Patent Publication Number: US-11650982-B2

Title: Automatic selection of precompiled or code-generated operator variants

Description:
TECHNICAL FIELD 
     The subject matter described herein relates to database management, and more particularly, to the generation of query execution plans. 
     BACKGROUND 
     Database management systems have become an integral part of many computer systems. For example, some systems handle hundreds if not thousands of transactions per second. On the other hand, some systems perform very complex multidimensional analysis on data. In both cases, the underlying database may need to handle responses to queries very quickly in order to satisfy systems requirements with respect to transaction time. Given the complexity of these queries and/or their volume, the underlying databases face challenges in order to optimize performance. 
     SUMMARY 
     In one aspect, a method, system, and articles of manufacture, including a computer program product, are provided. A method may include generating a mixed query plan including a first operator selected as a pre-compiled operator; generating the mixed query plan including a second operator selected as operator alternatives, the operator alternatives configuring the second operator as pre-compiled or code-generating alternatives; delaying selection of one of the operator alternatives until additional information regarding the mixed query plan becomes available; generating the mixed query plan including a third operator selected as a code-generating operator; and selecting, given the third operator representing the additional information, one of the operator alternatives to enable execution of the mixed query plan using the selected operator alternative. 
     In some variations, one or more features disclosed herein including the following features may optionally be included in any feasible combination. The query plan optimizer may generate the mixed plan to include the first operator and the second operator. The query plan optimizer may delay selection of one of the operator alternatives, generate the mixed query plan including the third operator, and select, given the third operator one of the operator alternatives. The selected one of the operator alternatives may correspond to a code-generated operator. When the selected one of the operator alternatives corresponds to the code-generated operator, a query plan optimizer may insert glue code into the mixed query plan. The selected one of the operator alternatives may correspond to a pre-compiled operator. The execution engine may execute the mixed query plan using the selected operator alternative. 
     Implementations of the current subject matter can include systems and methods consistent with the present description, including one or more features as described, as well as articles that comprise a tangibly embodied machine-readable medium operable to cause one or more machines (e.g., computers, etc.) to result in operations described herein. Similarly, computer systems are also described that may include one or more processors and one or more memories coupled to the one or more processors. A memory, which can include a computer-readable storage medium, may include, encode, store, or the like one or more programs that cause one or more processors to perform one or more of the operations described herein. Computer implemented methods consistent with one or more implementations of the current subject matter can be implemented by one or more data processors residing in a single computing system or multiple computing systems. Such multiple computing systems can be connected and can exchange data and/or commands or other instructions or the like via one or more connections, including but not limited to a connection over a network (e.g. the Internet, a wireless wide area network, a local area network, a wide area network, a wired network, or the like), via a direct connection between one or more of the multiple computing systems, etc. 
     The details of one or more variations of the subject matter described herein are set forth in the accompanying drawings and the description below. Other features and advantages of the subject matter described herein will be apparent from the description and drawings, and from the claims. While certain features of the currently disclosed subject matter are described for illustrative purposes in relation to an enterprise resource software system or other business software solution or architecture, it should be readily understood that such features are not intended to be limiting. The claims that follow this disclosure are intended to define the scope of the protected subject matter. 
    
    
     
       DESCRIPTION OF DRAWINGS 
       The accompanying drawings, which are incorporated in and constitute a part of this specification, show certain aspects of the subject matter disclosed herein and, together with the description, help explain some of the principles associated with the disclosed implementations. In the drawings, 
         FIG.  1 A  depicts a block diagram of a system, in accordance with some example embodiments; 
         FIG.  1 B ,  FIG.  1 C , and  FIG.  1 D  depict examples of mixed query plans including pre-compiled operators and code-generating operators; 
         FIG.  1 E  and  FIG.  1 F  depict examples of mixed query plans including operator alternatives; 
         FIG.  1 G  depicts a flowchart illustrating a process for handling an operator alternative in a query plan, in accordance with some example embodiments 
         FIG.  2    depicts a query plan, in accordance with some example embodiments; 
         FIG.  3    depicts a flowchart illustrating a process for translating a query plan into corresponding code, in accordance with some example embodiments; and 
         FIG.  4    depicts a block diagram illustrating a computing system, in accordance with some example embodiments. 
     
