Patent Publication Number: US-8990636-B2

Title: Detecting and resolving errors within an application

Description:
CROSS-REFERENCE TO RELATED APPLICATIONS 
     This application is a continuation of co-pending U.S. patent application Ser. No. 13/334,399, filed Dec. 22, 2011. The aforementioned related patent application is herein incorporated by reference in its entirety. 
    
    
     BACKGROUND 
     Embodiments of the present invention generally relate to application management. Specifically, the invention relates to detecting and resolving errors occurring within a portion of a software application. 
     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 
     Embodiments described herein provide a method, system and computer program product for managing errors within an application. The method, system and computer program product include monitoring errors occurring in each of a plurality of portions of the application while the application is executing. Additionally, the method, system and computer program product include detecting an error occurring in a first one of the plurality of portions of the application. The method, system and computer program product also include, upon detecting the error occurring in the first portion, determining whether to prevent subsequent executions of the first portion of the application. 
    
    
     
       BRIEF DESCRIPTION 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 computing application, according to one embodiment described herein. 
         FIG. 2  is a more detailed view of the compute node of  FIGS. 1A-1B , according to one embodiment described herein. 
         FIG. 3  is a more detailed view of the server computing system of  FIG. 1 , according to one embodiment described herein. 
         FIGS. 4A-B  illustrate operator graphs of a stream computing application, according to embodiments described herein. 
         FIG. 5  is a flow diagram illustrating a method for managing errors within a stream computing application, according to one embodiment described herein. 
     
    
    
     DETAILED DESCRIPTION 
     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 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 computing application, operators are connected to one another such that data flows from one operator to the next (e.g., over a TCP/IP socket). Scalability is reached by distributing an application across nodes by creating many small executable pieces of code (i.e., processing elements), each of one which contains one or more processing modules (i.e., operators). These processing elements can also be replicated on multiple nodes with load balancing among them. Operators in a stream computing application can be fused together to form a processing element. Additionally, multiple processing elements can be grouped together to form a job. Doing so allows processing elements 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, processing elements can be inserted or removed dynamically from an operator graph representing the flow of data through the stream computing application. 
     One advantage of stream computing 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 to perform various operations on the incoming data, and may dynamically alter the stream computing application by modifying the operators and the order in which they are performed. Additionally, stream computing applications are able to handle large volumes of data. 
     However, because stream computing applications often deal with large volumes of data, the processing of which is spread over multiple processing elements across multiple compute nodes, an operator may need to produce an output faster than it is able. Instead of requiring an operator to generate output data by processing currently received input data, an operator may instead output predetermined data. This predetermined data (or predicted output data) may be based on, for example, an average of the output data that was previously processed and transmitted by the operator. Moreover, the operator may only transmit predicted output data if the previously processed output data falls within an acceptable range. That is, if the previous output data is deterministic. An operator, or data flowing out of the operator, is “deterministic” if the values of the output data can be predicted with some minimum amount of confidence. For example, output data may be predictable or deterministic because a certain input always yields a certain output or because the output data typically has a value within a certain range—e.g., the output values for an operator are within a predefined range 80% of the time. Once the output data is deemed deterministic, using the predicted output data may allow the operator to transmit output data faster, or with less processing, than it otherwise would be able. 
     Moreover, the operator may output predetermined data only if there is a need to limit or stop processing received input data. For example, the stream computing application may be experiencing backpressure. “Backpressure” is a term used to describe one or more operators that are unable to transmit or receive additional data because either their buffer or a buffer associated with a downstream operator is full. In the case of some real-time applications, the operator may trade accuracy for increased data throughput where the time required for data to propagate through the stream computing application is an important factor. 
     One advantage of stream computing is that processing elements can be quickly moved into and out of the operator graph. Generally, operators within the processing elements can generate one or more errors under certain circumstances. For example, a particular operator could generate an exception upon being unable to connect to a remote database. While some errors may occur without affecting the output of an operator, in some circumstances, an operator experiencing errors may be affected to the point that the operator is no longer producing a meaningful result. For instance, consider an operator that enriches incoming tuples of data using data retrieved from a remote database. However, if the remote database is offline or if the operator is otherwise unable to connect to the remote database, the operator may be unable to perform its task of enriching the tuples by retrieving data from the remote database. As each operator consumes some amount of system resources (e.g., CPU cycles, memory, etc.) when executing, such system resources may be wasted when executing an operator that is producing no meaningful results. 
