Patent Publication Number: US-10324756-B2

Title: Dynamic reduction of stream backpressure

Description:
CROSS-REFERENCE TO RELATED APPLICATIONS 
     This application is a continuation of co-pending U.S. patent application Ser. No. 15/063,710, filed Mar. 8, 2016, which is a continuation of Ser. No. 13/682,008, filed Nov. 20, 2012, now U.S. Pat. No. 9,389,911, which is a continuation of patented U.S. patent application Ser. No. 13/190,694, filed Jul. 26, 2011, now U.S. Pat. No. 8,990,452. The aforementioned related patent applications are herein incorporated by reference in their entirety. 
    
    
     BACKGROUND 
     Field of the Invention 
     Embodiments of the present invention generally relate to stream applications. Specifically, the invention relates to detecting and reducing backpressure in a stream application. 
     Description of the Related Art 
     While computer databases have become extremely sophisticated, the computing demands placed on database systems have also increased at a rapid pace. Database systems are typically configured to separate the process of storing data from accessing, manipulating or using data stored in the database. More specifically, databases use a model where data is first stored, then indexed, and finally queried. However, this model cannot meet the performance requirements of some real-time applications. For example, the rate at which a database system can receive and store incoming data limits how much data can be processed or otherwise evaluated. This, in turn, can limit the ability of database applications to process large amounts of data in real-time. 
     SUMMARY 
     One embodiment herein is a method for processing data which includes receiving streaming data to be processed by a plurality of processing elements, the processing elements processing at least a portion of the received data by operation of one or more computer processors, where each of the processing elements comprises an executable portion of code. The method includes establishing an operator graph of the plurality of processing elements, the operator graph defining at least one execution path through which the streaming data flows through the plurality of processing elements, and where each processing element in the execution path is configured to at least one of receive data from at least one upstream processing element and transmit data to at least one downstream processing element. The method includes receiving a request to add at least one processing element to the execution path of the operator graph. Before adding the at least one processing element, the method includes identifying a predicted relative change of data throughput in a first processing element in the execution path, where the predicted relative change of data is based on predicting a utilization rate of one of the computer processors resulting from adding the at least one processing element to the operator graph. Upon determining that the relative change of data throughput satisfies a predetermined threshold, the method includes determining not to add the at least one processing element to the operator graph. 
     Another embodiment herein is a system for processing data that includes a computer process and a memory containing a program that, when executed on the computer processor, performs an operation for processing data. The operation includes receiving streaming data to be processed by a plurality of processing elements, where each of the processing elements comprises an executable portion of code and establishing an operator graph of the plurality of processing elements, the operator graph defining at least one execution path through which the streaming data flows through the plurality of processing elements, and where each processing element in the execution path is configured to at least one of receive data from at least one upstream processing element and transmit data to at least one downstream processing element. The operation includes receiving a request to add at least one processing element to the execution path of the operator graph and, before adding the at least one processing element, identifying a predicted relative change of data throughput in a first processing element in the execution path, where the predicted relative change of data is based on predicting a utilization rate of the computer processor resulting from adding the at least one processing element. Upon determining that the relative change of data throughput satisfies a predetermined threshold, the operation includes determining not to add the at least one processing element to the operator graph. 
     Another embodiment herein is computer program product for processing data that includes a non-transitory computer-readable storage medium having computer-readable program code embodied therewith, the computer-readable program code, when executed on one or more computer processors, is configured to receive streaming data to be processed by a plurality of processing elements, the processing elements processing at least a portion of the received data by operation of the one or more computer processors, where each of the processing elements comprises an executable portion of code. The computer-readable program code is configured to establish an operator graph of the plurality of processing elements, the operator graph defining at least one execution path through which the streaming data flows through the plurality of processing elements, and where each processing element in the execution path is configured to at least one of receive data from at least one upstream processing element and transmit data to at least one downstream processing element. The computer-readable program code is configured to receive a request to add at least one processing element to the execution path of the operator graph and, before adding the at least one processing element, identify a predicted relative change of data throughput in a first processing element in the execution path, where the predicted relative change of data is based on predicting a utilization rate of one of the computer processors resulting from adding the at least one processing element. Upon determining that the relative change of data throughput satisfies a predetermined threshold, the computer-readable program code is configured to determine not to add the at least one processing element to the operator graph. 
    
    
     
       BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWINGS 
       So that the manner in which the above recited aspects are attained and can be understood in detail, a more particular description of embodiments of the invention, briefly summarized above, may be had by reference to the appended drawings. 
       It is to be noted, however, that the appended drawings illustrate only typical embodiments of this invention and are therefore not to be considered limiting of its scope, for the invention may admit to other equally effective embodiments. 
         FIGS. 1A-1B  illustrate a computing infrastructure configured to execute a stream application, according to one embodiment of the invention. 
         FIG. 2  is a more detailed view of the compute node of  FIGS. 1A-1B , according to one embodiment of the invention. 
         FIG. 3  is a more detailed view of the server computing system of  FIG. 1 , according to one embodiment of the invention. 
         FIG. 4  is a flow diagram illustrating a method of removing or preventing backpressure, according to embodiments of the invention. 
         FIG. 5  is a diagram illustrating a system that may create backpressure, according to one embodiment of the invention. 
         FIGS. 6A-6C  are diagrams illustrating methods of lessening or preventing backpressure, according to embodiments of the invention. 
         FIGS. 7A-7C  are diagrams illustrating methods of lessening or preventing backpressure, according to embodiments of the invention. 