    
    
     When practical, similar reference numbers denote similar structures, features, or elements. 
     DETAILED DESCRIPTION 
     Database management systems and operations performed on the data managed by a database management system have become increasingly complex. For example, a database management systems (or database for short) can support relatively complex online analytical processing (OLAP, which can perform multi-dimensional analysis) to more straightforward transaction based online transaction processing (OLTP). Moreover, the database may be configured as a row-store database or column store database, each of which may have certain aspects with respect to queries and other operations at the database. For example, the database may encode data using dictionaries, while some databases may not. In addition to these various databases layer differences, the queries performed at a database can comprise a complex sequence of operations in order to generate corresponding responses. To implement the complex sequence, a query execution plan (or query plan for short) may be implemented. The query plan represents a sequence of operations, such as instructions, commands, and/or the like, to access data in the database. The database may also include a query plan optimizer to determine an efficient way to execute the query plan. 
     From an application or client perspective, it can be extremely cumbersome to access databases. For example, an application may need to query different types of databases using complex queries. As a consequence, the application layer in this example would need to be configured to handle the various types of databases and the various query types. Additionally or alternatively, each database may need to process queries from the application into a format and structure that can be handled by the given database. Pushing complex operations and support for a variety of different database types to the application layer may contravene the need to have lighter weight and/or readily deployable applications. On the other hand, pushing complex operations to the database layer where data is stored may draw processing and/or memory resources at the database layer and may thus reduce the performance and response times for queries. 
     An execution engine may be implemented to decouple the application layer from the database layer (e.g., the persistence or storage layer where data including database tables may be stored and/or queried). The execution engine may be separate from the database layer and the client application layer. The execution engine may be configured to receive a query and generate a query plan that includes one or more executable query operations. The execution engine may be further configured to optimize the query plan and compile the query plan by generating executable code corresponding to the query plan. 
     Some of the query operations included in the query plan may be executed by the execution engine itself. For instance, more complex query operations (e.g., rule-based query operations such as joins, projections, and/or the like) may be performed by the execution engine itself. For query operations that are performed by the query execution engine itself, the query engine may perform these query operations while accessing the database layer whenever necessary in order to read, write, update, and/or perform other operations on the data stored and/or persisted at the database layer. Meanwhile, the query plan may also include query operations that are delegated to the database layer. These query operations may be relatively basic query operations including, for example, SQL commands (e.g., reads, writes, scans, and/or the like). For query operations that are delegated to the database layer, the execution engine may receive corresponding responses from the database layer where data is stored and these query operations (e.g., SQL commands such as reads, writes, scans, and/or the like) are performed. 
     The generating of a query plan for a query may include translating the query into code (e.g., in a high-level programming language such as C++, a low-level assembly language such as low level virtual-machine (LLVM) assembly language, and/or other types of code) that can be compiled into machine code. Since the query may include a sequence of query operations, the execution engine may be configured to translate, into corresponding code, each query operation in the sequence of query operations. 
     In some embodiments, the execution engine may be configured to implement a mixed query plan, in which the sequence of query operations may include both code-generating query operations and pre-compiled query operations. The pre-compiled query operations may be associated with existing code (e.g., manually generated code in a high level programming language) that is inserted into an executable query plan during the generating of the query plan. For instance, complex and/or infrequently executed query operations may be implemented as pre-compiled query operations, such as code that has been compiled so that a CPU can execute it. In other words, pre-compiled code may correspond to executable, machine code which executes during runtime to provide the query or operation. By contrast, code-generating query operations may be associated with code which when executed at runtime dynamically generates additional code which may need to be compiled and executed as part of the query plan. For example, executable code when executed at runtime may generate “generated code.” 
     It should be appreciated that the designation of various query operations as pre-compiled and/or code-generating may be dependent on different and/or additional factors, metrics, and/or considerations. For instance, certain query operations may be designated as pre-compiled query operations including, for example, joins, table scans, reads from table columns, query operations on bulk data (e.g., table scans and/or the like which process multiple rows of data at once), and query operations that are not performance critical. Other query operations may be designated as code-generating query operations including, for example, arithmetic calculations, reads from dictionaries, joins with complex predicates (e.g. T1 Join T2 ON T1.X+T2.Y&gt;T2.A*42/T2.B), aggregations on calculated expressions (e.g., SUM(X+Y)), and sorting on calculated expressions. Because code-generating query operations may be tailored to a specific query at runtime, any query operation can be designated as a code-generating query operation in order to avoid the overhead associated with pre-compiled query operations, which have to be generic in order to be suitable for multiple queries. As such, the performance optimization associated with code-generating query operations can be desirable even when the underlying query operation is not complex. 
