Patent Publication Number: US-11650971-B2

Title: System and method for large-scale data processing using an application-independent framework

Description:
CROSS-REFERENCE TO RELATED APPLICATIONS 
     This application is a continuation of U.S. patent application Ser. No. 17/134,862, filed Dec. 28, 2020, which is a continuation of U.S. patent application Ser. No. 16/417,126, filed May 20, 2019, (now U.S. Pat. No. 10,885,012), which is a continuation of U.S. patent application Ser. No. 15/479,228, filed Apr. 4, 2017, (now U.S. Pat. No. 10,296,500), which is a continuation of U.S. patent application Ser. No. 14/099,806, filed Dec. 6, 2013, (now U.S. Pat. No. 9,612,883), which is a continuation of U.S. patent application Ser. No. 12/686,292, filed Jan. 12, 2010 (now U.S. Pat. No. 8,612,510), which is a continuation of U.S. patent application Ser. No. 10/871,245, filed Jun. 18, 2004 (now U.S. Pat. No. 7,756,919), each of which is incorporated by reference in its entirety. 
     This application is also related to U.S. patent application Ser. No. 10/871,244 filed Jun. 18, 2004, now U.S. Pat. No. 7,650,331, which is hereby incorporated by reference in its entirety. 
    
    
     TECHNICAL FIELD 
     The disclosed embodiments relate generally to data processing systems and methods, and in particular to a framework for simplifying large-scale data processing. 
     BACKGROUND 
     Large-scale data processing involves extracting data of interest from raw data in one or more datasets and processing it into a useful data product. The implementation of large-scale data processing in a parallel and distributed processing environment typically includes the distribution of data and computations among multiple disks and processors to make efficient use of aggregate storage space and computing power. 
     Various functional languages (e.g., LISP™) and systems provide application programmers with tools for querying and manipulating large datasets. These conventional languages and systems, however, fail to provide support for automatically parallelizing these operations across multiple processors in a distributed and parallel processing environment. Nor do these languages and systems automatically handle system faults (e.g., processor failures) and I/O scheduling. 
     SUMMARY 
     A system and method for large-scale data processing includes operations for automatically handling programming details associated with parallelization, distribution, and fault-recovery. In some embodiments, application programmers can process large amounts of data by specifying map and reduce operations. The map operations retrieve data (e.g., key/value pairs) from input data files and produce intermediate data values in accordance with the mapping operations. The reduce operations merge or otherwise combine the intermediate data values in accordance with the reduce operations (e.g., combining intermediate values that share the same key). In some embodiments, the system and methods use a master process to manage tasks and one or more local databases to reduce network traffic and file system (FS) reads. 
     In some embodiments, a system for large-scale processing of data in a parallel processing environment includes one or more map modules configured to read input data and to apply at least one application-specific map operation to the input data to produce intermediate data values. An intermediate data structure stores the intermediate data values. The system also includes reduce modules, which are configured to retrieve the intermediate data values from the intermediate data structure and to apply at least one user-specified reduce operation to the intermediate data values to provide output data. Preferably, the map and/or reduce operations are automatically parallelized across multiple processors in the parallel processing environment. The programs or instructions for handling parallelization of the map and reduce operation are application independent. The input data and the intermediate data values can include key/value pairs and the reduce operation can include combining intermediate data values having the same key. The intermediate data structure can include one or more intermediate data files coupled to each map module for storing intermediate data values. The map and reduce operations can be implemented on different processors coupled to a distributed network. The output data can be written to a file system, which is accessible via the distributed network. 
     In some embodiments, a system for large-scale processing of data in a parallel processing environment includes a set of interconnected computing systems. At least one of the computing systems includes a set of application independent map modules configured for reading portions of input files containing data, and for applying at least one application-specific map operation to the data to produce intermediate key-value pairs. The system also includes a set of application independent reduce modules, which are configured to apply at least one application-specific reduce operation to the intermediate key-value pairs so as to combine intermediate values sharing the same key. In one embodiment, the application independent map modules and application independent reduce modules are both incorporated into a same process, sometimes called a worker process. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         FIG.  1    is a block diagram of a large-scale data processing model. 
         FIG.  2    is a block diagram of a large-scale data processing system. 
         FIG.  3    is a block diagram of a large-scale data processing system, including a master process for managing tasks. 
         FIG.  4    is a block diagram of a computer system for the data processing systems shown in  FIGS.  2  and  3   . 
         FIG.  5    is a block diagram of a data distribution network for large-scale data processing. 
         FIG.  6    is a flow diagram of an embodiment of a process for assigning tasks to processes. 
         FIG.  7 A  is a block diagram of an exemplary task status table. 
         FIG.  7 B  is a block diagram of an exemplary process status table. 
     