     As such, embodiments provide techniques for managing errors within an application. Embodiments may monitor errors occurring in each of a plurality of portions of the application while the application is executing. For example, embodiments may monitor each operator within the operator graph to detect errors generated by the operator. Additionally, embodiments may detect an error occurring in a first one of the plurality of portions of the application. Generally, an error broadly refers to any exception or error message (or code) generated by a software application. For example, embodiments could detect when a particular operator within the operator graph throws an exception. Upon detecting the error occurring in the first portion of the application, embodiments may determine whether to prevent subsequent executions of the first portion of the application. Continuing the example, upon detecting the particular operator has thrown an exception, embodiments may determine whether to prevent subsequent executions of the operator. If, for instance, embodiments determine that subsequent executions of the operator should be prevented, embodiments may remove the operator from the operator graph, such that no data is routed to or from the removed operator in the stream computing application. 
       FIGS. 1A-1B  illustrate a computing infrastructure configured to execute a stream computing 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  13   01-4 , 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 computing application beginning from one or more source processing elements (PEs) through to one or more sink PEs. 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. 
     Generally, data attributes flow into a source PE of a stream computing application and are processed by that PE. 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 PE where the stream terminates). 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 a 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 computing 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 computing 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 . 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). 
       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 . Of note, because a processing element is a collection of fused operators, it is equally correct to describe the operator graph as execution paths between specific operators, which may include execution paths to different operators within the same processing element.  FIG. 1B  illustrates execution paths between processing elements for the sake of clarity. While a single operator within a processing element may be executed as an independently running process with its own process ID (PID) and memory space, multiple operators may also be fused together into a processing element to run as a single process (with a PID and memory space). In cases where two (or more) operators are running in independent processing elements, inter-process communication may occur using a “transport” (e.g., a network socket, a TCP/IP socket, or shared memory). However, when operators are fused together, the operators within a processing element can use more rapid communication techniques for passing tuples (or other data) between the operators. 
     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 . Of note, although the operators within the processing elements are not shown in  FIG. 1B , in one embodiment the data tuples flow between operators within the processing elements rather than between the processing elements themselves. For example, one or more operators within PE 1  may split data attributes received in a tuple and pass some data attributes to one or more other operators within PE 2 , while passing other data attributes to one or more additional operators within 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  (i.e., from operator(s) within PE 3  to operator(s) within 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 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. 
       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 disk (SSD), or flash memory storage drive, may store non-volatile data. 
     In this example, the memory  225  includes a plurality of processing elements  235 . The processing elements  235  include a collection of operators  240 . As noted above, each operator  240  may provide a small chunk of 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 computing application. In the context of the present disclosure, a plurality of operators  240  may be fused in a processing element  235 , such that all of the operators  240  are encapsulated in a single process running on the compute node  130 . For example, each operator  240  could be implemented as a separate thread, such that all of the operators  240  can be run in parallel within a single process. The processing elements may be on the same compute node  130  or on other compute nodes accessible over the data communications network  120 . Memory  225  may also contain stream connection data (not shown) which 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 computing application, also via TCP/IP sockets (or other inter-process data communication mechanisms). 
     As shown, storage  230  contains buffered stream data  260  and historical data  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 computing 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. Storage also contains historical data  265 , which represents previous errors produced by the various operators  240  within the processing elements  235  in the stream computing application. Such historical data  265  could be used, for instance, to determine whether to prevent subsequent executions of one of the operators  240 . For instance, the historical data  265  could be used to determine a rate of error for a particular one of the operators  240  and, if the determined rate of error exceeds a threshold rate of error, embodiments could determine that subsequent executions of the operator should be prevented. 
