and flushes newly created slice buffers to database  110  in an appropriate data file (.dat). 
     According to one embodiment, each tabular slice is index addressable, and a tick data within a tabular slice can be located using an index file that is implemented in a B-Tree format when the tabular slice was created and stored in database  110 . For example, a client query requests tick data for “MSFT.trade” in the time range from “time−1” to “time−4” by:
 
“query/symbol MSFT/recordtype trades/start time−1/end time−4.”
 
A key is created using the values in the query to locate the data files that contain the tabular slices that satisfy the query condition:
 
key=&lt;MSFT, trades, time−1, time−4&gt;.
 
Using the key, database software  102  finds the locations of the tabular slices in database  110  that contain tick data for MSFT.trade in the requested time range from “time−1” to “time−4.” In the present example, the two tabular slices containing tick data for MSFT.trade from “time−1” to “time−4” are uTS 1  and uTS 2 .
 
       FIG. 5  illustrates an exemplary process for reading and writing slice buffers from and to database  1   10 , according to one embodiment. Tick data are received in a tick data stream by input interface  123 . A slice buffer  501  and its associated key  502  are created from the received tick data in the memory. The slice buffer  501  is written into soft-cache  121  and the key  502  is written into the B-Tree index, “file1.idx.” Soft-cache  121  writes the newly created tabular slice buffer to the data file, “file1.dat.” Soft-cache  121  retains the slice buffer  501  in cache to facilitate rapid access. 
     A client&#39;s query for reading a tick data starts with constructing a key. Using the key, database software  102  accesses the index file, file1.idx, to identify the location of the slice buffer containing the desired tick data. If the slice buffer is not available in soft-cache  121 , it is read from a data file, file1.dat, in database  110  and copied into soft-cache  121  to grant the query client an access to the slice buffer via output interface  122 . Database software  102  may attempt an update access to the queried slice buffer in order to apply a correction to tick data contained in the tabular slice. Subsequent query access attempts may be held in a pending state until the outstanding query access completes. 
     Database software  102  accesses an index file to identify the location for a desired slice buffer, and reads the slice buffer into soft-cache  121  if it is not available in soft-cache  121 . According to one embodiment, database software  102  is a multi-threaded database software that maps execution of threads onto multiple cores of database server  101 &#39;s platform. Two database software code threads may concurrently attempt to access a slice buffer in soft-cache  121 . Database software  102  synchronizes access from multiple threads so that update and query operations are serialized. 
     According to one embodiment, the present method and system integrates compression hardware into a database engine for streaming financial data.  FIG. 6  illustrates an exemplary block diagram of a data compression system integrating data compression hardware, according to one embodiment. One or more data compression cards  620  are integrated with database server  101  via the PCIe bus, and each hardware card  620  contains one or more compression/decompression engines. A compression cache  601  is used as a container for data in transition between compressed and uncompressed forms. 
     Database software  102  enables a pool of pre-configured software compression and decompression engines and applies an appropriate compression and decompression engine to store and retrieve tick data. According to one embodiment, two MX4e compression cards by Indra Networks are installed and each of the MX4e cards have multiple compression engines. Database  110  may be hosted on multiple hard disk drives for redundant storage. A compression library  602  may be provided by database software  102  or other database software that database server  101  uses. According to one embodiment, compression cache  601  is implemented as an extension of soft-cache  121  and positioned so as to shield compression operations from soft-cache  121  to achieve compression transparency. 
     According to one embodiment, data compression is selectively applied to slice buffers based on the compression type of the data file that contains the slice buffers. Tick data tables may be selectively mapped to data files in database  110 . Data files may be individually configured as either compressed or uncompressed files. If a data file is configured as a compressed data file, compression is automatically applied to all the slice buffers in the data file. Slice buffers are compressed before being written to the data file and are uncompressed after being read from the data file. These compression functions are encapsulated in software compression cache  601  internal to database software  102  in order to be transparent to other database operations. 