     
    
    
     DETAILED DESCRIPTION 
     Stream-based computing is emerging as a technology that enables continuous analysis of large amounts of moving data to improve a user&#39;s decision making process. Products are available which allow users to create applications that process and query streaming data before it reaches a repository. With this emerging technology, users can specify processing logic to apply to inbound data records while they are “in flight,” with the results available in a very short amount of time, often in milliseconds. Constructing an application using this type of processing has opened up a new programming paradigm that will allow for a broad variety of innovative applications, systems and processes to be developed, as well as present new challenges for application programmers and database developers. 
     In a stream application, operators are connected to one another such that data flows from one operator to the next forming a logical dataflow graph. Scalability is reached by distributing an application across nodes by creating many small executable pieces of code (i.e., processing elements), as well as load balancing among them. One or more operators in a stream application can be fused together to form a processing element. Doing so allows the fused operators to share a common process space, resulting in much faster communication between operators than is available using inter-process communication techniques (e.g., using a TCP/IP socket). Further, groups of processing elements (i.e., jobs) can be inserted or removed dynamically from one or more applications performing streaming data analysis. 
     One advantage of stream applications is that they allow the user to granularly control the process flow of data through the application. In other words, the user may designate specific operators for each processing element that perform various operations on the incoming data, and may dynamically alter the stream application by modifying the operators and the order in which they are performed. 
     However, because stream applications often deal with large volumes of data, the processing of which is spread over multiple processing elements across multiple compute nodes, backpressure may slow down the processing of a particular execution path. “Backpressure” may be defined as a decrease of data throughput through a processing element. Specifically, a processing element experiences backpressure if the processing element is unable to receive data from another upstream processing element because, for example, a buffer associated with the processing element is already at maximum capacity. Backpressure may be prevented, however, by monitoring one or more indicators that predict if backpressure may occur. These indicators determine whether a processing element is able to process data as fast as data is received. Decreasing data throughput means the ratio of processed data to received data in a processing element is also decreasing. An indicator of backpressure may be buffer utilization, computer processor unit (CPU) utilization, or transient time of data through the processing element. Once a first processing element experiences backpressure—e.g., its buffer reaches maximum capacity such that it can no longer accept data from other upstream processing elements—the backpressure begins to spread to these upstream processing elements since the first processing element can no longer accept data from the upstream processing elements. The upstream processing element must then store the refused data in their own buffers. The buffer capacities of the processing elements experiencing backpressure may be increased, but this approach merely delays the spreading of the backpressure to other processing elements and does not decrease the time needed for data to flow through the distributed system. 
     Embodiments of the invention provide techniques for identifying indicators of backpressure and taking measures to alleviate the backpressure or prevent the backpressure from occurring. In particular, embodiments of the invention may use historical information collected from multiple executions, current CPU usage, buffer usage, response time and the like as indicators of current or future backpressure. Once an indicator is identified, an action is taken to alleviate the current backpressure or prevent the backpressure from occurring. 
     If the processing element is currently experiencing backpressure, a second processing element that feeds data into the processing element may be removed, or the last processing element added to the operator graph which contributes to the backpressure may be removed (e.g., LIFO). Alternatively, where multiple processing elements send data to the processing element or where one processing element transmits data on multiple data paths, the data rate for each of the data paths may be reduced. Moreover, the multiple processing elements or data paths may be prioritized according to importance of the data or data paths, amount of data they send, or their transient times. The assigned priority may then be used to determine which processing element to eliminate or which data rate to reduce. 
     If the processing element will experience future backpressure, a request to add a new processing element may be refused. Alternatively, the request may be granted but the data rates may be prioritized and reduced such that the backpressure is prevented. 
     In the following, reference is made to embodiments of the invention. However, it should be understood that the invention is not limited to specific described embodiments. Instead, any combination of the following features and elements, whether related to different embodiments or not, is contemplated to implement and practice the invention. Furthermore, although embodiments of the invention may achieve advantages over other possible solutions and/or over the prior art, whether or not a particular advantage is achieved by a given embodiment is not limiting of the invention. Thus, the following aspects, features, embodiments and advantages are merely illustrative and are not considered elements or limitations of the appended claims except where explicitly recited in a claim(s). Likewise, reference to “the invention” shall not be construed as a generalization of any inventive subject matter disclosed herein and shall not be considered to be an element or limitation of the appended claims except where explicitly recited in a claim(s). 
     As will be appreciated by one skilled in the art, aspects of the present invention may be embodied as a system, method or computer program product. Accordingly, aspects of the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment (including firmware, resident software, micro-code, etc.) or an embodiment combining software and hardware aspects that may all generally be referred to herein as a “circuit,” “module” or “system.” Furthermore, aspects of the present invention may take the form of a computer program product embodied in one or more computer readable medium(s) having computer readable program code embodied thereon. 
     Any combination of one or more computer readable medium(s) may be utilized. The computer readable medium may be a computer readable signal medium or a computer readable storage medium. A computer readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. More specific examples (a non-exhaustive list) of the computer readable storage medium would include the following: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the context of this document, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus or device. 
     A computer readable signal medium may include a propagated data signal with computer readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated signal may take any of a variety of forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A computer readable signal medium may be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. 
     Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing. 
     Computer program code for carrying out operations for aspects of the present invention may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, Smalltalk, C++ or the like and conventional procedural programming languages, such as the “C” programming language or similar programming languages. The program code may execute entirely on the user&#39;s computer, partly on the user&#39;s computer, as a stand-alone software package, partly on the user&#39;s computer and partly on a remote computer or entirely on the remote computer or server. In the latter scenario, the remote computer may be connected to the user&#39;s computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet Service Provider). 
     Aspects of the present invention are described below with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and/or block diagrams, and combinations of blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions/acts specified in the flowchart and/or block diagram block or blocks. 
