Abstract:
A method for processing a stream of tuples may comprise receiving a stream of tuples to be processed by a plurality of processing elements operating on one or more computer processors. In addition, the method may include generating a model of performance for processing the stream of tuples at runtime, wherein one or more tuples from the stream of tuples potentially cause adverse performance. Further, the method may comprise predicting a parameter for a tuple from the stream of tuples, the parameter indicating a potential for adverse performance, the predicting including using the model. The method may also include modifying processing of the tuple if the parameter falls outside a threshold.

Description:
CROSS-REFERENCE TO RELATED APPLICATIONS 
       [0001]    This application is a continuation of co-pending U.S. patent application Ser. No. 13/672,824, Nov. 9, 2012. The aforementioned related patent application is herein incorporated by reference in its entirety. 
     
    
     TECHNICAL FIELD 
       [0002]    This disclosure generally relates to stream computing, and in particular, to computing applications that receive streaming data and process the data as it is received. 
       BACKGROUND 
       [0003]    Database systems are typically configured to separate the process of storing data from accessing, manipulating, or using data stored in a database. More specifically, database systems use a model in which data is first stored and indexed in a memory before subsequent querying and analysis. In general, database systems may not be well suited for performing real-time processing and analyzing streaming data. In particular, database systems may be unable to store, index, and analyze large amounts of streaming data efficiently or in real time. 
       SUMMARY 
       [0004]    Embodiments of the disclosure provide a method, system, and computer program product for processing data. The method, system, and computer program receive streaming data to be processed by a plurality of processing elements comprising one or more stream operators. 
         [0005]    One embodiment is directed to a method for processing a stream of tuples. The method may comprise receiving a stream of tuples to be processed by a plurality of processing elements operating on one or more computer processors. In addition, the method may include generating a model of performance for processing the stream of tuples at runtime, wherein one or more tuples from the stream of tuples potentially cause adverse performance. Further, the method may comprise predicting a parameter for a tuple from the stream of tuples, the parameter indicating a potential for adverse performance, the predicting including using the model. The method may also include modifying processing of the tuple if the parameter falls outside a threshold. In one embodiment, the parameter indicates actual adverse performance of the tuple. 
         [0006]    Another embodiment is directed to a system for processing a stream of tuples in a stream-based application. 
         [0007]    Yet another embodiment is directed to a computer program product. 
     
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         [0008]      FIG. 1  illustrates a computing infrastructure configured to execute a stream computing application according to various embodiments. 
           [0009]      FIG. 2  illustrates a more detailed view of a compute node of  FIG. 1  according to various embodiments. 
           [0010]      FIG. 3  illustrates a more detailed view of the management system of  FIG. 1  according to various embodiments. 
           [0011]      FIG. 4  illustrates a more detailed view of the compiler system of  FIG. 1  according to various embodiments. 
           [0012]      FIG. 5  illustrates an operator graph for a stream computing application according to various embodiments. 
           [0013]      FIG. 6  illustrates a detection and sorting system for a poison tuple according to various embodiments. 
           [0014]      FIG. 7  illustrates a flow diagram of the detection of sorting system of  FIG. 6  according to various embodiments. 
           [0015]      FIG. 8  illustrates a flow diagram of the alternate execution path of  FIG. 7  according to various embodiments. 
       
    
    
       [0016]    Like reference numbers and designations in the various drawings indicate like elements. 
       DETAILED DESCRIPTION 
       [0017]    Stream-based computing and stream-based database computing are emerging as a developing technology for database systems. Products are available which allow users to create applications that process and query streaming data before it reaches a database file. 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 fractions of a second. Constructing an application using this type of processing has opened up a new programming paradigm that will allow for development of a broad variety of innovative applications, systems, and processes, as well as present new challenges for application programmers and database developers. 
         [0018]    In a stream-based computing application, stream operators are connected to one another such that data flows from one stream operator to the next (e.g., over a TCP/IP socket). Scalability is achieved by distributing an application across nodes by creating executables (i.e., processing elements), as well as replicating processing elements on multiple nodes and load balancing among them. Stream operators in a stream computing application can be fused together to form a processing element that is executable. Doing so allows processing elements to share a common process space, resulting in much faster communication between stream operators than is available using inter-process communication techniques (e.g., using a TCP/IP socket). Further, processing elements can be inserted or removed dynamically from an operator graph representing the flow of data through the stream computing application. 