     For example, data compression system  100  is installed in a system that already has uncompressed data files. All the existing data files may be compressed using an off-line software utility supplied by the database system. Alternatively, some existing data files are compressed while some are left uncompressed. In the latter case, when database software  102  accesses a data file in database  110 , a decision is made based on the compression attribute stored in the metadata for the data file whether or not to apply data decompression to the tabular slices stored in the data file. 
     Compression type of each data file is user configurable and specified when a data file is created. The compression type becomes an attribute of the data file and is used to select a compressor or decompressor from either the pool of hardware compression engines installed in database server  101  or from the pool of software compression engines accessible by database software  102 . According to one embodiment, a data file is created by an off-line software utility, with which a user specifies the name for the data file and its associated compression type. According to another embodiment, an instruction to create data files is provided in an initialization file. The instruction may include a list of data files and the compression type to use for the data files in the list. 
     Database server  101  supports different compression types concurrently. According to one embodiment, one data file is configured with a compression type specified for a hardware compression card while another data file is configured with a compression type specified for a software compressor embedded in the database software  102 , and a third data file is uncompressed. These data files are concurrently accessible and an appropriate compressor/decompressor engine is selected for each data file as needed to process slice buffers that are transferred to/from a particular data file. The compression type of a data file is read by database software  102  when the data file is opened for access and is used to control access to the tabular slice buffers stored therein. 
     According to one embodiment, database software  102  provides utilities to convert a data file to another data file with a different compression type. Compression type conversion is particularly useful in a disaster recovery scenario following a hardware malfunction when replacement hardware cards are not available. In one example, a data file is converted from a hardware compressed format to an uncompressed format. In another example, a data file is converted from an uncompressed format to one of the supported hardware compression formats. This conversion is particularly useful when upgrading an existing database  110  to support hardware compression. In yet another example, all or part of the uncompressed data files may be converted to a supported compression type before loading it into a data compression system. Compression type conversion might also be useful for migrating from one database system to another. In this case, database software  102  accesses each loaded data file in the existing database system and the access results are used to plan a data migration strategy for conversion to another database system. 
     Data compression hardware is subject to various types of malfunction. Detectable hardware failures are component failures, bus arbitration errors and any other hardware related failures. Detectable software failures include invalid request parameters, buffer size errors, invalid checksum in compressed data and any other software-related failures. Silent data compressor malfunctions cause corruption of compressed data without a reported failure. To detect failures caused by compressor malfunction, compressor verification logic runs the corrupted compressed data through a decompressor and optionally compares the uncompressed data with the data originally processed by the compressor. When the restoration of the corrupted data is successfully performed, the operation on the data continues. When the corrupted data cannot be successfully restored by the compressor verification logic, database software  102  notifies such failure and, depending on the failure type, a proper action is taken. For example, in the event of a detectable hardware failure, database software  102  avoids using the failed hardware for data compression and notifies the user for such hardware failure. 
     The present method and system recovers from hardware compression failures and continues data operation without performance degradation in the event of partial or total hardware malfunction. Hardware compression cards and the associated software drivers detect and report most hardware failures. Some hardware compression cards provide compressor verification logic to detect silent compressor malfunctions and report the error to the user. For a hardware compressor card that is not equipped with an internal compressor verification logic, software compressor verification logic performs similar functions as the internal compressor verification logic and detects hardware failures and malfunctions. 
     When a hardware malfunction is detected, client requests are redirected by request redirection logic that redirects client requests by configurable compression failover policies, such as “abort-on-error,” “failover to alternate hardware engine,” “failover to compressor bypass,” “failover to software decompress,” etc. 
     “Abort-on-error” is a redirection policy to abort operations associated with data compression/decompression that is used when a hardware malfunction is detected. In the event of a partial hardware malfunction, the “failover to alternate hardware engine” policy may be used to redirect client requests from the failed hardware compression engine to a functional hardware compression engine. According to one embodiment, a hardware compression card driver automatically redirects failed requests to its functioning internal compression engines, in which case database software  102  simply issues incoming requests to the hardware compression card without having a burden of redirecting requests in the event of hardware failure. When the hardware compression card driver does not automatically redirect failed requests based on the status of the hardware, database software  102  may interfere and redirect the failed requests to a functional compression card. 