     The pre-compiled query operations and code-generating query operations may operate on and output different units of data. For instance, code-generating query operations may operate on and output individual rows of data while pre-compiled query operations may operate on and output data chunks that include multiple rows of data (e.g., from a database table). Accordingly, to generate an executable query plan for a query having both code-generating query operations and pre-compiled query operations, a plan compiler may insert, between code for one or more pre-compiled query operations and code for one or more code-generating query operations, adaptor code (also referred to as glue code” configured to decompose data chunks into one or more constituent rows of data and/or recompose rows of data into one or more data chunks. 
     Although an executable query plan may include pre-compiled operators and code-generating operators, a query optimizer may not make the most optimum decision when deciding whether to implement any given operator as a pre-compiled operator or a code-generating operator as further described below with respect to  FIG.  1 C  and  FIG.  1 D . 
     In some embodiments, there is provided operator alternatives which may be inserted into an executable query plan. The operator alternatives may allow for a delay during query plan optimization in the selection of whether a given executable operator should optimally be a pre-compiled operator or a code-generating operators. Before further describing the operator alternatives, a description of an example implementation environment is provided. 
       FIG.  1 A  depicts an example of a system  100  including a database execution engine  150 , in accordance with some example implementations. 
     The system  100  may include one or more user equipment  102 A-N, such as a computer, a smart phone, a tablet, an Internet of Things (IoT) device, and/or other computer or processor-based devices. The user equipment may include a user interface, such as a browser or other application to enable access to one or more applications, database layer(s), and/or databases, to generate queries to one or more databases  190 A-N, and/or to receive responses to those queries. 
     In the example of  FIG.  1 A , the databases  190 A represent the database layer of a database management system where data may be persisted and/or stored in a structured way, and where the data can be queried or operated on using operations including SQL commands or other types of commands/instructions to provide reads, writes, and/or perform other operations. To illustrate by way of an example, user equipment  102 A-N may send a query via an execution engine  150  to the database layer  190 A-B, which may represent a persistence and/or storage layer where database tables may be stored and/or queried. The query may be sent via a connection, such as a wired and/or wireless connection (e.g., the Internet, cellular links, WiFi links, and/or the like). 
     The database execution engine  150  may include a query optimizer  110 , such as a SQL optimizer and/or type of optimizer, to receive at least one query from a user equipment and generate a query plan (which may be optimized) for execution by the execution engine  112 . The query optimizer  110  may receive a request, such as a query, and then form or propose an optimized query plan. The query plan (which may be optimized) may be represented as a so-called “query algebra” or “relational algebra.” For example, SELECT Columns from Table A and columns from Table B, and perform an INNER JOIN on Tables A and B may represent a query received by the database execution engine  150  including the query optimizer  110 . There may be several ways of implementing execution of this query. As such, the query plan may offer hints or propose an optimum query plan with respect to the execution time of the overall query. To optimize a query, the query plan optimizer  110  may obtain one or more costs for the different ways the execution of the query plan can be performed. The costs may be obtained via the execution interface  112 A from a cost function  114 , which responds to the query optimizer  110  with the cost(s) for a given query plan (or portion thereof), and these costs may be in terms of execution time at the database layer  190 A-N, for example. 
     The query optimizer  110  may form an optimized query plan, which may represent query algebra or relational algebra, as noted above. To compile a query plan, the query optimizer  110  may provide the query plan to the query plan compiler  116  to enable compilation of some, if not all, of the code (e.g., for a query plan into machine code). The query plan compiler  116  may compile the optimized query algebra into operations, such as program code and/or any other type of command, operation, object, or instruction. This code may include pre-compiled operations and/or code generating operations. 
     The query plan compiler  116  may generate a query plan by at least translating the query plan into corresponding code. For instance, the query plan compiler  116  may combine the existing code for the pre-compiled query operations  125  with the dynamically generated code for the code-generating query operations  127 . Moreover, because the pre-compiled query operations  125  and the code-generating query operations  127  may operate on and output different units of data, the query plan compiler  116  may insert, as noted, adaptor code (also referred to as glue code) between the code corresponding to the pre-compiled query operations  125  and the code corresponding to the code-generating operations  127 . 
       FIG.  1 B  depicts an example of a query plan  160 A- 160 E. The plan compiler  116  receives the query plan  160 A-E and generates an executable query plan that can be executed by the query execution engine  112  and/or a database layer, such as database  190 A. In this example, the query plan  160 A-E is in the form of relational operators, such as Table Scan  160 A, Read Value IDs for columns A and B  160 B, Read values from dictionaries of A and B  160 C, Project columns A, ““A+B*7”  160 D, and Send result to client  160 E. The query compiler  116  generates an executable query plan having executable operators (which can be executed by the query execution engine  112  or a database layer, such as  190 A,  190 B, etc.). 