    
    
     DESCRIPTION OF EMBODIMENTS 
     Large-Scale Data Processing Model 
       FIG.  1    is a block diagram of a large-scale data processing model  100 . The model  100  generally includes mapping operations  102  and reduction operations  104 . The mapping operations  102  apply one or more mapping operations to a set of input data α i  (e.g., text files, records, logs, sorted maps, etc.) to provide a set of intermediate data values β i . The reduction operations  104  apply one or more reduction operations to the set of intermediate data values β i  to provide a set of output data φ i  (e.g., tables, sorted maps, record I/O, etc.). In some embodiments, the mapping operations  102  are implemented by one or more application-specific mapping functions, which map a set of input data α i  to a set of intermediate data values β i . The intermediate data values β i  are stored in one or more intermediate data structures. Some examples of intermediate data structures include, without limitation, files, buffers, histograms, count tables and any other suitable data structure or device for storing digital information. The intermediate data values β i  are processed by the reduction operations  104 , which are implemented by one or more application-specific reduction functions, which reduce the set of intermediate data values β i  to a set of output data φ i . 
     Distributed Data Processing System 
     In order to explain the operation of the large scale data processing system, it is helpful to consider an exemplary distributed data processing system in which the large scale data processing is performed. In general, the embodiments described here can be performed by a set of interconnected processors that are interconnected by one or more communication networks. 
       FIG.  5    is a block diagram of an exemplary distributed data processing system  500 . It should be appreciated that the layout of the system  500  is merely exemplary and the system  500  may take on any other suitable layout or configuration. The system  500  is used to store data, perform computational tasks, and transmit data between datacenters DC1-DC4. The system may include any number of data centers DCx, and thus the number of data centers shown in  FIG.  5    is only exemplary. The system  500  may include dedicated optical links or other dedicated communication channels, as well as supporting hardware such as modems, bridges, routers, switches, wireless antennas and towers, and the like. In some embodiments, the network  500  includes one or more wide area networks (WANs) as well as multiple local area networks (LANs). In some embodiments, the system  500  utilizes a private network, i.e., the system and its interconnections are designed and operated exclusively for a particular company or customer. Alternatively, a public network may be used. 
     Some of the datacenters DC1-DC4 may be located geographically close to each other, and others may be located far from the other datacenters. In some embodiments, each datacenter includes multiple racks. For example, datacenter  502  (DC1) includes multiple racks  508   a , . . . ,  508   n . The racks  508  can include frames or cabinets into which components are mounted. Each rack can include one or more processors (CPUs)  510 . For example, the rack  508   a  includes CPUs  510   a , . . . ,  510   n  (slaves 1-16) and the nth rack  506   n  includes multiple CPUs  510  (CPUs 17-31). The processors  510  can include data processors, network attached storage devices, and other computer controlled devices. In some embodiments, at least one of processors  510  operates as a master processor, and controls the scheduling and data distribution tasks performed throughout the network  500 . In some embodiments, one or more processors  510  may take on one or more roles, such as a master and/or slave. A rack can include storage (e.g., one or more network attached disks) that is shared by the one or more processors  510 . 
     In some embodiments, the processors  510  within each rack  508  are interconnected to one another through a rack switch  506 . Furthermore, all racks  508  within each datacenter  502  are also interconnected via a datacenter switch  504 . As noted above, the present invention can be implemented using other arrangements of multiple interconnected processors. 
     Further details regarding the distributed network  500  of  FIG.  5    can be found in U.S. patent application Ser. No. 10/613,626, entitled “System and Method For Data Distribution,” filed Jul. 3, 2003, which application is incorporated by reference herein in its entirety. 
     In another embodiment, the processors shown in  FIG.  5    are replaced by a single large-scale multiprocessor. In this embodiment, map and reduce operations are automatically assigned to processes running on the processors of the large-scale multiprocessor. 
     Large-Scale Data Processing System I 
       FIG.  2    is a block diagram of a large-scale data processing system  200 . The system  200  provides application programmers with an application-independent framework for writing data processing software that can run in parallel across multiple different machines on a distributed network. The system  200  is typically a distributed system having multiple processors, possibly including network attached storage nodes, that are interconnected by one or more communication networks.  FIG.  2    provides a logical view of a system  200 , which in some embodiments may be implemented on a system having the physical structure shown in  FIG.  5   . In one embodiment, the system  200  operates within a single data center of the system  500  shown in  FIG.  5   , while in another embodiment, the system  200  operates over two or more data centers of the system  500 . 