     In the event of a total hardware malfunction, the “failover to compressor bypass” policy may be used to bypass data compression so that a slice buffer is written to a data file in an uncompressed form. This allows real-time operation to continue without a performance penalty. Metadata is written to the data file with the slice buffer to identify the format of the data. Database software  102  or one of its batch utilities automatically bypasses decompression when the slice buffer is read. 
     In another event of a total hardware malfunction, the “failover to software decompress” policy may be used. Database software  102  applies decompression to a compressed slice buffer using a compatible software decompressor. This is a non-configurable fail-safe feature that allows real-time operation or disaster recovery to continue in the event of a total hardware failure. 
     According to one embodiment, compressed slice buffers are stored in database  110  in a format referred to as a compression block. Database software  102  combines one or more uncompressed tabular slices in memory buffers in database server  101  and compresses the data from the memory buffer into CBLOCK  703 . CBLOCK  703  is padded with null characters to force the size of the CBLOCK  703  to be a multiple of the sector size of the file system of database  110 . The null padding referred to as a “slack space” allows efficient use of the I/O bandwidth of database  110 . 
     According to one embodiment, multiple uncompressed tabular slices are combined into a CBLOCK  703  and data compression is applied to the content of the CBLOCK  703  independent of tabular slice boundaries. Since the size of tabular slices varies depending on the dynamics of the incoming tick data stream, data compression with varying sizes for each tabular slice may result in a poor compression rate. This is especially true when the size of slice buffers is very small (e.g., 512 bytes). In this case, in order to achieve good compression rates, data compression is applied to a collection of slice buffers rather than to individual slice buffers. The size of the data to which data compression is applied is stored in the compressed data file as an attribute. Once the compression size is set, it is applied to all CBLOCKs in a data file. 
       FIG. 7A  illustrates an exemplary process for constructing a compressed block from a slice buffer, according to one embodiment. A 128K byte uncompressed tabular slice buffer  701  stored in a memory buffer is compressed by compressor  702  in the increment of 32K bytes. Memory buffers of database server  101  or compression hardware  620  may be used for temporary storage of tabular slice buffer  701 . Each compressed data block  703   a  is contiguously stored in a compressed block  703 . If the size of the compressed block  703  is bigger than the total size of compressed data blocks  703   a , the compressed block  703  is filled with slack space  703   b . The size of CBLOCK  703  is smaller than the uncompressed slice buffer  701  and the data compression rate is primarily determined by the compressor  702  and the data contained in the slice buffer  701 . 
       FIG. 7B  illustrates an exemplary process for constructing a compressed block from several slice buffers, according to one embodiment. Uncompressed slice buffers  701 , with a size of 512 bytes, are stored in a memory buffer. The data in the memory buffer is compressed by compressor  702  in increments of 32K bytes and contiguously stored in a compressed block  703   a . When the last part of the memory buffer is less than 32K bytes after it is run through the compressor, metadata is stored in the CBLOCK  703  to identify the size of the last part of the memory buffer so that when the CBLOCK  703  is decompressed, the expected size of the uncompressed data for the memory buffer is known to the decompressor. The other parts of the CBLOCK  703  may be run through the decompressor without referring to the metadata to get the expected uncompressed size of the data since they are compressed with a fixed compression size of 32K bytes. 
       FIG. 8  illustrates an exemplary process for accessing both uncompressed and compressed data files, according to one embodiment. The mixed usage of compressed and uncompressed formats is transparent to users and other non-compression software in the database, and both compressed and uncompressed files are concurrently accessed without performance degradation. The uncompressed data file, ufile.dat, is accessed from soft-cache  121  while the compressed data file, cfile.dat, is accessed from compression cache  601  and then from soft-cache  121  after it is decompressed, so that only the access to the compressed data file burdens compression cache  601 . The subsequent operations on the data in soft-cache  121  are transparent, whether the stored data was compressed or uncompressed. 