         [0019]    A “tuple” is data. More specifically, a tuple is a sequence of one or more attributes associated with a thing. Examples of attributes may be any of a variety of different types, e.g., integer, float, Boolean, string, etc. The attributes may be ordered. A tuple may be extended by adding one or more additional attributes to it. In addition to attributes associated with a thing, a tuple may include metadata, i.e., data about the tuple. As used herein, “stream” or “data stream” refers to a sequence of tuples. Generally, a stream may be considered a pseudo-infinite sequence of tuples. 
         [0020]    Stream computing applications handle massive volumes of data that need to be processed efficiently and in real time. For example, a stream computing application may continuously ingest and analyze hundreds of thousands of messages per second and up to petabytes of data per day. Accordingly, each stream operator in a stream computing application may be required to process a received tuple within fractions of a second. 
         [0021]    A poison tuple may be any tuple that has one or more attributes with values that could cause a stream operator to adversely affect the stream operator&#39;s performance, e.g., crashing the system, or slowing system performance, with respect to a particular parameter threshold. Examples of poison tuples may include a tuple with a binary attribute that is corrupt, a tuple that contains a bad URL that points to a slow or unreliable website, or a tuple with sensor data that is significantly inconsistent in consecutive readings, or a tuple that would approach a time parameter threshold. A poison tuple may slow system performance as a result of processing the tuple, for example, on an operator graph. According to various embodiments, a management system may be configured to identify a poison tuple and isolate a tuple from a primary execution path. 
         [0022]    A suspect poison tuple may be a tuple that has one or more attributes that could potentially cause a stream operator to adversely affect the stream operator&#39;s performance, e.g., crashing the system, or slowing system performance, with respect to a particular parameter threshold. An example of a suspect poison tuple may include a tuple without enough data to indicate that it would be a poison tuple but with enough data to indicate that it could slow performance. 
         [0023]      FIG. 1  illustrates one exemplary computing infrastructure  100  that may be configured to execute a stream-based computing application, according to some embodiments. The computing infrastructure  100  includes a management system  105  and two or more compute nodes  110 A- 110 D—i.e., hosts—which are communicatively coupled to each other using one or more communications networks  120 . The communications network  120  may include one or more servers, networks, or databases, and may use a particular communication protocol to transfer data between the compute nodes  110 A- 110 D. A compiler system  102  may be communicatively coupled with the management system  105  and the compute nodes  110  either directly or via the communications network  120 . 
         [0024]      FIG. 2  is a more detailed view of a compute node  110 , which may be the same as one of the compute nodes  110 A- 110 D of  FIG. 1 , according to various embodiments. The compute node  110  may include, without limitation, one or more processors (CPUs)  205 , a network interface  215 , an interconnect  220 , a memory  225 , and a storage  230 . The compute node  110  may also include an I/O device interface  210  used to connect I/O devices  212 , e.g., keyboard, display, and mouse devices, to the compute node  110 . 
         [0025]    Each CPU  205  retrieves and executes programming instructions stored in the memory  225  or storage  230 . 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 device interface  210 , storage  230 , network interface  215 , and memory  225 . The interconnect  220  may be one or more busses. The CPUs  205  may be a single CPU, multiple CPUs, or a single CPU having multiple processing cores in various embodiments. In one embodiment, a processor  205  may be a digital signal processor (DSP). One or more processing elements  235  (described below) may be stored in the memory  225 . A processing element  235  may include one or more stream operators  240  (described below). In one embodiment, a processing element  235  is assigned to be executed by only one CPU  205 , although in other embodiments the stream operators  240  of a processing element  235  may include one or more threads that are executed on two or more CPUs  205 . The memory  225  is generally included to be representative of a random access memory, e.g., Static Random Access Memory (SRAM), Dynamic Random Access Memory (DRAM), or Flash. The storage  230  is generally included to be representative of a non-volatile memory, such as a hard disk drive, solid state device (SSD), or removable memory cards, optical storage, flash memory devices, network attached storage (NAS), or connections to storage area network (SAN) devices, or other devices that may store non-volatile data. The network interface  215  is configured to transmit data via the communications network  120 . 