     As shown in  FIG.  2   , a set of input files  202  are processed by a first set of processes  204 , herein called map processes, to produce a set of intermediate data, represented here by files  206 . The intermediate data  206  is processed by a second set of processes  208 , herein called reduce processes, to produce output data  210 . Generally each “map process” is a process configured (or configurable) to perform map functions and to execute an application-specific map operator. Each “reduce process” is a process configured (or configurable) to perform reduce functions and to execute an application-specific reduce operator. A control or supervisory process, herein called the work queue master  214 , controls the set of processing tasks. As described in more detail below, the work queue master  214  determines how many map tasks to use, how many reduce tasks to use, which processes and processors to use to perform those tasks, where to store the intermediate data and output data, how to respond to any processing failures, and so on. 
     It should be noted that the work queue master  214  assigns tasks to processes, and that multiple processes may be executed by each of the processors in the group of processors that are available to do the work assigned by the work queue master  214 . In the context of  FIG.  5    or any other multiple processor system, the set of processes controlled by the work queue master  214  may be a subset of the full set of processes executed by the system, and furthermore the set of processors available to do the work assigned by the work queue master  214  may be fewer than the full set of processors in the system. Some of the resources of the system may be used for other tasks, such as tasks that generate the input data  202 , or that utilize the output data  210 . However, in some embodiments, some or all of the tasks that generate the input data  202  or utilize the output data  210  may also be controlled or supervised by the work queue master  214 . In addition, in some embodiments processors can be added or removed from the processing system during the execution of a map-reduce operation. The work queue master  214  keeps track of the processors in the system and the available processes executing on those processors. 
     Application programmers are provided with a restricted set of application-independent operators for reading input data and generating output data. The operators invoke library functions that automatically handle data partitioning, parallelization of computations, fault tolerance (e.g., recovering from process and machine failures) and I/O scheduling. In some embodiments, to perform a specific data processing operation on a set of input files, the only information that must be provided by an application programmer is: information identifying of the input file(s), information identifying or specifying the output files to receive output data, and two application-specific data processing operators, hereinafter referred to as map( ) and reduce( ). Generally, the map( ) operator specifies how input data is to be processed to produce intermediate data and the reduce( ) operator specifies how the intermediate data values are to be merged or otherwise combined. Note that the disclosed embodiments are not limited to any particular type or number of operators. Other types of operators (e.g., data filters) can be provided, as needed, depending upon the system  200  architecture and the data processing operations required to produce the desired, application-specific results. In some embodiments, the application programmers provide a partition operator, in addition to the map( ) and reduce( ) operators. The partition( ) operator, specifies how the intermediate data is to be partitioned over a set of intermediate files. 
     To perform large-scale data processing, a set of input files  202  are split into multiple data blocks 0, . . . , N−1 of either a specified or predefined size (e.g., 64 MB). Alternately, in some embodiments the input files  202  have a predefined maximum size (e.g., 1 GB), and the individual files are the data blocks. A data block is a subset of data that is retrieved during processing. In some embodiments, the data blocks are distributed across multiple storage devices (e.g., magnetic or optical disks) in a data distribution network to fully utilize the aggregate storage space and disk bandwidth of the data processing system. 
     Referring to  FIGS.  2  and  5   , in some embodiments the input data files  202  are stored in one or more data centers DC1-DC4. Ideally, the work queue master  214  assigns tasks to processors  510  in datacenters where the input files are stored so as to minimize network traffic whenever possible. In some embodiments, the work queue master  214  uses input file information received from a file system to determine the appropriate processor or process for executing a task, using a hierarchical decision process. When a process in a processor in a datacenter DC1-DC4 is idle, it requests a task from the work queue master  214 . The work queue master  214  searches the input file information received from the file system (e.g., FS  446 ,  FIG.  5   ), for an unprocessed data block on the machine assigned to process the task. If none are available, the work queue master  214  searches the file information for an unprocessed data block on the same rack  508  as the machine assigned to process the task. If none are available, the work queue master  214  searches the file information for an unprocessed data block in the same datacenter as the machine assigned to process the task. If none are available, the work queue master  214  will search for unprocessed blocks in other datacenters. 
     By using a hierarchical assignment scheme, data blocks can be processed quickly without requiring large volumes of data transfer traffic on the network  500 . This in turn allows more tasks to be performed without straining the limits of the network  500 . 
     Task Management 
     Referring again to  FIG.  2   , application programmers develop the map( ) and/or reduce( ) operators, which are computer programs that process input data and intermediate, respectively. In some embodiments these operators are compiled into binary files  212  suitable for use on a particular processing platform. The binary files  202  are loaded into a work queue master module  214 , which manages jobs submitted by users of the system  200 . In some embodiments, the work queue master  214  loads (or causes to be loaded) onto each process to which it allocates a map or reduce task, the library procedures, and the map( ) or reduce( ) operator required to perform the task assigned to the process. 