     As noted, the query compiler  116  may choose whether a given executable operator is a pre-compiled operator or a code-generating operator. In the  FIG.  1 B  example, the query compiler forms the Table Scan operation  160 A as a pre-compiled operator, such as Table Scan  170 A. Likewise, the query compiler forms the Read Value IDs for columns A and B  160 B as a pre-compiled Read ValueIDs operator  170 B, and Send result to client  160 E as a pre-compiled send result to client operator  170 C. Thus, in this example, pre-compiled operators may be pre-compiled C++ code for example, although the pre-compiled operators may take other forms. 
     In the  FIG.  1 B  example, the query compiler  116  forms the Read values from dictionaries of A and B operator  160 C and Project columns A, ““A+B*7” operators  160 D as code-generating operators  172 A and  172 B. As noted, a code-generating operator generates other code as shown at  172 A-B.  FIG.  1 B  also shows so called “adaptor” or “glue” code  174 A-B generated by the database execution engine  150  framework to allow the generated code to iterate over the rows of the input data chunk  182 B and output data chunk  182 C. 
     The query execution engine  112  (or corresponding database  190 A, etc.) executes the table scan pre-compiled operator  170 A generating data chunk  182 A. Next, the query execution engine  112  executes the Read Value ID pre-compiled operator  170 B and generates data chunk  182 B. However, the next operator is a code-generating operator  172 A, but the compiler has detected that there is now a code-generated operator, inserts the glue code, so that the query compiler output can be executed as part of the full executable plan by the query execution engine. Next, the query execution engine  112  compiles the code-generate operation  160 D and executes it. As shown, the framework inserts during compilation and execution glue or adaptor code  174 A-B to allow reading and/or writing over the rows and/or columns of the tables at  182 B-C. The query execution engine  112  then executes the Send result to client as the pre-compiled operator  170 C, which provides the projection result  182 C to a client device such as user equipment  102 A. 
       FIG.  1 C  depicts a query plan  1110 A-C and the corresponding executable operations  1120 A-B and  1130 . In this example, the query plan compiler  116  as part of optimization has decided to implement the first executable operator as pre-compiled operator  1120 A, the second executable operator as a code-generating operator  1130 , and the third executable operator as a pre-compiled operator  1120 B. At runtime, this execution plan may not be as efficient as a plan including only pre-compiled operators to perform  1110 A-C because the code-generating  1110 B overhead (e.g., compilation before execution) may be too costly when compared to for example a scenario as in  FIG.  1 B  in which 2 code-generating operators  160 C and  160 D are executed sequentially, for example. 
       FIG.  1 D  depicts a query plan  2110 A-C and the corresponding executable operations  2120 A-B and  2130 . In this example, the query plan compiler as part of optimization has decided to implement the first executable operator as code generated operator  2120 A, the second executable operator as a pre-compiled operator  2130 , and the third executable operator as a code-generating operator  2120 B. At runtime however, this execution plan may not be as efficient as a plan including only code-generating operators to perform  2110 A-C because the code-generation overhead (e.g., compilation before execution) may make it more efficient to perform a single compile of 3 code-generating operators (rather than 1 first compile session for  2120 A followed by pre-compiled operator  2130 , and the 2 compile session for  2120 B). Another reason why it is more efficient in this case is that there is less “glue code” executed (e.g., if all three were one code-generated operator, the loop over the input data chunk would only be there one time instead of three). 
     The examples of  FIG.  1 C  and  FIG.  1 D  make clear that a static rule that maps an executable operator to either pre-compiled or code-generating may be less than optimum. 
     As noted, there may be provided operator alternatives, in accordance with some embodiments. The operator alternatives allow for a delay in the selection of an executable operator. 
       FIG.  1 E  depicts a query plan including operator alternatives. In the example of  FIG.  1 E , the query plan  1110 A-C of  FIG.  1 C  is shown. The query plan compiler  116  as part of optimization selects the first executable operator as pre-compiled operator  1120 A, and then the second executable operator is selected to include operator alternatives  1900 A-B. Although it may be possible to provide operator alternatives for every operator, this may be too burdensome and/or inefficient for the query optimizer. Instead, the certain operators may be flagged as candidates for being implemented as operator alternatives. 
     In the  FIG.  1 E  example, the operator alternatives  1900 A-B for materializing the Table “T1.X” are in the form of the pre-compiled code  1900 A and the code-generating operator  1900 B. The operator alternatives allow the database execution engine  150  and in particular the plan compiler  116  to delay deciding on which of the alternative operators to select. In this example, the query plan compiler  116  processes the next operator  1110 C as a pre-compiled operator  1120 B. When it selects the third operator  1110 C as a pre-compiled operator  1120 B, the plan compiler  116  may then decide which is more optimum (e.g., based on a cost function  114 ) and then select one of operator alternatives, the pre-compiled code  1900 A or the code-generating operator  1900 B. For example, the plan compiler  116  may decide that it is more optimum to select the pre-compiled code  1900 A given that the operators before and after are pre-compiled. 
       FIG.  1 F  depicts another example of a query plan including operator alternatives. In the example of  FIG.  1 F , the query plan  2110 A-C of  FIG.  1 D  is shown. In the  FIG.  1 F  example, the query plan compiler  116  as part of optimization selects the first executable operator as code-generating operator  2120 A, and then the second executable operator is selected to be the operator alternatives, such as the pre-compiled code operator  1900 A and a code-generating operator  106 B. As noted, the operator alternatives allow the database execution engine  150  including the plan compiler  116  to delay deciding on which of the alternative operators  1900 A-B to select. In this example, the query plan compiler  116  processes the next operator  2110 C as code-generating operator  2120 B. When it selects the third operator  2110 C as the code-generating operator  2120 B, the plan compiler  116  may then decide which is more optimum (e.g., based on a cost function  114 ) and then select one of operator alternatives, the pre-compiled code  1900 A or the code-generating operator  1900 B. For example, the plan compiler  116  may decide that it is more optimum to select the code-generating operator  1900 B given that the operators before and after are code generated. 