         [0026]    A streams application may include one or more stream operators  240  that may be compiled into a “processing element” container  235 . The memory  225  may include two or more processing elements  235 , each processing element having one or more stream operators  240 . Each stream operator  240  may include a portion of code that processes tuples flowing into a processing element and outputs tuples to other stream operators  240  in the same processing element, in other processing elements, or in both the same and other processing elements in a stream computing application. Processing elements  235  may pass tuples to other processing elements that are on the same compute node  110  or on other compute nodes that are accessible via communications network  120 . For example, a processing element  235  on compute node  110 A may output tuples to a processing element  235  on compute node  110 B. 
         [0027]    The storage  230  may include a buffer  260 . Although shown as being in storage, the buffer  260  may be located in the memory  225  of the compute node  110  or in a combination of both memories. Moreover, storage  230  may include storage space that is external to the compute node  110 , such as in a cloud. 
         [0028]      FIG. 3  is a more detailed view of the management system  105  of  FIG. 1  according to some embodiments. The management system  105  may include, without limitation, one or more processors (CPUs)  305 , a network interface  315 , an interconnect  320 , a memory  325 , and a storage  330 . The management system  105  may also include an I/O device interface  310  connecting I/O devices  312 , e.g., keyboard, display, and mouse devices, to the management system  105 . 
         [0029]    Each CPU  305  retrieves and executes programming instructions stored in the memory  325  or storage  330 . Similarly, each CPU  305  stores and retrieves application data residing in the memory  325  or storage  330 . The interconnect  320  is used to move data, such as programming instructions and application data, between the CPU  305 , I/O device interface  310 , storage unit  330 , network interface  305 , and memory  325 . The interconnect  320  may be one or more busses. The CPUs  305  may be a single CPU, multiple CPUs, or a single CPU having multiple processing cores in various embodiments. In one embodiment, a processor  305  may be a DSP. Memory  325  is generally included to be representative of a random access memory, e.g., SRAM, DRAM, or Flash. The storage  330  is generally included to be representative of a non-volatile memory, such as a hard disk drive, solid state device (SSD), removable memory cards, optical storage, flash memory devices, network attached storage (NAS), connections to storage area-network (SAN) devices, or the cloud. The network interface  315  is configured to transmit data via the communications network  120 . 
         [0030]    The memory  325  may store a stream manager  134 . The stream manager  134  may be a part of the management system  105 . The stream manager  134  may also contain a tuple management system  340 , which is described further in  FIG. 6 . The tuple management system (TMS)  340  may stand alone or may be a part of a particular compute node. Additionally, the storage  330  may store an operator graph  335 . The operator graph  335  may define how tuples are routed to processing elements  235  ( FIG. 2 ) for processing. 
         [0031]      FIG. 4  is a more detailed view of the compiler system  102  of  FIG. 1  according to some embodiments. The compiler system  102  may include, without limitation, one or more processors (CPUs)  405 , a network interface  415 , an interconnect  420 , a memory  425 , and storage  430 . The compiler system  102  may also include an I/O device interface  410  connecting I/O devices  412 , e.g., keyboard, display, and mouse devices, to the compiler system  102 . 
         [0032]    Each CPU  405  retrieves and executes programming instructions stored in the memory  425  or storage  430 . Similarly, each CPU  405  stores and retrieves application data residing in the memory  425  or storage  430 . The interconnect  420  is used to move data, such as programming instructions and application data, between the CPU  405 , I/O device interface  410 , storage unit  430 , network interface  415 , and memory  425 . The interconnect  420  may be one or more busses. The CPUs  405  may be a single CPU, multiple CPUs, or a single CPU having multiple processing cores in various embodiments. In one embodiment, a processor  405  may be a DSP. Memory  425  is generally included to be representative of a random access memory, e.g., SRAM, DRAM, or Flash. The storage  430  is generally included to be representative of a non-volatile memory, such as a hard disk drive, solid state device (SSD), removable memory cards, optical storage, flash memory devices, network attached storage (NAS), connections to storage area-network (SAN) devices, or to the cloud. The network interface  415  is configured to transmit data via the communications network  120 . 