     The work queue master  214 , when it receives a request to process a set of data using a specified set application-specific map( ), reduce( ) and, optionally, partition( ) operators, determines the number of map tasks and reduce tasks to be performed to process the input data. This may be based on the amount of input data to be processed. For example, a job may include 10,000 map tasks and 10 reduce tasks. In some embodiments, the work queue master module generates a task status table having entries representing all the tasks to be performed, and then begins assigning those tasks to idle processes. As noted above, tasks may be allocated to idle processes based on a resource allocation scheme (e.g., priority, round-robin, weighted round-robin, etc.). 
     Process and Task Status Tracking 
       FIG.  6    is a flow diagram of an embodiment of a process  600  for assigning tasks to processes. Process  600  parallelizes a data processing task over as many processes as is consistent with the available computing resources. While the process  600  described below includes a number of steps that appear to occur in a specific order, it should be apparent that the process  600  steps are not limited to any particular order, and, moreover, the process  600  can include more or fewer steps, which can be executed serially or in parallel (e.g., using parallel processors or a multi-threading environment). Further, it should be noted that the steps or acts in process  600  are application-independent and are implemented using modules or instructions that are application-independent. Only the actual map and reduce operators, which produce intermediate data values from the input data and that produce output data from the intermediate data values, respectively, are application-specific. These application-specific operators are invoked by the map and reduce tasks assigned to processes in step  610 . By making a clear boundary between the application-independent aspects and application-specific aspects of performing a large scale data processing operation, the application-independent aspects can be optimized, thereby making the entire large scale data processing operation very efficient. 
     The process  600  begins by determining if there are tasks waiting to be assigned to a process (step  606 ). If there are no tasks waiting, then the process  600  waits for all the tasks to complete (step  604 ). If there are tasks waiting, then the process  600  determines if there are any idle processes (step  608 ). If there are idle processes, then the process  600  assigns a waiting task to an idle process (step  610 ) and returns to step  606 . If there are no idle processes, the process  600  waits for an idle process (step  614 ). Whenever a process completes a task, the process sends a corresponding message to the work queue master  214 , which updates the process and task status tables (step  612 ). The work queue master  214  may then assign a new task to the idle process, if it has any unassigned tasks waiting for processing resources. For reduce tasks, the work queue master  214  may defer assigning any particular reduce task to an idle process until such time that the intermediate data to be processed by the reduce task has, in fact, been generated by the map tasks. Some reduce tasks may be started long before the last of the map tasks are started if the intermediate data to be processed by those reduce tasks is ready for reduce processing. 
     In some embodiments, whenever a process fails, which may be discovered by the work queue master  214  using any of a number of known techniques, the work queue master  214  (A) determines what task was running in the failed process, if any, (B) assigns that task to a new process, waiting if necessary until an idle process becomes available, and (C) updates its process and task status tables accordingly. In some embodiments, the work queue master  214  may undertake remedial measures (step  602 ), such as causing the failed process to be restarted or replaced by a new process. In some embodiments, the work queue master may further detect when such remedial measures fail and then update its process status table to indicate such failures. In addition, in some embodiments, when a map task fails and is restarted in a new process, all processes executing reduce tasks are notified of the re-execution so that any reduce task that has not already read the data produced by the failed process will read the data produced by the new process. 
       FIG.  7 A  shows an exemplary task status table for keeping track of the status of map and reduce tasks. In some embodiments, each task (e.g., map, reduce) is assigned task ID, a status, a process, and one or more input files and output files. In some embodiments, the input files field may specify a portion of an input file (e.g., where the portion comprises a data block) to be processed by the task, or this field may specify portions of two of more input files. The status field indicates the current status of the task (e.g., waiting, in-progress, completed, or failed), which is being performed by the assigned process identified in the process field. The process retrieves data from one or more input files (or the one or more input file portions) identified in the input file field and writes the results of the task ID to one or more output files identified in the output file field. For example, in  FIG.  7 A , task Red0000 is assigned to process P0033, which is still in progress. The process P0033 retrieves data blocks from input file 12340 (e.g., intermediate file A,  FIG.  2   ) and writes the results of the task to output file 14000. In some embodiments, until a task is assigned to a process, the process field in the task status table indicates that no process has yet been assigned to perform that task. It should be apparent that there could be more or fewer fields in the task status table than shown in  FIG.  7 A , such as multiple fields for identifying output and input files. 