     These computer program instructions may also be stored in a computer readable medium that can direct a computer, other programmable data processing apparatus, or other devices to function in a particular manner, such that the instructions stored in the computer readable medium produce an article of manufacture including instructions which implement the function/act specified in the flowchart and/or block diagram block or blocks. 
     The computer program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other devices to cause a series of operational steps to be performed on the computer, other programmable apparatus or other devices to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide processes for implementing the functions/acts specified in the flowchart and/or block diagram block or blocks. 
     Embodiments of the invention may be provided to end users through a cloud computing infrastructure. Cloud computing generally refers to the provision of scalable computing resources as a service over a network. More formally, cloud computing may be defined as a computing capability that provides an abstraction between the computing resource and its underlying technical architecture (e.g., servers, storage, networks), enabling convenient, on-demand network access to a shared pool of configurable computing resources that can be rapidly provisioned and released with minimal management effort or service provider interaction. Thus, cloud computing allows a user to access virtual computing resources (e.g., storage, data, applications, and even complete virtualized computing systems) in “the cloud,” without regard for the underlying physical systems (or locations of those systems) used to provide the computing resources. 
     Typically, cloud computing resources are provided to a user on a pay-per-use basis, where users are charged only for the computing resources actually used (e.g., an amount of storage space used by a user or a number of virtualized systems instantiated by the user). A user can access any of the resources that reside in the cloud at any time, and from anywhere across the Internet. In context of the present invention, a user may access applications or related data available in the cloud. For example, the nodes used to create a stream application may be virtual machines hosted by a cloud service provider. Doing so allows a user to access this information from any computing system attached to a network connected to the cloud (e.g., the Internet). 
       FIGS. 1A-1B  illustrate a computing infrastructure configured to execute a stream application, according to one embodiment of the invention. As shown, the computing infrastructure  100  includes a management system  105  and a plurality of compute nodes  130   1-4 , each connected to a communications network  120 . Also, the management system  105  includes an operator graph  132  and a stream manager  134 . As described in greater detail below, the operator graph  132  represents a stream application beginning from one or more source operators through to one or more sink operators. This flow from source to sink is also generally referred to herein as an execution path. However, an operator graph may be a plurality of linked together executable units (i.e., processing elements) with or without a specified source or sink. Thus, an execution path would be the particular linked together execution units that data traverses as it propagates through the operator graph. 
     Typically, processing elements receive an N-tuple of data attributes from the stream as well as emit an N-tuple of data attributes into the stream (except for a sink operator where the stream terminates or a source operator where the stream starts). Of course, the N-tuple received by a processing element need not be the same N-tuple sent downstream. Additionally, the processing elements could be configured to receive or emit data in formats other than an N-tuple (e.g., the processing elements could exchange data marked up as XML documents). Furthermore, each processing element may be configured to carry out any form of data processing functions on the received tuple, including, for example, writing to database tables or performing other database operations such as data joins, splits, reads, etc., as well as performing other data analytic functions or operations. 
     The stream manager  134  may be configured to monitor a stream application running on the compute nodes  130   1-4 , as well as to change the structure of the operator graph  132 . The stream manager  134  may move processing elements (PEs) from one compute node  130  to another, for example, to manage the processing loads of the compute nodes  130  in the computing infrastructure  100 . Further, stream manager  134  may control the stream application by inserting, removing, fusing, un-fusing, or otherwise modifying the processing elements (or what data-tuples flow to the processing elements) running on the compute nodes  130   1-4 . Additionally, the stream manager  134  may request that a processing element change the rate at which data is emitted from the element. 
     In one embodiment of the invention, the management system  105  may maintain multiple operator graphs  132 . In such an embodiment, one operator graph  132  is designated as the primary operator graph, which represents the general or default processing flow, while the other operator graphs may represent alternate processing flows that may be used, for example, to reduce or prevent backpressure. 
       FIG. 1B  illustrates an example operator graph that includes ten processing elements (labeled as PE 1 -PE 10 ) running on the compute nodes  130   1-4 . While a processing element may be executed as an independently running process with its own process ID (PID) and memory space, multiple processing elements may also be fused to run as single process or job (with a PID and memory space). In cases where two (or more) processing elements are running independently, inter-process communication may occur using a network socket (e.g., a TCP/IP socket). However, when processes are fused together, the fused processing elements can use more rapid communication techniques for passing N-tuples (or other data) among the joined operators in the fused processing elements. 
     As shown, the operator graph begins at a source  135  (that flows into the processing element labeled PE 1 ) and ends at sink  140   1-2  (that flows from the processing elements labeled as PE 6  and PE 10 ). Compute node  130   1  includes the processing elements PE 1 , PE 2  and PE 3 . Source  135  flows into the processing element PE 1 , which in turn emits tuples that are received by PE 2  and PE 3 . For example, PE 1  may split data attributes received in a tuple and pass some data attributes to PE 2 , while passing other data attributes to PE 3 . Data that flows to PE 2  is processed by the operators contained in PE 2 , and the resulting tuples are then emitted to PE 4  on compute node  130   2 . Likewise, the data tuples emitted by PE 4  flow to sink PE 6   140   1 . Similarly, data tuples flowing from PE 3  to PE 5  also reach sink PE 6   140   1 . Thus, in addition to being a sink for this example operator graph, PE 6  could be configured to perform a join operation, combining tuples received from PE 4  and PE 5 . This example operator graph also shows data tuples flowing from PE 3  to PE 7  on compute node  130   3 , which itself shows data tuples flowing to PE 8  and looping back to PE 7 . Data tuples emitted from PE 8  flow to PE 9  on compute node  130   4 , which in turn emits tuples to be processed by sink PE 10   140   2 . 