         [0033]    The memory  425  may store a compiler  136 . The compiler  136  compiles modules, which include source code or statements, into the object code, which includes machine instructions that execute on a processor. In one embodiment, the compiler  136  may translate the modules into an intermediate form before translating the intermediate form into object code. The compiler  136  may output a set of deployable artifacts that may include a set of processing elements and an application description language file (ADL file), which is a configuration file that describes the streaming application. In some embodiments, the compiler  136  may be a just-in-time compiler that executes as part of an interpreter. In other embodiments, the compiler  136  may be an optimizing compiler. In various embodiments, the compiler  136  may perform peephole optimizations, local optimizations, loop optimizations, inter-procedural or whole-program optimizations, machine code optimizations, or any other optimizations that reduce the amount of time required to execute the object code, to reduce the amount of memory required to execute the object code, or both. 
         [0034]    The compiler  136  may also provide the application administrator with the ability to optimize performance through profile-driven fusion optimization. Fusing operators may improve performance by reducing the number of calls to a transport. While fusing stream operators may provide faster communication between operators than is available using inter-process communication techniques, any decision to fuse operators requires balancing the benefits of distributing processing across multiple compute nodes with the benefit of faster inter-operator communications. The compiler  136  may automate the fusion process to determine how to best fuse the operators to be hosted by one or more processing elements, while respecting user-specified constraints. This may be a two-step process, including compiling the application in a profiling mode and running the application, then re-compiling and using the optimizer during this subsequent compilation. The end result may, however, be a compiler-supplied deployable application with an optimized application configuration. 
         [0035]      FIG. 5  illustrates an exemplary operator graph  500  for a stream computing application beginning from one or more sources  135  through to one or more sinks  504 ,  506 , according to some embodiments. This flow from source to sink may also be generally referred to herein as an execution path. Although  FIG. 5  is abstracted to show connected processing elements PE 1 -PE 10 , the operator graph  500  may include data flows between stream operators  240  ( FIG. 2 ) within the same or different processing elements. Typically, processing elements, such as processing element  235  ( FIG. 2 ), receive tuples from the stream as well as output tuples into the stream (except for a sink—where the stream terminates, or a source—where the stream begins). 
         [0036]    The example operator graph shown in  FIG. 5  includes ten processing elements (labeled as PE 1 -PE 10 ) running on the compute nodes  110 A- 110 D. A processing element may include one or more stream operators fused together to form an independently running process with its own process ID (PID) and memory space. In cases where two (or more) processing elements are running independently, inter-process communication may occur using a “transport,” e.g., a network socket, a TCP/IP socket, or shared memory. However, when stream operators are fused together, the fused stream operators can use more rapid communication techniques for passing tuples among stream operators in each processing element. 
         [0037]    The operator graph  500  begins at a source  135  and ends at a sink  504 ,  506 . Compute node  110 A includes the processing elements PE 1 , PE 2 , and PE 3 . Source  135  flows into the processing element PE 1 , which in turn outputs 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 in a new tuple to PE 2 , while passing other data attributes in another new tuple to PE 3 . As a second example, PE 1  may pass some received tuples to PE 2  while passing other tuples to PE 3 . Data that flows to PE 2  is processed by the stream operators contained in PE 2 , and the resulting tuples are then output to PE 4  on compute node  110 B. Likewise, the tuples output by PE 4  flow to operator sink PE 6   504 . Similarly, tuples flowing from PE 3  to PE 5  also reach the operators in sink PE 6   504 . 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 tuples flowing from PE 3  to PE 7  on compute node  110 C, which itself shows tuples flowing to PE 8  and looping back to PE 7 . Tuples output from PE 8  flow to PE 9  on compute node  110 D, which in turn outputs tuples to be processed by operators in a sink processing element, for example PE 10   506 . 
         [0038]    The tuple received by a particular processing element  235  ( FIG. 2 ) is generally not considered to be the same tuple that is output downstream. Typically, the output tuple is changed in some way. An attribute or metadata may be added, deleted, or changed. However, it is not required that the output tuple be changed in some way. Generally, a particular tuple output by a processing element may not be considered to be the same tuple as a corresponding input tuple even if the input tuple is not changed by the processing element. However, to simplify the present description and the claims, an output tuple that has the same data attributes as a corresponding input tuple may be referred to herein as the same tuple. 
         [0039]    Processing elements  235  ( FIG. 2 ) may be configured to receive or output tuples in various formats, e.g., the processing elements or stream operators could exchange data marked up as XML documents. Furthermore, each stream operator  240  within a processing element  235  may be configured to carry out any form of data processing functions on received tuples, 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. 