       FIG.  7 B  shows a process status table for keeping track of the status of all the processes to which the work queue master  214  can assign tasks. In some embodiments, each process is assigned to a task and a location. In some embodiments, each process is permanently assigned to a particular location (i.e., a particular processor). The status field indicates the current status of the process, which performs the assigned task at the assigned location. For example, process P0010 is “Busy” performing task Map0103 on location CPU011. It should be apparent that there could be more or fewer field in the process status table than shown in  FIG.  7 B , such as assigning multiple locations assigned to a single task (e.g., parallel processing). 
     Map Phase 
     In some embodiments, the set of application-specific data processing operations that the map( ) operator can perform is constrained. For example, in some embodiments, the map( ) operator may be required to process the input data one record at a time, proceeding monotonically from the first record to the last record in the data block being processed. In some embodiments, the map( ) operator may be required to generate its output data in the form of key/value pairs. Either the key or value or both can comprise structured data, as long as the data can be encoded into a string. For example, the key may have multiple parts, or the value may have multiple parts. 
     By requiring the map( ) operator&#39;s output to be in the form of key/value pairs, the resulting intermediate data can be mapped to a set of intermediate data files in accordance with a partition( ) operator. An exemplary partition( ) operator may specify that all intermediate data is to be directed to an intermediate file corresponding to the value of the first byte of the key. Another exemplary partition( ) operator may specify that all intermediate data is to be directed to an intermediate file corresponding to the value of the function “hash(Key) modulo N”, where N is a value specified by the application programmer and “hash(Key)” represents the value produced by applying a hash function to the key of the key/value pairs in the intermediate data. In some embodiments, the partition operator is always a modulo function and the application programmer only specifies the modulus to be used by the modulo function. In one embodiment, the partition operator is automatically selected by the work queue master  214 , or by one of the application-independent library functions, discussed below. 
     In some embodiments, the data blocks 0, . . . , N−1 are automatically assigned to map tasks (executed by map processes  204 -0, . . . ,  204 -N−1) in an application independent manner, by the work queue master  214 . In particular, the work queue master  214  is configured to determine the number of data blocks to be processed, and to create a corresponding number of instances of the map process  204 . Stated in another way, the work queue master  214  assigns a corresponding number of map tasks to processes, as suitable processes become available. Since the number of map tasks may exceed the number of processes available to the work queue master  214 , the work queue master  214  will assign as many map tasks as it can to available processes, and will continue to assign the remaining map tasks to processes as the processes complete previously assigned tasks and become available to take on new tasks. The work queue master  214  uses the task status table and process status tables, described above, to coordinate its efforts. 
     Reduce Phase 
     Application independent reduce modules  208  read intermediate data values (e.g., key/value pairs) from the intermediate files  206 . In some embodiments, each reduce module  208  reads from only one intermediate file  206 . The reduce modules  208  sort the intermediate data values, merge or otherwise combine sorted intermediate data values having the same key and then write the key and combined values to one or more output files  210 . In some embodiments, the intermediate file  206  and the output files  210  are stored in a File System (FS), which is accessible to other systems via a distributed network. 
     Software Implementation 
     In some embodiments, the map and reduce modules  204  and  208  are implemented as user-defined objects with methods to carry out application-specific processing on data using known object oriented programming techniques. For example, a MapReduction base class can be created that includes methods and data for counting the number of input files that contain a particular term or pattern of terms, sorting the results of the sort, eliminating duplicates in the sorted results and counting the number of occurrences of the term. Application programmers can derive other classes from the base class and instantiate the base class as an object in the application code to access its data and methods. 
     Large-Scale Data Processing System II 
     While the system  200  provides good performance for many large-scale data processing, the performance of the system  200  may diminish as the amount of data to be processed and thus the number of tasks increases. For instance, performance may be diminished when the size of the data blocks is decreased, thereby increasing the number of map tasks. Since the intermediate files  206  are stored in the FS, an increase in tasks results in an increase in intermediate file access requests and an associated increase in network traffic. Additionally, a single work queue master  214  can only handle a limited number of task assignments per time period, beyond which the work queue master  214  begins to limit system performance. Increasing the size of those tasks to accommodate additional jobs could result in load imbalances in the system  200 . These performance issues are addressed in the system  300 , which is described below with respect to  FIG.  3   . 