       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  335 . The stream manager  134  may use the primary operator graph  335  to route tuples to PEs  235  for processing. The stream manager  134  is configured with a PE management component  340 . Generally, the PE management component  340  is configured to detect and manage errors occurring within the operators  240  of the stream computing application. For instance, the PE management component  340  could determine a particular one of the operators  240  is experiencing problems when the operator throws a particular exception. As another example, the PE management component  340  could monitor an error log for the operator and could determine that the operator is experiencing problems when a particular error message is detected within the error log. Upon detecting that the operator is experiencing problems, the PE management component  340  could determine whether to prevent subsequent executions of the operator. For example, the PE management component  340  could retrieve a plurality of error profiles, each specifying one or more conditions, which, if satisfied, indicate that subsequent executions of the operator satisfying the conditions should be prevent. Upon detecting that a particular exception was thrown, the PE management component  340  could determine whether the particular exception satisfies any of the error profiles. 
     Additionally, the PE management component  340  could consider other errors generated by the operator. For instance, the PE management component  340  could calculate a rate at which the operator has generated errors during a period of time. The PE management component  340  could then determine whether the calculated rate of error satisfies any of the error profiles. For example, one of the error profiles could specify a threshold rate of error and the PE management component  340  could compare the calculated rate of error with the threshold rate of error to determine whether the error profile is satisfied. 
     If the PE management component  340  determines that subsequent executions of the operator should be prevented (e.g., if at least one of the error profiles is satisfied), the PE management component  340  could modify the operator graph  335  to remove the operator, such that no data flows to or from the removed operator within the stream computing application. The PE management component  340  could additionally terminate the problematic operator so that the operator does not wastefully consume system resources once removed from the operator graph  335 . That is, since the operator has been removed from the stream computing application, there is no need to continue to expend system resources to execute the operator. For instance, if the operator is the only operator running within one of the processing elements  235 , the PE management component  340  could terminate the processing element  235  containing the operator in order to free up the system resources consumed by the processing element  235 . As another example, if the operator is one of several operators running within a processing element  235 , the PE management component  340  could terminate only a portion of the processing element  235  that corresponds to the operator. For example, the processing element  235  could be implemented as a process running on a compute node  130 , and each operator  240  could be implemented using one or more threads within the process. If the PE management component  340  determines that a particular operator is problematic and should be removed, the PE management component  340  could then terminate the corresponding one or more threads for the operator within the processing element. Advantageously, doing so avoids wasting system resources on portions of the stream computing application (i.e., operators within the stream computing application) that are no longer producing meaningful results. 
       FIGS. 4A-B  illustrate operator graphs of a stream computing application, according to embodiments described herein. As shown,  FIG. 4A  illustrates an operator graph  400  which includes an operator  410  which generates output tuples that are transmitted to operators  415 ,  425  and  430 . Similarly, the operators  415 ,  425  and  430  generate output tuples which are transmitted to the operator  420 , which in turn generates output tuples which are transmitted to one or more downstream operators  435 . As discussed above, the depicted operators  410 ,  415   420 ,  425  and  430  may reside within one or more processing elements, executing across one or more compute nodes. 
     In the depicted stream computing application, a PE management component  340  could be configured to monitor each of the operators  410 ,  415 ,  420 ,  425  and  430  to detect when an error is generated by a respective one of the operators  410 ,  415 ,  420 ,  425  and  430 . For purposes of the present example, assume that the operator  430  is configured to receive tuples of data from the operator  410  and to enrich the received tuples with data retrieved from a remote database. As shown, these enriched tuples are then transmitted to the operator  420  for further processing. Further, assume that the PE management component  340  determines that the operator  430  has generated an error, indicating that the remote database is unavailable. Upon detecting that the operator  430  has generated the remote database unavailable error, the PE management component  340  could determine that the operator  430  is no longer producing meaningful output for the stream computing application. That is, because the operator&#39;s  430  task is to incorporate data retrieved from the remote database into the incoming tuples but the error indicates that the operator  430  is unable to access the remote database, the operator  430  is unable to perform its task and meaningfully enrich the incoming tuples of data. 
     Accordingly, the PE management component  340  could determine that subsequent executions of the operator  430  should be prevented, as the operator  430  is still consuming system resources (e.g., CPU cycles, memory, etc.) but is no longer producing meaningful output. The PE management component  340  could then remove the operator  430  from the operator graph for the stream computing application, such that tuples of data will no longer flow to and from the operator  430 . An example of this is shown in  FIG. 4B , which depicts a modified operator graph  440  of the stream computing application where the operator  430  has been taken offline in order to prevent subsequent executions of the operator  430 . The operator graph  440  includes the operator  410 , which generates output tuples of data that are transmitted to the operators  415  and  425 . Similarly, the operators  415  and  425  generate output tuples of data which flow to the operator  420 , which in turn generates output tuples that are transmitted to the one or more downstream operators  435 . 