     Furthermore, although embodiments of the present invention are described within the context of a stream application, this is not the only context relevant to the present disclosure. Instead, such a description is without limitation and is for illustrative purposes only. Of course, one of ordinary skill in the art will recognize that embodiments of the present invention may be configured to operate with any computer system or application capable of performing the functions described herein. For example, embodiments of the invention may be configured to operate in a clustered environment with a standard database processing application. 
       FIG. 2  is a more detailed view of the compute node  130  of  FIGS. 1A-1B , according to one embodiment of the invention. As shown, the compute node  130  includes, without limitation, at least one CPU  205 , a network interface  215 , an interconnect  220 , a memory  225 , and storage  230 . The compute node  130  may also include an I/O devices interface  210  used to connect I/O devices  212  (e.g., keyboard, display and mouse devices) to the compute node  130 . 
     Each CPU  205  retrieves and executes programming instructions stored in the memory  225 . Similarly, the CPU  205  stores and retrieves application data residing in the memory  225 . The interconnect  220  is used to transmit programming instructions and application data between each CPU  205 , I/O devices interface  210 , storage  230 , network interface  215 , and memory  225 . CPU  205  is included to be representative of a single CPU, multiple CPUs, a single CPU having multiple processing cores, and the like. The memory  225  is generally included to be representative of a random access memory (e.g., DRAM or Flash). Storage  230 , such as a hard disk drive, solid state device (SSD), or flash memory storage drive, may store non-volatile data. 
     In this example, the memory  225  includes a plurality of processing elements  235  and stream connection data  255 . Each PE  235  includes a collection of operators  240 . As noted above, each operator  240  may provide a small chunk of executable code configured to process data flowing into a processing element (e.g., PE  235 ) and to emit data to other operators  240  in that PE and to other processing elements in the stream application. Such processing elements may be on the same compute node  130  or on other compute nodes accessible over the data communications network  120 . The stream connection data  255  represents the connections between PEs on compute node  130  (e.g., a TCP/IP socket connection between two separate PEs  235 ), as well as connections to other compute nodes  130  with upstream and or downstream PEs in the stream application, also via TCP/IP sockets (or other inter-process data communication mechanisms). 
     As shown, storage  230  contains buffered stream data  260  and historical information  265 . The buffered stream data  260  represents a storage space for data flowing into the compute node  105  from upstream processing elements (or from a data source for the stream application). For example, buffered stream data  260  may include data tuples waiting to be processed by one of the PEs  235 —i.e., a buffer. Buffered stream data  260  may also store the results of data processing performed by processing elements  235  that will be sent to downstream processing elements. For example, a PE  235  may have to store tuples intended for a downstream PE  235  if that PE  235  already has a full buffer, which may occur when the operator graph is experiencing backpressure. On the other hand, the historical information  265  is formulated after running a particular stream application several times. Historical information  265  can be, for example, the conditions or operator graph that caused backpressure. In sum, the buffered stream data  260  is a real-time measurement while the historical information  265  is a predictor of whether the PE  235  may experience backpressure. 
       FIG. 3  is a more detailed view of the server computing system  105  of  FIG. 1 , according to one embodiment of the invention. As shown, server computing system  105  includes, without limitation, a CPU  305 , a network interface  315 , an interconnect  320 , a memory  325 , and storage  330 . The client system  130  may also include an I/O device interface  310  connecting I/O devices  312  (e.g., keyboard, display and mouse devices) to the server computing system  105 . 
     Like CPU  205  of  FIG. 2 , CPU  305  is configured to retrieve and execute programming instructions stored in the memory  325  and storage  330 . Similarly, the CPU  305  is configured to store and retrieve application data residing in the memory  325  and storage  330 . The interconnect  320  is configured to move data, such as programming instructions and application data, between the CPU  305 , I/O devices interface  310 , storage unit  330 , network interface  305 , and memory  325 . Like CPU  205 , CPU  305  is included to be representative of a single CPU, multiple CPUs, a single CPU having multiple processing cores, and the like. Memory  325  is generally included to be representative of a random access memory. The network interface  315  is configured to transmit data via the communications network  120 . Although shown as a single unit, the storage  330  may be a combination of fixed and/or removable storage devices, such as fixed disc drives, removable memory cards, optical storage, SSD or flash memory devices, network attached storage (NAS), or connections to storage area-network (SAN) devices. 
     As shown, the memory  325  stores a stream manager  134 . Additionally, the storage  330  includes a primary operator graph  132  and an alternate operator graph  340 . The stream manager  134  may generally route tuples received as part of a data stream to PEs  235  to be processed, according to the primary operator graph  132 . Additionally, the stream manager  134  can access the historical information  265  and buffered stream data  260  located on each compute node  130 . These two elements provide the stream manager  134  with the necessary information to detect current or future backpressure. For example, if the buffered stream data  260  shows the stream manager  134  that the buffer is nearly full of received tuples, the stream manager  134  may remove a processing element that sends tuples to the processing element with the full buffer. Alternatively, the stream manager  134  may identify that a CPU  205  on a different computer node  130  has free processing power. In such a scenario, the stream manager  134  may move a processing element  235  to the different compute node  130  with the available processing power to alleviate or prevent the backpressure. 
     The stream manager  134  may include a backpressure logic module  327  which includes logic for alleviating or preventing backpressure after an indicator of backpressure is detected. The backpressure logic module  327  may be a configuration file (e.g., an XML configuration file) that instructs the stream manager  134  which action to take to lessen or prevent backpressure. These actions include deleting processing element and/or data paths, changing the amount of data transmitted on data paths, or refusing to add jobs or processing elements to an operator graph. 