         [0040]    The stream manager  134  of  FIG. 1  may be configured to monitor a stream computing application running on compute nodes, e.g., compute nodes  110 A- 110 D, as well as to change the deployment of an operator graph, e.g., operator graph  132 . The stream manager  134  may move processing elements from one compute node  110  to another, for example, to manage the processing loads of the compute nodes  110 A- 110 D in the computing infrastructure  100 . Further, stream manager  134  may control the stream computing application by inserting, removing, fusing, un-fusing, or otherwise modifying the processing elements and stream operators (or what tuples flow to the processing elements) running on the compute nodes  110 A- 110 D. One example of a stream computing application is IBM®&#39;s InfoSphere® Streams (note that InfoSphere® is a trademark of International Business Machines Corporation, registered in many jurisdictions worldwide). 
         [0041]    Because a processing element may be a collection of fused stream operators, it is equally correct to describe the operator graph as one or more execution paths between specific stream operators, which may include execution paths to different stream operators within the same processing element.  FIG. 5  illustrates execution paths between processing elements for the sake of clarity. 
         [0042]      FIG. 6  illustrates an operator graph  600  for identifying and routing poison tuples according to an embodiment. The operator graph  600  may correspond to the tuple management system (TMS)  340  on  FIG. 3  which may be part of the stream manager  134 . In some embodiments, the operator graph  600  may be distributed across part or all of the processing elements or compute notes on  FIG. 5 . 
         [0043]    A stream operator  612  may be configured to perform a user-defined function and may be coupled to a tuple management system (TMS)  340  that may reside in the stream manager  134 . The TMS  340  may detect and route poison tuples exclusively or share responsibility with the stream manager  134 . In various embodiments, the TMS  340  may provide instructions to a particular operator that allows the particular operator to detect poison tuples. In other embodiments, the TMS  340  may provide instructions to a particular operator that allows the particular operator to detect potentially adverse, suspect poison tuples. 
         [0044]    In some embodiments, the TMS  340  may be selectively enabled or disabled by a user. Examples of a user may include an application programmer, an end user of a system, or an administrator. In other embodiments, the TMS  340  may be disabled at the stream manager level  134  as a response to a parameter. In some embodiments, an example of a parameter may include a relatively fast processing time from a previously processed tuple that has a limited variance in attributes. In other embodiments, an example of a parameter may include a minimum number of tuples that are processed in the operator graph  600 . In the shown examples, a relatively fast tuple processing time or a lack of tuples processed may limit the need for any tuple bypass paths (discussed below). In other embodiments, a parameter value may also be changed depending on the processing load. For example, if the processing load is relatively low, the parameter value may have a higher limit to allow tuples with a longer processing time. 
         [0045]    In some embodiments, the TMS  340  may also receive a model  616  of possible errors or expected processing times above a threshold of a parameter. The model  616  provides data that allows the TMS  340  to form a prediction regarding which tuples will be potentially adverse or will likely become a poison tuple. An example of a tuple that will be potentially averse may be a sensor that is clearly reporting impossible data like a thermometer that reads 500 degrees Celsius for the outdoor air temperature after readings of 30 degrees Celsius. Although the terms predicting and prediction are used, the TMS  340  may also perform actions such as comparing parameters to form a comparison, evaluating parameters to form an evaluation, forecasting parameters to form a forecast, determining parameters to form a determination, or any combination thereof. It is intended through the use of the terms predicting and prediction to include the scope of such other terminology as well and without limitation. 
         [0046]    The model  616  may be in the form of an XML document or any other format that the TMS  340  may reference to identify or predict a poison tuple or a suspect poison tuple. The model  616  may be in the form of user input parameters, or profile results created by operators from previous tuples with similar characteristics, but other configurations are contemplated. For example, the model  316  may feed into the TMS  340  when processing a tuple that has similar attributes to tuples previously processed by the operator graph  600 . In the shown example, the TMS  340  may compare a predicted value for a parameter, which may be referred to as a prediction, such as processing time, with a threshold value for the parameter that may be established by a user. The predicted value for the parameter may be derived from the model  316  using profiled results from previous tuples, e.g., historical data. The model  316  may also contain the threshold value for the parameter, which may also be referred to as a parameter threshold, that may be established by a user. 