     However, unlike the operator graph  400  depicted in  FIG. 4A , the modified operator graph  440  includes the offline operator  450 , which represents the operator  430  from  FIG. 4A  now removed from the stream computing application. That is, in the depicted embodiment, the PE management component  340  has determined that the operator  430  is no longer producing meaningful output values and accordingly, the PE management component  340  has taken the operator  430  offline (represented by the offline operator  450 ) and removed the operator  430  from the operator graph. As such, tuples of data no longer flow from the operator  410  to the operator  430  or from the operator  430  to the operator  420  in the stream computing application. 
     In determining that the operator  430  is no longer producing meaningful output, the PE management component  340  may consider the type of error that was detected. For instance, the operator  430  could still produce meaningful output even though the operator  430  generated a first type of error, but may not produce meaningful output upon experiencing a second type of error. For example, consider the operator described above that is configured to enrich incoming tuples using data retrieved from a remote database. If the operator generates an error indicating that the remote database is unavailable, such an error may indicate that the operator is no longer able to perform its task of enriching the incoming tuples and thus is no longer producing meaningful output. On the other hand, if the operator generates a second type of error indicating that one of the incoming tuples contained a value outside of a particular range, such an error may not indicate that the operator is unable to produce meaningful output. As such, the PE management component  340  may be configured to consider the type of the error detected in making the determination of whether subsequent executions of an operator should be terminated. 
     The PE management component  340  can also consider the frequency at which the operator is generating errors. Continuing the above example, if the operator generates only a single error indicating the remote database is unavailable, the PE management component  340  could determine that the operator is likely still able to produce meaningful output values. That is, since only a single error was generated, the PE management component  340  could determine that the error relates to a momentary interruption of connectivity between the operator and the remote database but that such an interruption is transitory in nature. On the other hand, if the PE management component  340  determines the operator is generating an error 80% of the time when processing incoming tuples, the PE management component  340  could determine that such a rate of error exceeds a threshold rate of error and thus could determine that subsequent executions of the operator should be prevented. More specifically, the PE management component  340  could determine that since the operator is producing an error a substantial amount of the time and because the operator is not producing any meaningful output data each time the error is generated, the PE management component  340  could determine that subsequent execution of the operator is not worth the cost of the system resources being consumed by the operator. Accordingly, the PE management component  340  could prevent subsequent executions of the operator (e.g., by removing the operator from the operator graph). 
     In addition to removing the problematic operator  430  from the operator graph, the PE management component  340  may terminate any executing software corresponding to the operator  430 . For instance, consider an embodiment where each processing element is implemented in a separate process, and where the operator(s) within each processing element are implemented using a separate one or more threads within the respective process. In such an embodiment, the PE management component  340  could determine whether there are any other operators within the processing element in which the problematic operator  430  is located. If so, the PE management component  340  could terminate the one or more threads corresponding to the problematic operator  430  within the processing element, without disturbing the processing of the other operators within the processing element. On the other hand, if the PE management component  340  determines that the problematic operator  430  is the only operator within the processing element, the PE management component  340  could terminate the entire process for the processing element in order to free up the system resources consumed by the processing element. That is, because the problematic operator is the only operator within the processing element, there may be no need to continue running the processing element without any operators within it. Accordingly, the PE management component  340  could terminate the entire process for the processing element containing the problematic operator. 
       FIG. 5  is a flow diagram illustrating a method for managing errors within a stream computing application, according to one embodiment described herein. As shown, the method  500  begins at step  510 , where a stream computing application is initiated. For instance, such initiation may include executing a separate application instance for each processing element within the stream computing application, with each processing element including one or more operators (e.g., implemented with each using a separate one or more threads within the process for the processing element). As discussed above, the processing elements may be executed across one or more computer systems. 