       FIG.  3    is a block diagram of a large-scale data processing system  300 , including a master process  320  (sometimes called a supervisory process) for managing tasks. In system  300 , one or more master processes  320  assign one or more tasks to one or more worker processes  304  and  308 . In some embodiments, the master process  320  is a task itself (e.g., task 0) initiated by the work queue master module  314  and is responsible for assigning all other tasks (e.g., mapping and reducing tasks) to the worker processes  304 ,  308 , in a master/slave type relationship. The worker processes  304 ,  308  include two or more process threads, each of which can be invoked based on the particular task assigned to it by the master process  320 . For example, each worker process  304  invokes a map thread to handle an assigned map task and invokes a reduce thread to handle an assigned reduce task. In one embodiment, the worker processes  304 ,  308  include one or more additional threads. For example, a distinct thread may be used to receive remote procedure calls (e.g., from the master process) and to coordinate work done by the other threads. In another example, a distinct thread may be used to handle remote read requests received from other processors (i.e., peers) in the system. 
     In one embodiment, the number of worker processes is equal to the number of machines available in the system  300  (i.e., one worker process per machine). In another embodiment, two or more worker processes are used in each of the machines in the system  300 . If a worker process fails, its task is reassigned to another worker process by the master process  320 . In some embodiments, the master process  320  or the work queue master  314  may undertake remedial measures to repair, restart or replace a failed worker process. 
     In some embodiments, when the work queue master  314  receives a map/reduce data processing job, the work queue master  314  allocates the job to a master process  320 . The master process  320  determines the number (M) of map tasks and the number (R) of reduce tasks to be performed, and then makes a request to the work queue master  314  for M+R processes (M+R+1, including the master process  320 ) to be allocated to the map/reduce data processing job. The work queue master  314  responds by assigning a number of processes to the job, and sends that information to the master process  320 , which will then manage the performance of the data processing job. If the number of processes requested exceeds the number of processes available, or otherwise exceeds the number of processes that the work queue master  314  is allowed to assign to the job, the number of processes assigned to the job will be less than the number requested. 
     In some embodiments, all R of the reduce tasks are all immediately assigned to processes, but the reduce tasks do not begin work (e.g., on data sorting) until the master process  320  informs them that there are intermediate files ready for processing. In some embodiments, a single worker process  304 / 308  can be assigned both a map task and a reduce task, simultaneously (with each being executed by a distinct process thread), and therefore assigning reduce tasks to processes at the beginning of the job does not reduce the throughput of the system. 
     Map Phase 
     The division of input files  302  into data blocks 0, . . . , N−1, may be handled automatically by the application independent code. Alternately, the user may set an optional flag, or specify a parameter, so as to control the size of the data blocks into which the input files are divided. Furthermore, the input data may come from sources other than files, such as a database or in-memory data structures. 
     The input data blocks 0, . . . , N−1, which may in some embodiments be treated as key/value pairs, are read by application independent worker processes  304 -0, . . . ,  304 -N−1, as shown in  FIG.  3   . The input files  302  can include a variety of data types typically used in data processing systems, including without limitation text files, record I/O, sorted data structures (such as B-trees), tables and the like. Each of the worker processes  304  to which a map task has been assigned applies the application-specific map( ) operator to the respective input data block so as generate intermediate data values. The intermediate data values are collected and written to one or more intermediate files  306 , which are stored locally at the machine (e.g., in one or more local databases) in which the worker process  304  is executed. The intermediate files  306  are retained (i.e., they are persistent) until the reduce phase completes. Note that in some embodiments, each of the intermediate files  306  receives output from only one worker process  304 , as shown in  FIG.  3   . When a worker process  304  completes its assigned task, it informs the master process  320  of the task status (e.g., complete or error). If the task was successfully completed, the worker process&#39;s status report is treated by the master process  320  as a request for another task. 
     In some embodiments, if there are enough worker processes  304  that all the intermediate values can be held in memory across the worker processes, then the system need not write any data to files on local disks. This optimization reduces execution time for map-reduce operations in which the number of worker processes is sufficient to handle all the map tasks at once, and the amount of intermediate data is sufficiently small to be kept in memory. 
     Application-Specific Combiner Function 
     In some cases, there is significant repetition in the intermediate keys produced by each map task, and the application-specific Reduce function is both commutative and associative. When all these conditions apply, a special optimization can be used to significantly reduce execution time of the map-reduce task. An example of a situation in which the optimization can be applied is a map-reduce operation for counting the number of occurrences of each distinct word in a large collection of documents. In this example, the application-specific map function (sometimes called the map( ) operator elsewhere in this document) outputs a key/value pair for every word w in every document in the collection, where the key/value pair is &lt;w, 1&gt;. The application-specific reduce function (sometimes called the reduce( ) operator elsewhere in this document) for this example is: 
     