         [0047]    The stream operator  612  may include additional tuple processing functions that may modify the tuples received from source  135 . When the TMS  340  identifies a poison tuple or a suspect poison tuple by comparing against a threshold parameter value (such as in the example above), the stream operator  612  may associate an indication with the tuple. The indication may signal that one or more particular stream operators, e.g., operator  630 , should not process this particular incoming tuple. In some embodiments, the indication may instruct one or more particular stream operators that the tuple is to remain in the data stream without being processed, such as in the bypass execution path  620 , or discarded  622  entirely. In other embodiments, the indication may instruct one or more stream operators that the tuple is to remain in the data stream with limited processing. An example of limited processing may be a situation in which a stream operator may be configured with 2 or more processing operations, and only some of the operations are skipped. The indication may be associated with a tuple by adding or modifying header or trailer information for the tuple, such as, for example, metadata for the tuple. The indication may be a tuple, other than the poison tuple, that is output to the downstream stream operator indicating that the next tuple, next group of tuples, next tuples within a period of time, or combinations thereof, received should be processed, discarded  622 , or sent along the bypass execution path  620 , according to some embodiments. 
         [0048]    In another embodiment, the stream operator  612  may associate an indication with one or more tuples when the requirement is a performance requirement or condition from the TMS  340 . A performance requirement or condition may be, for example, a maximum duration of time within which a stream operator, e.g., stream operator  624 , may be required to complete its processing. A performance requirement or condition may be associated with the model and may be based on historical data. In other embodiments, a performance requirement or condition may be a maximum number of exceptions that can occur, a maximum number of iterations a process may run, or other similar performance-based criteria. 
         [0049]    In an embodiment, once a tuple is output from the “first” stream operator  612 , the tuple may be transmitted to a “second” stream operator in one of three execution paths. The tuple may be transmitted to the primary execution path  618 , and if an indication is present, the bypass execution path  620 , or be discarded  622  in various embodiments. The primary execution path  618  may contain stream operators  624  and  626 . The bypass execution path may contain stream operators  630 , and  632 . Execution paths may, for example, complete different types of processing on a given tuple, or they may process different attributes of a given tuple. In some embodiments, the primary execution path  618  may be the only execution path, but in others, the bypass execution path  620  may be the only execution path. In some embodiments, the operator graph  600  may have more than three execution paths. In other embodiments, the operator graph may have fewer than three execution paths. 
         [0050]    Execution paths  618  and  620  may run independently of each other, but their respective operators may still be hosted on the same compute nodes that support the stream computing application. Stream operators from different execution paths,  618 ,  620 , may share the same network interface  215 , memory  225 , or CPU  205 , of the compute node  110  as shown in  FIG. 2 . In another embodiment, a stream operator on the operator graph  600  may be configured to further communicate with the TMS  340 . 
         [0051]    According to some embodiments, an input data stream may be received by the “first” stream operator  612  from the source  135 . If the operator  612  does not detect a poison tuple or a tuple suspected of being poison, then the data stream may transmit an output tuple to the primary execution path  618 . Stream operators,  624 ,  626 , may process the tuples from the input data stream. In the shown embodiment, all operators may contain stream management code which may transmit performance data to the TMS  340 . In other embodiments, for example, the performance data may be transmitted to the stream operator  612 , directly to the model  616 , or other locations where performance data may be recorded. The TMS  340  may incorporate the performance data into the model  616  in order to identify poison tuples. For example, if the model  616  indicates that a tuple processes too slowly, either operator  624  or  626  may transmit the tuple to the alternate execution path  620  or discard  622  the tuple depending on a threshold programmed by a particular user. 
         [0052]    The TMS  340  may detect poison tuples using the model  616 , which may produce a requirement for the operator  612  to associate an indication. If the TMS  340  detects a poison tuple, then the tuple may be transmitted to the alternate execution path  618  or discarded  622 , according to the associated indication. The requirements may be based upon a parameter. The parameter may be a projected processing time, a time limit, a processing load including availability of other resources, the perceived importance of the data, or whether a tuple may cause a fatal error, but other configurations are contemplated. The requirements may also be user defined. For example, if the processing time at a particular stream operator along execution path  618  is 1 ms, a user may want to discard  622  any tuples that take over 4 ms to process. Alternatively, in this example, a user may want to process tuples that take between 1 ms and 4 ms along the alternate execution path  620 . 