     Once the stream computing application is initiated, the PE management component  340  begins monitoring operators within the stream computing application to detect when the operators generate errors (step  515 ). As discussed above, such an error broadly represents any exception or error message (or error code) that can be generated by a software application. For instance, to monitor exceptions generated by the operators, the PE management component  340  could be implemented at least in part as a wrapper object for the operator that is configured to catch exceptions generated by the operator. Additionally, the PE management component  340  could be configured to monitor an error log or a database to which the operator outputs error messages. 
     The PE management component  340  then determines whether any errors have been detected (step  520 ). If not, the method  500  returns to step  515 , where the PE management component  340  continues monitoring operators in the stream computing application to detect errors. On the other hand, if the PE management component  340  determines an error has been detected, the PE management component  340  generates a record of the error (step  525 ). For example, the PE management component  340  could log the detected error in a database managed by the PE management component  340 . Such an error record could then be used, for instance, to calculate a rate of error for a particular operator over some period of time. As another example, the error record could be used to determine a total number of errors generated by an operator over some period of time. Such determinations could then be used to determine whether to prevent subsequent executions of the operator. 
     In the depicted example, the PE management component  340  is configured to calculate a rate of error for the operator (step  530 ). Generally, the rate of error is calculated over some period of time. For example, the PE management component  340  could be configured to use a predetermined period of time in calculating the rate of error. In calculating the rate of error, the PE management component  340  may use not only the most recently detected error but could also use any other errors generated by the operator during the period of time. 
     The PE management component  340  then determines whether the calculated error rate for the operator exceeds a threshold rate of error (step  535 ). In other embodiments, the PE management component  340  may be configured to determine whether the total number of errors generated by the operator within a window of time exceeds a threshold amount of errors and, if so, can prevent subsequent executions of the operator. Additionally, as discussed above, the PE management component  340  can also be configured to consider the type of error that was detected. For example, upon detecting that the operator has generated a first type of error, the PE management component  340  could prevent subsequent execution of the operator in order to conserve system resources. Such an embodiment may be advantageous, for instance, when a particularly severe type of error is detected. 
     Returning to the depicted embodiment, if the PE management component  340  determines that the calculated rate of error does not exceed the threshold rate of error, the method  500  returns to step  515 , where the PE management component  340  continues monitoring operators in the stream computing application. On the other hand, if the PE management component  340  determines that the calculated rate of error does exceed the threshold rate of error, the PE management component  340  remotes the operator from the operator graph for the stream computing application (step  540 ). Additionally, the PE management component  340  may also terminate subsequent execution of the removed operator. For example, in an embodiment where each operator is implemented using one or more threads within a processing element process, the PE management component  340  could terminate the one or more threads corresponding to the removed operator. Additionally, if the processing element contains no other operators besides the removed operator, the PE management component  340  could be configured to terminate the process for the processing element in order to further conserve system resources. That is, because the processing element contains no operators once the problematic operator is removed, the processing element may no longer be useful to the stream computing application and thus can be terminated to avoid wasting system resources. 
     Once the problematic operator is removed from the operator graph, the PE management component  340  generates a notification specifying the removed operator (step  545 ). Such a notification could then be, for instance, transmitted to a system administrator of the stream computing application to alert the administrator that the PE management component  340  has removed an operator from the stream computing application. Such a notification may be useful, for instance, as the administrator may be able to correct the problem with the removed operator and then reintroduce the operator into the stream computing application. Once the notification is generated, the method  500  ends. Advantageously, the method  500  allows for operators that are no longer producing meaningful output to be detected and removed from the stream computing application, so as to conserve the system resources used to execute the stream computing application. 
     In the preceding, reference is made to embodiments of the invention. However, 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 preceding 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 above 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 consumed 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 stream computing application configured with a PE management component could execute across one or more nodes within the cloud. The PE management component application could monitor operators within the stream computing application to detect errors generated by the operators. Upon detecting that a first one of the operators has generated an error, the PE management component could determine whether to prevent subsequent executions of the operator and, if so, could modify an operator graph for the stream computing application to prevent data flowing to or from the problematic operator. Doing so provides an enhanced stream computing application which users may access from any computing system attached to a network connected to the cloud (e.g., the Internet). 
     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). 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. Each block of the block diagrams and/or flowchart illustrations, and combinations of blocks in the block diagrams and/or flowchart illustrations, 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.