       FIG. 4  is a flow diagram illustrating a method of mitigating or preventing backpressure, according to embodiments of the invention. At step  405 , the stream manager  134  creates an operator graph  132  which includes at least one processing element  235  that receives data or tuples from at least one other processing element  235 . 
       FIG. 5  is a diagram illustrating a system that may create backpressure, according to one embodiment of the invention.  FIG. 5  is one example of an operator graph  132  that has at least one processing element—PE 16 —that receives data from at least one other processing element—PE 9  or PE 11 . In this embodiment, processing elements are arranged into separate jobs. Each job contains one or more processing elements which perform a specific task such as concatenating or combining tuples, writing to a database, retrieving data from a database, executing other database operations such as data joins, splits, or reads, accessing a third party service, and the like. Each job may then send tuples to a second job where another task may be performed; for example, Jobs  1  and  2  each send tuples to Job  4 . Specifically, PE 9  and PE 11  both send tuples or data to PE 16 . Further, Job  3  has not yet been added to the operator graph  132  as indicated by the ghosted line  502 . At a later time, the stream manager  134  may receive a request to add Job  3  to the operator graph  132  such that Job  3  begins to send tuples to Job  4 . Note that the processing elements and jobs may be scattered on a plurality of nodes  130 . For example, PE 13  and PE 18  may be located on the same node (not shown) even though they are grouped in separate jobs. Stated differently, the software implementation of grouping the processing elements into jobs may be done independently of the hardware that runs the processing elements. 
     In one embodiment, backpressure may occur when only one processing element sends data to a second processing element. For example, in Job  2 , PE 10  sends tuples to PE 11 . If PE 10  begins to send twice as many tuples, or sends tuples that include more data, such that the CPU  205  associated with PE 11  cannot process the tuples as quickly as they are received, the buffered stream data  260  associated with PE 11 &#39;s buffer may begin to increase. Once the buffer reaches the maximum capacity, PE 11  may refuse a portion of the tuples sent to it by PE 10 , thereby forcing PE 10  to store the rejected tuples in its own buffer as a result of the backpressure. Once PE 10 &#39;s buffer is at maximum capacity, it is also unable to process received tuples. In this manner, backpressure in one processing element  235  may spread to other processing elements. 
     In another embodiment, backpressure may occur when a processing element—e.g., PE 16 —receives tuples from two different processing elements—e.g., PE 9  and PE 11 . For example, PE 16  may be able to process tuples as quickly as they are received from PE 9 ; however, PE 16  may begin to buffer tuples when it must process received tuples from both PE 9  and PE 11 . Eventually, the buffer may reach maximum capacity and cause PE 16  to begin to refuse tuples sent from PE 9  or PE 11 . 
     In another embodiment, a CPU  205  associated with a processing element may be used by other processing elements. For example, other processing elements may be added or moved to a compute node  130  which slows down a shared CPU  205  and causes the data throughput on a processing element to decrease. Similarly, a processing element may be moved to a different computer node  130  where the CPU  205  has a greater utilization percentage. Both of these examples demonstrate the effect of hardware on backpressure. Specifically, backpressure may occur without a change in the operator graph or data rates flowing between processing elements. 
     Additionally, the stream manager  134  may determine if adding a new job with its accompanying processing elements  235  will cause backpressure. For example, PE 16  may be able to process tuples as quickly as they are received from both PE 9  and PE 11 . However, the CPU  205  associated with PE  16  may be running near maximum utilization—e.g., 90%-99% CPU utilization. Adding another data path that transmits data from PE 15  to PE 16  may exceed the capabilities of the CPU  205 . Thus, if the stream manager  134  adds Job  3  to the operator graph  132  and the CPU  205  can now no longer process the tuples as fast as they are received, PE 16 &#39;s buffer may fill up and cause backpressure to spread to other processing elements. 
     A processing element may temporarily be unable to process tuples as fast as they are received but may not be experiencing backpressure—i.e., blocking other PEs from sending it data. For example, the usage of a CPU may suddenly spike due to routine system maintenance which may cause tuples to be loaded into the buffered stream data  260 . These temporary slowdowns may be sufficiently controlled and handled by the buffers associated with each processing element. Instead, the stream monitor  134  may use a predetermined period of time when evaluating whether backpressure is going to occur. That is, the stream monitor  134  may be configured to ignore temporary slowdowns and identify a processing element that is constantly unable to process tuples as fast as they are received which may eventually result in the processing element experiencing backpressure. 
     Returning to  FIG. 4 , at step  410  the stream manager  134  monitors each processing element for an indicator of backpressure. In general, backpressure occurs after the data throughput of a processing element decreases such that the processing element receives data faster than it can process the data, or if the processing element is blocked from sending data to a downstream processing element. Backpressure may result in any one of the situations described above. 
     The stream manager  134  may monitor several indicators to detect backpressure in a processing element  235 . In one embodiment, the stream manager  134  may monitor the buffered stream data  260 . If the buffered stream data  260  exceeds the capacity of the buffer storage, then backpressure occurs. Alternatively, the stream manager  134  may monitor a rate the buffered stream data  260  is increasing (or decreasing) or set a threshold that is less than the capacity of the buffer. In the former example, the stream manager  134  monitors the current amount of data stored in the buffer at certain intervals of time. The stream manager  134  may then use this information to determine a rate that specifies whether the usage of the buffer is increasing or decreasing. Moreover, the stream manager  134  may take the average of multiple measured rates to determine whether backpressure is occurring (rather than a temporary increase in data being stored in the buffer). If the rate is above a certain value, the stream manager  134  may assume backpressure is occurring. In the latter example, the stream manager  134  may set a threshold that is less than the capacity of the buffer—e.g., 75% of the total capacity. Nonetheless, in both examples, the stream manager  134  may advantageously detect backpressure before it begins to spread to other upstream processing elements. For example, if the rate at which the amount of data in a buffer is increasing is above 20%, the stream manager  134  may determine, before the buffer reaches maximum capacity, that a processing element is unable to process tuples as fast as it receives them. 