       
         
           
               
               
             
               
                   
                   
               
             
            
               
                   
                 input data is “values”; 
               
               
                   
                 int result=0; // initialize result to zero 
               
               
                   
                 for each v in values: 
               
               
                   
                  result += ParseInt(v); 
               
               
                   
                 output: &lt;key, result&gt; 
               
               
                   
                   
               
            
           
         
       
     
     Each map task in this example will produce hundreds or thousands of records of the form &lt;word, 1&gt;. The Reduce function simply adds up the count values. To help conserve network bandwidth for map-reduce operations that satisfy these properties, the user may provide an application-specific Combiner function or operator. The Combiner function is invoked with each unique intermediate key and a partial set of intermediate values for the key. This is similar to the Reduce function, except that it gets executed at the end of each Map task by the same machine and process that performed by Map task. The Combiner function partially summarizes the intermediate key/value pairs. In fact, when using a Combiner function, the same function is typically specified for the Combiner and Reduce operations. The partial combining performed by the Combiner operation significantly speeds up certain classes of Map-Reduce operations, in part by significantly reducing the amount of information that must be conveyed from the processors that handle Map tasks to processors handling Reduce tasks, and in part by reducing the complexity and computation time required by the data sorting and Reduce function performed by the Reduce tasks. 
     Reduce Phase 
     Application independent worker processes  308  which have been assigned reduce tasks read data from the locally stored intermediate files  306 . In some embodiments, the master process  320  informs the worker processes  308  where to find intermediate data files  306  and schedules read requests for retrieving intermediate data values from the intermediate data files  306 . In some embodiments, each of the worker processes  308  reads a corresponding one of the intermediate files  306  produced by all or a subset of the worker processes  304 . For example, consider a system in which each of the worker processes  304  assigned a map task outputs M (e.g., 100) intermediate files, which we will call Partion-1,j through Partition-M,j, where j is an index identifying the map task that produced the intermediate files. The system will have 100 worker processes  308 , Worker-1 to Worker-M, each of which reads a corresponding subset of the intermediate files, Partition-p,j for all valid values of “j,” produced by the worker processes  304 , where “p” indicates the partition assigned to a particular worker process Worker-P ( 304 ) and “j” is an index identifying the map tasks that produced the intermediate files. 
     Each worker process  308  sorts the intermediate data values in the subset of the intermediate files read by that worker process in accordance with the key of the key/value pairs in the intermediate data. The sorting of the key/value pairs is an application independent function of the reduce threads in the worker processes  308 . Each worker process  308  also merges or otherwise combines the sorted intermediate data values having the same key, and writes the key and combined values to one or more output files  310 . The merging or other combining operation performed on the sorted intermediate data is performed by an application-specific reduce( ) operator. In some embodiments, the output files  310  are stored in a File System, which is accessible to other systems via a distributed network. When a worker process  308  completes its assigned reduce task, it informs the master process  320  of the task status (e.g., complete or error). If the reduce task was completed successfully, the worker process&#39;s status report is treated by the master process  320  as a request for another task. If the reduce task failed, the master process  320  reassigns the reduce task to another worker process  308 . 
     Recovering from Task and Processor Failures 
     In some embodiments, the master process  320  is configured to detect task and processor failures. When a task failure is detected, the master process  320  reassigns the task to another process. In some embodiments, the master process  320  redistributes the work of the failed task over a larger number of tasks so as to complete that task more quickly than by simply re-executing the task on another process. The master process subdivides the work assigned to the failed task to a plurality of newly mini-tasks, and then resumes normal operation by assigning the mini-tasks to available processes. The number of mini-tasks may be a predefined number, such as a number between 8 and 32, or it may be dynamically determined based on the number of idle processes available to the master process. In the case of a failed map task, division of the work assigned to the failed task means assigning smaller data blocks to the mini-tasks. In the case of a failed reduce task, division of the work assigned to the failed task may mean assigning the data sorting portion of the reduce task to a larger number of worker processes, thereby performing a distributed sort and merge. The resulting sorted data may, in some embodiments, be divided into a number of files or portions, each of which is then processed using the reduce( ) operator to produce output data. By detecting such failures and taking these remedial actions, the amount of delay in completing the entire data processing operation is significantly reduced. 
     When a processor failure is detected by the master process  320 , it may be necessary to re-execute all the tasks that the failed processor completed as well as any tasks that were in process when the processor failed, because the intermediate results produced by map tasks are stored locally, and the failure of the processor will in many cases make those results unavailable. Using the status tables, described above, the master process  320  determines all the tasks that ran on the processor, and also determines which of those tasks need to be re-executed (e.g., because the results of the tasks are unavailable and are still needed). The master process  320  then updates its status tables to indicate that these identified tasks are waiting for assignment to worker tasks. Thereafter, re-execution of the identified tasks is automatically handled using the processes and mechanisms described elsewhere in this document. 
     In some embodiments, an additional mechanism, herein called backup tasks, is used to guard against task failures as well as task slow downs. One of the main problems that lengthens the total time taken for a map-reduce operation to complete is the occurrence of “straggler” tasks or machines. A straggler is a process or machine that takes an unusually long time to complete one of the last few map or reduce tasks in the computation. Stragglers can arise for many reasons, including both hardware and software errors or conditions. When a large map-reduce operation is divided into thousands of map and reduce tasks executed by thousands of processes, the risk of a straggler task occurring is significant. The use of backup tasks, as described next, effectively guards against stragglers, without regard to the cause of the problem causing a process or machine to run slowly. In these embodiments, the master process determines when the map-reduce operation is close to completion. In one embodiment, the criteria for being close to completion is that the percentage of map tasks that have completed is above a threshold. In another embodiment, the criteria for being close to completion is that the percentage of map and reduce tasks, taken together, that have completed is above a threshold. The threshold can be any reasonably number, such as 95, 98, or 99 percent, or any percentage above 90 percent. Once the master process determines that the map-reduce operation is close to completion, the master process schedules backup executions of all remaining tasks. These duplicate tasks may be called backup map tasks and backup reduce tasks.  FIG.  7 A  shows an exemplary backup task, Map103b, in the task status table. Each task is marked as completed when either the primary or backup execution completes. This mechanism obviously increases the computational resources, and thus in some embodiments the criteria for invoking this mechanism are selected so as to increase the computational resources by no more than a few percent (e.g., five percent). The use of backup tasks significantly reduces the time to complete large map-reduce operations, often by more than twenty-five percent. 