     Uncompressed data file, ufile.dat, contains five slice buffers (uTS 1 -uTS 5 ). The five slice buffers are stored in contiguous locations. The associated B-Tree index file, file.idx, contains keys and the locations for each of the five uncompressed tabular slices. The compressed data file, cfile.dat, also contains five compressed slice buffers (cTS 1 -cTS 5 ), since there is a one-to-one correspondence between uncompressed and compressed tabular slices. The interface between database server  101  and the uncompressed data file is through soft-cache  121  and the B-Tree index file, while the interface between database server  101  and the compressed data file is through compression cache  601 . 
     The storage space for the compressed tabular slices is smaller in comparison with the storage space for the uncompressed tabular slices. Since the locations of the compressed tabular slices are different from those of the uncompressed ones, the tick data in the compressed data file cannot be located with the B-Tree index. As a result, a translation map is needed to map the location of the uncompressed tabular slice contained in the index to the location of the compressed tabular slice stored in the compressed data file. Using the translation map, database server  101  provides rapid random access to the compressed data and an efficient mechanism for insert operations. 
     Database server  101  stores compressed tabular slices in a CBLOCK format to store a large number of compressed tabular slices in a relatively small number of CBLOCKs. The method of storing multiple compressed tabular slices in a CBLOCK as the unit of transfer between memory and database  110  is advantageous over other storage methods. First, the number of CBLOCKs is considerably less than the number of uncompressed tabular slices in a table that reduces the number of keys required in the compression translation map. The size of a CBLOCK is generally larger than the compressed size of a tabular slice, thus the data transfer in between the system memory and database  110  becomes efficient, especially for intelligent storage systems such as Storage Area Networks (SAN) or Network Attached Storage (NAS). Slack space, unused space appended to a CBLOCK to accommodate in-place expansion, may be amortized over multiple compressed tabular slices to increase space efficiency and improve random update performance. 
       FIG. 9  illustrates an exemplary process for accessing a compressed data file that stores compressed tabular slices in a compressed block format, according to one embodiment. The B-Tree index is loaded in the same manner as it would be to access an uncompressed data file. Compressed data file, cfile.dat, is loaded and connected with compression cache  601  via compression translation map, “file.map.” 
     Database software  102  accesses the B-Tree index using an application query key  502  to identify the location of a desired uncompressed tabular slice. The location of the desired uncompressed tabular slice is presented to soft-cache  121 . Soft-cache  121  identifies the associated compressed data file by the compression type obtained from the file header when the data file was loaded. Soft-cache  121  then passes the request for the uncompressed data to compression cache  601 . 
     Compression cache  601  accesses file.map to identify the location and size of the compressed CBLOCK that contains one or more tabular slices. After the CBLOCK that contains the queried tabular slice is decompressed, the location of the desired uncompressed tabular slice is found using the location information with respect to the first tabular slice in the CBLOCK. Since the query key  502  contains the uncompressed location of the first tabular slice in the CBLOCK and all the tabular slices in the CBLOCK are contiguous in the uncompressed data file, a simple many-to-one mapping is performed to restore any uncompressed tabular slice in the CBLOCK. 
     This many-to-one mapping is advantageous for accessing compressed data over the one-to-one mapping for accessing uncompressed data or compressed data formatted by a variable size.  FIG. 8  shows an uncompressed data file, ufile.dat, that contains five uncompressed tabular slices, namely uTS 1 , uTS 4 , uTS 5 , uTS 2  and uTS 3 . The corresponding B-Tree index file, file.idx, contains five entries, one for each of the uncompressed tabular slices. Similarly, the compressed data file, cfile.dat, contains five compressed tabular slices, cTS 1 , cTS 4 , cTS 5 , cTS 2  and cTS 3 . Each compressed tabular slice is index addressable by the index file. The size of the compressed data file is reduced by compressing data; however, the number of index-addressable entries remain unchanged. 
     When a data compression block is used (e.g., CBLOCK), the number of index-addressable entries may be reduced so that many-to-one mapping is possible.  FIG. 9  shows two index-addressable compressed blocks, CBLOCK 1  and CBLOCK 2 . The translation map, file.map, locates only the two index-addressable compressed blocks and the locations of all the five entries are obtained by referring to the B-Tree index file that stores information regarding the sequence of the entries and the size of each entry. 