     In one embodiment, the response time of the processing element may be an indicator of backpressure. The stream manager  134  may flag certain tuples and measure the length of time needed for the tuples to be processed and transmitted by a processing element or a group of processing elements (e.g., a job). The stream manager  134  may store the measured response time and compare it to current response times. Backpressure may be occurring if the response times increases beyond a threshold or percentage of the previously measured response times—e.g., a 50% increase in response time. Additionally, the stream manager  134  may require the response time to increase for a certain period of time or for a certain number of flagged tuples to ensure that the longer response times are not because of a temporary slowdown that will not cause constant backpressure (e.g., routine system maintenance may cause the transient time to spike). 
     In one embodiment, the CPU utilization associated with a processing element may be an indicator of backpressure. As mentioned previously, CPU utilization may cause backpressure. Accordingly, the stream manager  134  may monitor the CPU utilization to determine if an associated processing element is experiencing backpressure. For example, if the CPU associated with a processing element is being used around 100% constantly, the stream manager  134  may assume that there is backpressure on any processing elements that use the CPU. 
     Moreover, CPU utilization may indicate future backpressure. For example, referring to  FIG. 5 , if the stream manager receives a request to add Job  3  to the operator graph but determines that the CPU associated with PE 16  is running at or near its maximum capabilities, then the stream manager  134  may assume that adding Job  3 , which transmits additional tuples to PE  16 , will cause backpressure. That is, the CPU utilization percentage may indicate that backpressure is not currently occurring but backpressure is likely to occur if other tasks are assigned to the processing elements associated with the CPU. 
     In one embodiment, the stream manager  134  may record and use historical information  265  as an indicator of future backpressure. The stream manager  134  may record whenever backpressure was previously detected (e.g., by any of the methods discussed above) and save the conditions that caused the backpressure as historical information  265 . If, while monitoring a processing element, the stream manager  134  detects the same conditions saved in the historical information  265 , the stream manager  134  may assume that backpressure will again occur. For example, referring to  FIG. 5 , if the stream manager  134  receives a request to add Job  3  to the operator graph, but the historical information  265  indicates that backpressure occurred last time Job  3  began to send tuples to PE 16 , the stream manager  134  may assume that future backpressure will occur if Job  3  is added. 
     Any combination of the above mentioned indicators of backpressure may be combined in a weighted formula as an indicator of backpressure. For example, the stream manager  134  may monitor both the response time and the buffered stream data  260  to determine whether backpressure is occurring. Moreover, one of ordinary skill in the art will recognize the different indicators that may be monitored to determine whether backpressure is occurring, or will occur, in a stream application. 
     At step  415 , the stream manager  134  determines if there is an indicator of present or future backpressure. If so, the method  400  moves to step  420 . Otherwise, the stream manager  134  returns to step  410  and continues to monitor each processing element  235 . 
     After an indicator of backpressure is detected, the stream manager  134  changes the rate of data flowing through at least one processing element such that the backpressure is alleviated or prevented. The stream manager  134  may change the data rate through a processing element that is not experiencing backpressure or the processing element that is experiencing, or will experience, the backpressure (or both). In one embodiment (illustrated in  FIGS. 6A-6C ), the stream manager  134  changes the flow of data in a processing element that is upstream from the processing element that is experiencing backpressure. In another embodiment (illustrated in  FIGS. 7A and 7C ), changes may be made to processing elements that are downstream of the processing element that is experiencing backpressure. In general, backpressure may be alleviated by reducing the amount of data a processing element receives or by reducing the amount of processing the processing element must perform on the received data (or both). 
       FIGS. 6A-6C  are diagrams illustrating methods of lessening or preventing backpressure, according to embodiments of the invention.  FIG. 6A  is an operator graph that illustrates removing a data path or processing element that is transmitting data to a second processing element. For example, PE 1  may have recently been added to the operator graph which contained PE 2  and PE 3 . However, the addition of PE 1  may cause the stream manger  134  to detect backpressure at PE 3  using one of the indicators discussed above. Accordingly, the stream manager  134  may alleviate the backpressure by removing PE 1  from the operator graph or preventing PE 1  from transmitting data to PE 3  along the data path (indicated by the “X”). In one embodiment, the stream manager  134  may alleviate backpressure in a processing element that is at least two hierarchical levels downstream from the processing element that is removed—i.e., a processing element downstream of PE 3 . By removing PE 1 , for example, the stream manager  134  may also decrease the amount of data flowing to any processing element that is downstream of PE 1 , and thus, any backpressure experienced by that processing element. In this manner, the stream manager  134  may alleviate backpressure by changing the flow of data in a processing element that is different from the processing element experiencing the backpressure. 
     In one embodiment, if the stream manager  134  detects that future backpressure may occur when an additional processing element is included, the stream manager  134  may eliminate a current processing element (or data path) so that future backpressure does not occur when the new processing element is added. That is, removing the current processing element frees at least enough computing resources to process the data supplied by the additional processing element without creating backpressure. 
     Further, each of the data paths and processing elements may be prioritized according to an importance of the processing element or associated job, the amount of data being sent by the processing element, how data is used in downstream processing elements, or by the amount of time needed for a tuple to progress through a processing element or job. 