         [0053]    The alternate execution path  620  may receive output tuples from stream operator  612 . The alternate execution path  620  may include operators  630  and  632 . The output of stream operator  630  may become the input for stream operator  632 . Stream operator  630  may refer to the TMS  340  to determine whether a requirement exists for one or more particular stream operators to output the particular tuple without processing it. The output tuple from operator  632  may be transmitted to the sink  634 . 
         [0054]    Poison tuples may also be rehabilitated. During rehabilitation, certain attributes of a poison tuple may be discarded by either operator  630  or  632 . Attributes of a poison tuple may be further analyzed by the stream operator  612 . For example, a photograph of a license plate may be enhanced by a particular stream operator to transmit to a particular transcription stream operator. In this instance, stream operator  630  or stream operator  632  may further transmit a rehabilitated poison tuple to the primary execution path  618 . 
         [0055]    In another embodiment, the TMS  340  may determine that the tuple from stream operator  630  may cause a fatal error or otherwise cannot be processed and may discard the tuple. A discarded tuple may be further transmitted to a sink  640 . The sink  640  may, for example, write the tuple values to a memory in some embodiments. A user may be able to take action on the discarded tuple or list of discarded tuples. 
         [0056]      FIG. 7  illustrates a method  700  for detecting and sorting poison tuples in a streaming application. For purposes of illustration, an example of a tollbooth tracking cars and people will be used throughout the discussion of the embodiment. The method  700  begins at operation  702 . At operation  702 , a stream operator  612  may receive the tuple. The stream operator  612  may refer to the TMS  340  to determine if the tuple is a poison tuple or a suspect poison tuple. The stream operator  612  may be a stream operator that may reside on the compute node, e.g., compute notes  110 . For example, in the tollbooth illustration, if the user wants to determine how many cars other than red cars pass through a particular tollbooth, the user may write this as a function of the stream operator  612 . Once the tuple is received, the stream operator  612  may look for a model  616  in operation  704 . The model  616  may be a process or library that the TMS  340  may use to predict which tuples may be suspect poison tuple. The model  616  may also contain a predictive processor, in other embodiments, the model  616  may be a predictive data matrix. 
         [0057]    If there is no model  616 , or if the model  616  does not indicate a poison tuple, a tuple may proceed to operation  706 . In operation  706 , the tuple may be processed along a primary execution path, e.g., primary execution path  618 . The results may also be added to the model  616  in operation  708 . In the tollbooth example, the TMS  340  may determine that the license plate attribute for a particular car may have taken longer to process through a stream operator than other tuples because it was not supported in the database. In this example, the performance data for the tuple with the unsupported license plate may be transmitted to the TMS  340  and may be added to the model  616 . In another embodiment, the performance data may be added to the model  616  directly. 
         [0058]    In another embodiment, if there is no model  616 , the TMS  340  may simulate performance data. According to various embodiments, the TMS  340  may simulate performance data by processing the tuple along the primary execution path  618  in a limited processing configuration, for example, by skipping processing by some operators. The TMS  340  may compile performance data from the operators used to process the tuple into the model  616  or may use the simulation to directly predict how the tuple will perform. The tuple may be transmitted to the sink  628  or reused in the operator graph  600  according to some embodiments. 
         [0059]    If there is a model  616 , the stream operator  612  may further determine if the tuple is a suspect poison tuple in operation  710 . A tuple may be a suspect poison tuple if the attributes cause the processing element  610  to predict potentially adverse performance such as slower processing time compared to other tuples for the primary execution path  618 . The TMS  340  may access the data from the model  616  to make a prediction of a parameter. The parameter may be a projected processing time, a time limit, a processing load, or whether the tuple would cause a fatal error but other configurations are contemplated. In the tollbooth illustration, for example, if a license plate from New York were scanned, the predictive data matrix may automatically indicate that standard license plates within the United States have had no prior issues and forward the tuple to operation  706 . In another embodiment, the predictive data matrix may perform predicting functions based on historical data or user selected criteria. For example, in the tollbooth illustration, the predictive data matrix may note that there is a particular probability that a tuple containing a car with an unrecognized license plate, such as a vanity license plate or a dealer license plate, will slow down processing of the operator graph  600  based on all the cars containing unrecognized license plates. In the same example, the predictive data matrix may note that the particular probability is based on a small sample size and the user may choose to disregard the prediction. 