     According to one embodiment, a compressed data stored in the compressed data file, cfile.dat, is accessed in the following steps. First, the location of the uncompressed tabular slice is looked up in the B-Tree index as if it were stored in an uncompressed data file, ufile.dat. Next, this location of the uncompressed data is translated to the location of the CBLOCK  703  that contains the compressed form of the tabular slice in the compressed data file, cfile.dat. The compressed data block CBLOCK  703  is read from cfile.dat and decompressed. The decompressed CBLOCK  703  is delivered to soft-cache  121  for the access. This way, rapid-random access to a compressed data is possible because of the many-to-one mapping and the subsequent reduction in the number of index-addressable entries in the translation map, file.map. 
     Compression cache  601  accesses the compressed data file, cfile.dat, to read the CBLOCK into compression cache  601 . Compression cache  601  locates the desired compressed tabular slice in the CBLOCK, decompresses the data and loads the uncompressed tabular slice into soft-cache  121 . The number of CBLOCKs is generally less than the number of uncompressed tabular slices, and the size of the corresponding translation map is quite small, thus efficient and rapid access to the compressed data is achieved. Moreover, since the translation map is implemented using a B-Tree indexing, efficient random access is supported for both query and insert operations. 
     An access to a compressed tabular slice in a compressed data file starts with a query to the B-Tree index to obtain the location of the uncompressed tabular slice in the uncompressed data file, ufile.dat. It is noted that the location of the tabular slice in the uncompressed data file is a logical representation and does not refer to the physical location of the tabular slice in the compressed data file. After the compressed data file is decompressed, the location of the uncompressed tabular slice is used to access the tabular slice. For example, the location of the uncompressed tabular slice, “GOOG.trade.time−1,” is obtained from file.idx, and the location of the corresponding uncompressed tabular slice, uTS 5 , is identified. Although the location of uTS 5  does not refer to the physical location of the queried tabular slice, it is used as the key into the translation map, file.map, to determine the location of the CBLOCK 2  that contains cTS 5 , which is the compressed data of uTS 5 . After CBLOCK 2  is decompressed, the location of uTS 5  obtained earlier is used to access the queried tabular slice uTS 5 . 
     According to one embodiment, the present method and system creates a new uncompressed tabular slice from data in an incoming tick data stream and allocates a space for the uncompressed tabular slice at the end of an uncompressed data file, even though the uncompressed data file does not exist. When a newly created tabular slice is added into a compressed data file, the location of the uncompressed tabular slice is determined by the relative location of the tabular slice in the uncompressed data file.  FIG. 10A  illustrates an exemplary process for adding a new tabular slice into an uncompressed data file, according to one embodiment. A new MSFT.trade at time−6 is added at the end of the existing tabular slices and an entry is added into the associated B-Tree index for “MSFT.trade.time−6” that contains the location of the tabular slice uTS 6 . 
     Database software  102  accesses the B-Tree index to add a query key that contains the location of the new uncompressed tabular slice in the uncompressed data file. The location is presented to soft-cache  121  along with the data in the uncompressed tabular slice in the same manner as used to access uncompressed data. Soft-cache  121  combines the uncompressed tabular slice with other uncompressed tabular slices that are contiguous in the uncompressed data file and passes a request to write the combined uncompressed tabular slices to compression cache  601 . If the associated data file is not compressed, soft-cache  121  writes the combined uncompressed tabular slices directly to the data file without running through compression cache  601 . 
       FIG. 10B  illustrates an exemplary process for adding a new tabular slice into a compressed data file, according to one embodiment. The new tabular slice uTS 6  is compressed to cTS 6  and a new compressed block, CBLOCK 3 , is added into the compressed data file, cfile.dat. The location of the tabular slice uTS 6  is used to create an entry in the B-Tree index file.idx. It is also used to create an entry in the translation map, file.map, to map the uncompressed location of uTS 6  in the uncompressed data file. The uncompressed data file may not physically exist, but the location of the uTS 6  is used as the linkage between the user key “MSFT.trade.time−6” and the location of the compressed block CBLOCK 3 . 