     Each processing element may be prioritized individually or by its association with a job. For example, a processing element may be part of a job that processes information submitted by a client, and as such, may be given a higher priority than a processing element that performs routine system maintenance that may be delayed to execute at a different time. Alternatively, the priority of each job or processing element may be set by a system administrator. The stream manager  134  may then delete the processing element with the lowest priority. In a similar embodiment, the processing elements may be prioritized according to first-in first-out (FIFO) or last-in first-out (LIFO) algorithms. Accordingly, the first processing element that connected to PE 3  may be the first deleted or the last processing element that connected to PE 3  may be the first deleted. 
     The processing elements may be prioritized based on the amount of data sent on a respective data path. If, for example, PE 1  sends triple the amount of tuples to PE 3  than PE 2 , then PE 1  may be given a higher priority since the most data will not reach PE 3  if PE 1  is eliminated. In another embodiment, PE 1  may be deleted since it is transmitting the most data and is likely the greatest cause of backpressure to a downstream processing element. 
     The processing element may be prioritized based on how that data is used by downstream processing elements. For example, if the data sent on one data path is used by several downstream processing elements, it may be given a greater priority than data on a data path that is used by only one downstream processing element. 
     The processing elements may be prioritized according to their response times necessary to transmit data tuples, for example, the slowest processing element is given the lowest priority. A downstream processing element with a slow response time may be unable to process data as fast as it is received, thus causing backpressure that may spread to upstream processing elements. Accordingly, the stream manager  134  may remove one or more connections to the slowest processing element which may cause backpressure on upstream processing elements. 
     Moreover, any combination of the above mentioned prioritization methods may be combined in a weighted formula. Furthermore, the listed methods of prioritizing processing elements and data paths are exemplary only. Any method that prioritizes processing elements or data paths and is compatible with the functionality described herein is contemplated by this disclosure. 
     The operator graph illustrated in  FIG. 6A  is not limited to two processing elements that send tuples to a single processing element. Instead, there may be only one processing element that transmits data to a second processing element or more than two processing elements transmitting data to a single processing element. Moreover, when there are multiple processing elements connected to a single processing element, more than one processing elements or data paths may be removed to alleviate any backpressure on a processing element farther downstream in the operator graph. 
       FIG. 6B  is an operator graph that illustrates changing, for at least one processing element, the rate that tuples are transmitted along a data path to alleviate backpressure. As shown, PE 4  sends tuples to PE 6  at a rate that is 50% of the original rate and the PE 5  now sends tuples to PE 6  at a rate that is 75% of the original rate. For example, if PE 4  previously sent 100 tuples per second to PE 6 , the stream manager  134  has cut that rate to 50 tuples per second. Though PE 6  receives only a fraction of the data that was intended for it, doing so permits all connections to be maintained and provides PE 6  with at least a sampling of the data. This reduction of transmitted data may alleviate backpressure that is occurring at PE 6  or any processing element that is farther downstream on the operator graph. 
     In one embodiment, the stream manager  134  may reduce data rates on multiple data paths by using a round-robin approach. Specifically, the stream manager  134  may allow PE 4  to send a tuple to PE 6  only after PE 5  has sent a tuple. Again, PE 6  may receive only a sampling of the data. However, each upstream processing element is treated equally and the data flowing to PE 6  may be decreased. Alternatively, the PE 4  and PE 5  and their associated data paths may be prioritized using any of the methods discussed in relation to  FIG. 6A . The data rate may then be changed based on the assigned priority. For example, PE 5  may be part of a client job, and thus, ranked higher than PE 4 . Based on the ranking, the stream manager  134  may then reduce the data rate for PE 5  by only a quarter while reducing the data rate associated with PE 4  by a half. 
     In one embodiment, the stream manger  134  may detect that future backpressure will occur if a job is added. Instead of denying the request, the stream manager  134  may prevent backpressure by changing the rate that current processing elements transmit data. Presumably, the stream manager  134  would change the rates such that enough computing resources are freed to handle the new data that may be transmitted by the new job, and thus, prevent any backpressure. 
     The operator graph illustrated in  FIG. 6B  is not limited to two processing elements that send tuples to a single processing element. Instead, there may be only one processing element that transmits data to a second processing element or more than three processing elements transmitting data. 
       FIG. 6C  is an operator graph that illustrates a method of denying a request for adding a new processing element. The stream manager  134  may deny a request to add a job or processing element to an operator graph based on indicators of future backpressure. Specifically, stream manager  134  may receive a request to add or change the operator graph such that PE 8  transmits tuples to PE 9 . Before changing the operator graph, the stream manager  134  may detect that the change will cause future backpressure in PE 9  or another processing element located farther downstream. An indicator may be that the CPU usage for the CPU  205  associated with PE 9  is at or near maximum utilization, and therefore, sending additional tuples to PE 9  will cause backpressure. Alternatively, the stream manager  134  may have previously added PE 8  to the operator graph which caused backpressure in a downstream processing element. Using this historical information  265 , the stream manager  134  may determine that adding PE 8  will again cause backpressure. The ghosted line  602  indicates that the stream manager  134  denied the request to add the processing element. 
     Although only three processing elements are shown, in another embodiment,  FIG. 6C  may include only one processing element attempting to connect to a second processing element. Additionally, there may be two or more processing element already connected to one processing element before the stream manager  134  receives a request to connect another processing element. 
       FIG. 7A-7C  are diagrams illustrating methods of lessening or preventing backpressure, according to embodiments of the invention.  FIG. 7A , in contrast to  FIG. 6A , is an operator graph illustrating removing a data path from a processing element—PE 1 —that transmits tuples on multiple data paths rather than receiving tuples on multiple data paths. However, both  FIGS. 6A and 7A  illustrate methods of alleviating or preventing backpressure. In  FIG. 7A , at least one of the multiple data paths transmitting tuples from PE 1  may be eliminated to prevent or lessen backpressure. If the backpressure is located at PE 4 , eliminating the data path from PE 1  to PE 3  may lessen the amount of data in the tuples that are sent from PE 3  to PE 4 , or could cause PE 3  to send less tuples to PE 4 . 