         [0060]    Alternatively, the stream operator  612  may indicate that the tuple is poison, e.g., a car with an unrecognized license plate. The stream operator  612  may further determine if the tuple should be sent to operation  712  or operation  714 . The decision to send the tuple to operation  712  or operation  714  may depend on parameters. For example, an operator may have a phonetic alphabet recognition algorithm but not a logographic, i.e., languages that use symbols, character recognition algorithm. Therefore, the stream operator  612  may transmit the unrecognized license plate with the phonetic characters to operation  714  but a logographic license plate may be sent to operation  712 . 
         [0061]      FIG. 8  illustrates operation  714  in  FIG. 7  when a tuple may be transmitted to a bypass execution path, e.g., the bypass execution path  620 . In operation  810 , a suspect poison tuple is received by a stream operator  630 . The stream operator  630  may further examine if the tuple may negatively impact performance in operation  812 . In another embodiment, the TMS  340  may determine whether a suspect poison tuple negatively impacts system performance. Using the tollbooth example as an illustration, if tuples containing dealer license plates typically have an average processing time of 12 ms and tuples containing standard US license plates have an average processing time of 2 ms, then the stream operator  630  may predict that a tuple containing a dealer license plate may have a processing time of 12 ms and route to another stream operator, such as stream operator  632 . In operation  814 , a stream operator, e.g., stream operator  632 , may try to rehabilitate a tuple with attribute of the dealer license plate by identifying the characters on the license plate along with an indication. The indication may note that the license plate is unrecognized and may bypass a particular operator without processing it further. The indication may also associate an image file of the license plate attribute and transmit the rehabilitated tuple to operator  624 . 
         [0062]    If a tuple is unrecognizable, such as a license plate that is blurry in the previously mentioned example, then the tuple may proceed according to operation  816 . In operation  816 , the tuple may be removed from the stream operator  630  and transmitted to stream operator  622 . 
         [0063]    In operation  812 , the stream operator, such as stream operator  630 , may determine that the tuple may not negatively impact performance of the operator graph  600  by further analysis. For example, if a license plate in the prior example was photographed at a wrong visual angle, then the stream operator  630  may determine that the stream operator  624  may be able to transcribe the license plate number using a transcription capability even though the license plate may be at a different visual angle. 
         [0064]    In the foregoing, reference is made to various embodiments. It should be understood, however, that this disclosure is not limited to the specifically described embodiments. Instead, any combination of the described features and elements, whether related to different embodiments or not, is contemplated to implement and practice this disclosure. Furthermore, although embodiments of this disclosure may achieve advantages over other possible solutions or over the prior art, whether or not a particular advantage is achieved by a given embodiment is not limiting of this disclosure. Thus, the described 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). 
         [0065]    As will be appreciated by one skilled in the art, aspects of the present disclosure may be embodied as a system, method, or computer program product. Accordingly, aspects of the present disclosure 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 disclosure 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. 
         [0066]    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 thereof. 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 thereof. In the context of this disclosure, 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. 
         [0067]    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. 
         [0068]    Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to wireless, wire line, optical fiber cable, RF, etc., or any suitable combination thereof. 
         [0069]    Computer program code for carrying out operations for aspects of the present disclosure may be written in any combination of one or more programming languages, including: (a) an object oriented programming language such as Java, Smalltalk, C++, or the like; (b) conventional procedural programming languages, such as the “C” programming language or similar programming languages; and (c) a streams programming language, such as IBM Streams Processing Language (SPL). The program code may execute as specifically described herein. In addition, 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). 
         [0070]    Aspects of the present disclosure have been described with reference to flowchart illustrations, block diagrams, or both, of methods, apparatuses (systems), and computer program products according to embodiments of this disclosure. It will be understood that each block of the flowchart illustrations or block diagrams, and combinations of blocks in the flowchart illustrations 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 or acts specified in the flowchart or block diagram block or blocks. 
         [0071]    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 or act specified in the flowchart or block diagram block or blocks. 
         [0072]    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 or acts specified in the flowchart or block diagram block or blocks. 
         [0073]    Embodiments according to this disclosure 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. 
         [0074]    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 disclosure, a user may access applications or related data available in the cloud. For example, the nodes used to create a stream computing 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). 
         [0075]    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 disclosure. 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 or flowchart illustration, and combinations of blocks in the block diagrams 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. 
         [0076]    Although embodiments are described within the context of a stream computing 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. 
         [0077]    While the foregoing is directed to exemplary embodiments, 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.