     According to one embodiment, soft-cache  121  identifies a compressed data file by the compression type read from the header of the data file and passes the request to write the uncompressed tabular slice to compression cache  601 . Compression cache  601  selects a hardware compressor, compresses the uncompressed data in the CBLOCK, stores the metadata in the CBLOCK, updates the translation map for the new CBLOCK and writes the CBLOCK into the compressed data file. 
     According to one embodiment, a CBLOCK includes metadata for the component compressed tabular slices. This metadata is maintained in the CBLOCK and is available when performing operations on the CBLOCK. For example, metadata includes the size of each compressed tabular slice, the location of each compressed tabular slice in the uncompressed table, the size of the corresponding uncompressed tabular slice and an indicator that identifies tabular slices that are stored in the CBLOCK in uncompressed form. 
     According to one embodiment, an uncompressed tabular slice is stored in a CBLOCK in an uncompressed form. since slice buffers are opaque binary objects, the tabular slice contained therein can be in either compressed or uncompressed form. The present method and system is capable of handling tabular slices that are not compressible because of the nature of the data or because of compressor malfunction without special handling. If the metadata in the CBLOCK identifies a tabular slice containing uncompressed data, the data is transferred directly from the CBLOCK to soft-cache  121 . If the metadata in the CBLOCK identifies a tabular slice containing compressed data, compression cache  601  selects a compatible decompressor from the available pool and decompresses the data before transferring to soft-cache  121 . 
     According to one embodiment, all tabular slices are accessed by database  110  in uncompressed form in soft-cache  121 . The integrated compression engine guarantees to transfer uncompressed tabular slices in soft-cache  121  to the corresponding data file, regardless of whether or not the data file is compressed. For uncompressed data files, uncompressed slice buffers are accessed and updated directly to the corresponding uncompressed data file. For compressed data files, uncompressed slice buffers are processed by compression cache  601  and are accessed and updated to the corresponding compressed data file. In this manner, data compression hardware can be transparently retrofitted to a software-based data compression system and all its client applications. 
     According to one embodiment, the present method and system utilizes thread technology to fully exploit the parallelism inherent in the hardware. For example, database software  102  utilizes multiple threads to exploit the parallelism inherent in the multi-core technology of Windows Server platforms and this compression method leverages and expands the use of threads in accesses to data compression hardware cards. 
     According to one embodiment, database software  102  utilizes multiple compression threads to maximize throughput by taking full advantage of the compression engine load balancing provided in the compression hardware. Multiple concurrent threads are used to decompress data in a CBLOCK. Each thread decompresses a subset of the data in a CBLOCK and each thread can concurrently run a compression engine in compression hardware, resulting in a highly parallel decompression operation. 
     According to one embodiment, database software  102  utilizes multiple I/O and compression threads to achieve maximum parallelism between I/O and compression. When multiple CBLOCKs are read from a compressed data file to satisfy a request for data, a multi-threaded read pipeline is executed. For example, one thread reads CBLOCKs from the data file in the fastest and most efficient order into a software pipeline in compression cache  601  Another thread processes the CBLOCKs in compression cache pipeline to decompress data in the CBLOCKs. Another thread loads the uncompressed tabular slices from the CBLOCKs into soft-cache  121 , processes the uncompressed tabular slices and delivers tick data to the client query. This pipeline architecture takes full advantage of the multi-core technology in the Windows Server platform and the parallelism in the compression hardware to achieve performance improvement for decompressing data. When CBLOCKs are constructed from uncompressed slice buffers in soft-cache  121 , a multi-threaded write pipeline is executed. For example, one thread constructs CBLOCKs and another thread writes the CBLOCKs to the compressed data file. This pipeline approach provides a high degree of parallelism in the writing of compressed data and achieves performance improvement for compressing data. 
     A system and method for data compression using compression hardware has been described with respect to specific examples and subsystems. It will be apparent to those of ordinary skill in the art that it is not limited to these specific examples or subsystems but extends to other embodiments as well.