     In one embodiment, if the backpressure is at PE 1 , removing PE 3  would lessen the amount of tuples that must be transmitted from PE 1 , thereby permitting PE 1  to increase its data throughput. That is, PE 1  no longer must perform the processing required to transmit tuples to PE 3 . PE 1  may then use the freed computer resources to process the data that it sends on the remaining connection. In this manner, removing a downstream processing element—e.g., PE 3 —may alleviate the backpressure experienced by an upstream processing element—e.g., PE 1 . 
     In one embodiment, if the stream manager  134  detects that future backpressure that may occur when an additional processing element is added, the stream manager  134  may eliminate a current processing element (or data path) so that future backpressure does not occur when the new processing element is added. Stated differently, removing the current processing element frees at least enough computing resources to process the data supplied by the additional processing element without creating backpressure. 
     In another embodiment, each of the data paths may be prioritized using the methods described in the description accompanying  FIG. 6A . In this manner, the lowest priority data path or paths are eliminated first. 
       FIG. 7B  is an operator graph illustrating alleviating backpressure by changing the rate that tuples are transmitted along multiple data paths from a single processing element. Specifically, PE 5  continues to send tuples to PE 6  at a rate that is 100% that of the original rate but sends tuples to PE 7  by a rate that is 25% that of the original rate. Though PE 7  receives only a fraction of the data that was intended for it, doing so permits all connections to be maintained and provides each processing element with at least a sampling of the data. In this manner, changing the data rate may alleviate backpressure that is occurring at PE 6  or any processing element that is farther downstream on the operator graph—e.g., PE 8 . 
     In one embodiment, the stream manager  134  may reduce data rates on multiple data paths by using a round-robin approach. Specifically, the stream manager  134  may allow PE 5  to send a tuple to PE 6  only after PE 5  has sent a tuple to PE 7  and vice versa. Again, PE 6  and PE 7  may receive only a sampling of the data. However, each downstream processing element is treated equally and the data flowing to PE 8 , along with backpressure, may be decreased. 
     In one embodiment, if the backpressure is at PE 5 , changing the rate at which PE 5  sends data to PE 6  or PE 7  would lessen the amount of tuples that must be transmitted from PE 5  and thus permit PE 5  to increase its data throughput. For example, if PE 5  must write to a database (i.e., a time consuming process) before sending a tuple to PE 7 , then reducing the rate that tuples are sent by 75% may also reduce the number of writes to the database by 75%. By transmitting only 25% of the intended data to PE 7 , PE 5  may be able to process data as fast as it is received by discarding a portion of the incoming data. Any incoming data that is discarded does not have to be processed by PE 5 . 
     Further, the multiple data paths associated with PE 5  may be prioritized using any of the methods discussed in relation to  FIG. 6A . The stream manager  134  may then change the data rate based on the assigned priority. 
     In one embodiment, the stream manger  134  may detect that future backpressure will occur if a job is added. Instead of denying the request, the stream manager  134  may prevent backpressure by changing the rate that current processing elements transmit data. For example, the stream manager  134  may change the rates such that enough computing resources are freed to handle the new data that may be transmitted by the new job, and thus, prevent any backpressure. 
       FIG. 7C  is an operator graph that illustrates a method of denying a request for adding a new data path between two processing elements in the operator graph. The stream manager  134  may deny a request to add a new data path to an operator graph based on one or more indicators of future backpressure. Specifically, stream manager  134  may receive a request to add or change the operator graph such that PE 9  transmits tuples to PE 11 . Before changing the operator graph, the stream manager  134  may detect that the change will cause future backpressure in PE 12  or another processing element located downstream. An indicator may be that the CPU utilization for the CPU  205  associated with PE 11  is at, or near, maximum utility, and therefore, sending additional tuples to PE 11  will cause backpressure. Alternatively, the stream manager  134  may have previously added the data path between PE 9  and PE 11  to the operator graph which caused backpressure in a downstream processing element. Using historical information  265 , the stream manager  134  may recognize that adding the data path will again cause backpressure. The ghosted line  702  indicates that the stream manager  134  denied the request to add the data path. 
     In another embodiment, adding PE 11  to the operator graph may cause future backpressure at PE 9 —i.e., a processing element upstream from the element to be added. The historical information  265  or the CPU  205  associated with PE 9  may alert the stream manager  134  that backpressure may occur. By preventing a downstream processing element from being added—i.e., PE 11 —the stream manager  134  may prevent backpressure at an upstream processing element—i.e., PE 9 . 
     Although only two data paths are shown in  FIGS. 7A-7C , a processing element may have only one data path, or more than two data paths, that transmit data and still use the techniques described above. 
     In general, a stream manager  134  monitors each processing element for an indicator of current or future backpressure. Once an indicator is detected, the stream manager  134  may use the backpressure logic module  327  to determine the action (e.g., one of the actions illustrated in  FIGS. 6A-6C and 7A-7C ) that will alleviate or prevent backpressure. The stream manager  134  then performs this action. 
     The flowchart and block diagrams in the Figures illustrate the architecture, functionality and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present invention. In this regard, each block in the flowchart or block diagrams may represent a module, segment or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems that perform the specified functions or acts, or combinations of special purpose hardware and computer instructions. 
     While the foregoing is directed to embodiments of the present invention, other and further embodiments of the invention may be devised without departing from the basic scope thereof, and the scope thereof is determined by the claims that follow.