       FIG.  1 G  depicts a flowchart illustrating a process  1400  for a mixed query plan including operator alternatives. 
     At  1402 , a query optimizer generates a mixed query plan including one or more pre-compiled operators, one or more code-generating operators, and an operator alternatives. For example, the query optimizer  110  may generate a mixed query plan, and a portion of the query plan may include one or more of the following: a pre-compiled operator  1120 A ( FIG.  1 E ), operator alternatives  1900 A-B, and a precompiled operator  1120 B, although the mixed query plan may take other forms as well. 
     At  1412 , the query optimizer delays selection of the operator alternatives. For example, the query optimizer  110  delays its decision regarding which is the more optimum of the two operator alternatives  1900 A-B until it has additional information about the query plan. 
     At  415 , the query optimizer selects, for the operator alternative, a corresponding pre-compiled operator or a corresponding code-generating operator to replace the operator alternatives in the mixed query plan. In this example, the query optimizer  110  is sequentially processing the query plan, so when it detects that the third operator in the sequence is a pre-compiled operator  1120 B, the query optimizer may decide (e.g., based on a cost function) that it is more optimum to choose the pre-compiled alternative  1900 A. 
     At  420 , the query optimizer may execute the mixed query plan, For example, the mixed query plan may then be executed via the query execution engine  112 . 
     Referring again to  FIG.  1 A , the query plan compiler  116  may generate a query plan that includes both full table query operations and split table query operations. A full table query operation may operate on tables as a whole because performing the operation may include simultaneously loading, examining and/or altering all of the data in the table. For example, sorting the rows of a table (e.g., a SQL ORDER BY command) and hash joining two or more tables are full table query operations that may be performed on tables as a whole. By contrast, a split table query operation may operate on portions of a table because the performing the operation may include separately loading, examining, and/or altering data from individual portions of the table. For instance, filtering, materialization (e.g., projection), and equipartitioned joins (e.g., between two table partitions) may be split table query operations that can be performed on individual portions of a table and not on the table as a whole. The execution engine may replace a single split table query operation in the query plan with a plurality of parallel operations that each operates on a portion (e.g., partition and/or fragment) of the table. To generate a query plan that includes both full table query operations and split table query operations, the query plan compiler  116  may insert one or more switch operations. A switch operation may be inserted between a full table query operation and a split table query operation. A full table query operation may output a data chunk corresponding to a table in its entirety while a subsequent split table query operation operates on only portions (e.g., partition and/or fragment) of the table. As such, the switch operation may be configured to distribute data from the data chunk output by the full table query operation to each of the parallel operations forming the split table query operation. The query plan compiler may be further configured to compile code in both high-level programming languages (e.g., C++) and low-level assembly language (e.g., low level virtual machine assembly language) into executable code, which may be directly executed by a computer processor and/or processing circuitry (e.g., numerical machine code and/or the like). 
     The database execution engine  150  may further include a plan generator  118  configured to provide, to the query execution engine  112 , the query plan subsequent to compilation by the plan compiler  116 . 
     The query optimizer  110  may be configured to select other execution engines. For example, the query optimizer  110  may select via interface  112 C an execution engine configured specifically to support a row-store database or an ABAP type database, or the query optimizer  110  may select via interface  112 D an execution engine configured specifically to support a column-store type database. In this way, the query optimizer  110  may select whether to use the universal database execution engine  150  or legacy (e.g., database-specific) execution engines (available via interfaces  112 C/D, for example). 
     The query execution engine  112  may receive, from the plan generator  118 , a query plan that has been generated and/or optimized by the query optimizer  110  and compiled by the plan compiler  116 . It should be appreciated that the query execution engine  112  may also receive query plans and/or queries directly from a higher-level application or another device, such as user equipment  102 A-N. The query execution engine  112  may then forward, via an execution interface  112 B, the query plan to a plan execution engine  120 . The plan execution engine  120  may step through the query plan and determine to perform some of the query operations from the query plan within the database execution engine  150  and delegate other query operations for execution at one or more of the database layers  190 A-N. Query operations delegated to the database layers  190 A-N may be sent, to one or more of the database layers  190 A-N, via an execution engine application programming interface (API). To illustrate further, Table 1 below depicts an example of a query execution plan including a (1) TableScan (Filter X=1) and a (2) Materialization (Columns A, B). In this example, the TableScan would result in one or more calls via the execution engine API  199  to one or more of databases  190 A-B. Specifically, the TableScan operation at Table 1 would result in a call for a dictionary look up for a value “X,” an indexvector scan with a valueid obtained from the dictionary look up, which results in a document ID list. Then for each document ID, a call is made to look up the value IDs for columns A and B. The value IDs may be used to look up dictionary values to materialize the columns A and B including the actual data values for those columns. 
     