     Master Process &amp; Status Tables 
     The master process  320  is responsible for assigning tasks to the worker processes  304  and  308  and for tracking their status and output. Periodically, the master process  320  solicits a report from each worker process assigned a task to determine its task status. In some embodiments, the report can be solicited using a polling scheme (e.g., round-robin). If the task status indicates that the worker process has failed, then the task is put back in the appropriate task queue to be reassigned to another worker process. In some embodiments, the master process  320  maintains status tables  326  for managing tasks, as described with respect to  FIGS.  7 A and  7 B . 
     In one embodiment in which more than one master process  320  is used, a locking mechanism is used to ensure that each of the entries of the status tables is modified by only one of the master processes at any one time. Whenever a master process  320  attempts to assign a map or reduce task to a process, or perform any other management of a map or reduce task, the master process first acquires (or attempts to acquire) a lock on the corresponding status table entry. If the lock is refused, the master process concludes that the map/reduce task is being managed by another master process and therefore the master process looks for another map/reduce task to manage. In another embodiment, the task status table is divided into portions, with each master process being given ownership of a corresponding portion of the task status table, and responsibility for managing the map/reduce tasks in that portion of the task status table. Each master process can read other portions of the task status table, but only uses information in entries indicating that the corresponding task has been completed. 
     The system  300  provides several advantages over other systems and methods by using one or more master processes to assign and manage tasks, together with local databases to store intermediate results produced by the tasks. For example, by distributing file reads over multiple local databases more machines can be used to complete tasks faster. Moreover, since smaller tasks are spread across many machines, a machine failure will result in less lost work and a reduction in the latency introduced by such failure. For example, the FS load for system  200  is O(M*R) file opens and the FS load for system  300  is O(M) input file opens+O(R) output file opens, where M is the number of map tasks and R is the number of reduce tasks. Thus, the system  200  requires significantly more file system file open operations than the system  300 . 
     Computer System for Large-Scale Data Processing 
       FIG.  4    is a computer system  400  for the data processing systems  200  and  300  shown in  FIGS.  2  and  3   . The computer system  400  generally includes one or more processing units (CPUs)  402 , one or more network or other communications interfaces  410 , memory  412 , and one or more communication buses  414  for interconnecting these components. The system  400  may optionally include a user interface  404 , for instance a display  406  and a keyboard  408 . Memory  412  may include high speed random access memory and may also include non-volatile memory, such as one or more magnetic disk storage devices. Memory  412  may include mass storage that is remotely located from the central processing unit(s)  402 . 
     The memory  412  stores an operating system  416  (e.g., Linux or Unix), a network communication module  418 , a system initialization module  420 , application software  422  and a library  430 . The operating system  416  generally includes procedures for handling various basic system services and for performing hardware dependent tasks. The network communication module  418  is used for connecting the system  400  to a file system (FS)  446 , servers or other computing devices via one or more communication networks, such as the Internet, other wide area networks, local area networks, metropolitan area networks, and the like. The system initialization module  420  initializes other modules and data structures stored in memory  414  required for the appropriate operation of the system  400 . In some embodiments, the application software  422  includes a map operator  424 , a reduce operator  426  and a partition operator  428 , and the library  430  includes application-independent map functions  432 , reduce functions  434 , and partition functions  436 . As discussed above, the application software  422  may also include a combiner operator  425  when the map-reduce operation meets certain conditions. The functions, procedures or instructions in the library  430  handle the application independent aspects of large scaled data processing jobs, while the application software  422  provides the application-specific functions for producing output data. The application software  422  may include source programs for the map, combiner, reduce and partition operators as well as the corresponding compiled programs, represented by binary files  212  and  312  in  FIGS.  2  and  3   , respectively. 
     One or more status tables  444  are also included to track tasks and processes, as described with respect to  FIGS.  7 A and  7 B . In some embodiments, the computer system  400  includes worker processes  438 , intermediate files  440 , and one or more master process(es)  442 . The interaction of worker processes  438  and master processes  442  were described with respect to  FIG.  3   . 
     Referring to  FIGS.  2 ,  3  and  4   , an application programmer can create a script or program using the application software  422 , which includes one or more operators  424 ,  426  and  428 . The script or program is processed into binary files  212 ,  312  and provided to the work queue master  214 ,  314 . 
     For the embodiment shown in  FIG.  2   , input files  202  are split into multiple data blocks and assigned by the work queue master  214  to individual, application independent map and reduce processes  204  and  208 . The processes  204  invoke map functions  432  to process the input data (e.g., counting the number of occurrences of a term) to provide intermediate data values. In some embodiments, the input data is structured in the form of key-value pairs. The partition function  436  partitions the map output into one or more intermediate files  440 , which are stored on the FS  446 . The intermediate data values are processed by the map and reduce processes  204  and  208 , which invoke reduce functions  208  for sorting and combining intermediate data values having the same key, and for storing the key and values in one or more output files  210  located on the FS  446 . The work queue master  214  manages the map and reduce processes  204  and  208  with the assistance of status tables  444 , as described with respect to  FIGS.  7 A and  7 B . 
     For the embodiment shown in  FIG.  3   , input files  302  are split into multiple data blocks and assigned by the master process  442  to individual, application independent worker processes  438 . The worker processes  438  invoke map functions  432  for operating on blocks of input data (e.g., counting the number of occurrences of a term) to provide intermediate data values. The partition function  436  partitions the map output into one or more intermediate files  440 , which are stored locally in memory  412 . The intermediate data values are processed by application independent worker processes  438 , which invoke reduce functions  434  for sorting and combining intermediate data values having the same key, and for storing the resulting output data in one or more output files  310  located on the file system  446 . The master process  442  manages the worker processes  436  with the assistance of status tables  444 , as described with respect to  FIGS.  7 A and  7 B . 
     The foregoing description, for purpose of explanation, has been described with reference to specific embodiments. However, the illustrative discussions above are not intended to be exhaustive or to limit the invention to the precise forms disclosed. Many modifications and variations are possible in view of the above teachings. The embodiments were chosen and described in order to best explain the principles of the invention and its practical applications, to thereby enable others skilled in the art to best utilize the invention and various embodiments with various modifications as are suited to the particular use contemplated.