       
         
           
               
               
             
               
                 TABLE 1 
               
               
                   
               
               
                 Operation 
                 Calls made on Database API 
               
               
                   
               
             
            
               
                 1) TableScan  
                 dictionary lookup column X for the value ID of “1” 
               
               
                 (Filter X = 1) 
                 indexvector scan with a valueid from the lookup, 
               
               
                   
                 which results in a document ID (docid) list that 
               
               
                   
                 identifies one or more rows in Table 1 
               
               
                 2) Materialization  
                 For each docid, lookup value IDs (valueids) for 
               
               
                 (Columns A, B) 
                 columns A + B 
               
               
                   
                 For the valueids, lookup dictionary value in 
               
               
                   
                 dictionaries of A and B 
               
               
                   
               
            
           
         
       
     
     The query execution engine  150  may perform other operations including rule-based operations, such as joins and projections, as well as filtering, group by, multidimensional analysis, and/or the like to reduce the processing burden on the database layer. In this way, the query execution engine  150  may perform these and other complex operations as part of a query plan, while the database&#39;s persistence/storage layer  190 A-N can perform simpler operations to reduce the processing burden at the database&#39;s persistence/storage layer  190 A-N. 
     The query execution engine  150  may provide for a plan execution framework that is able to handle data chunk(s), pipelining, and state management during query execution. Furthermore, the query execution engine  150  may provide the ability to access table storage via an abstract interface to a table adapter, which may reduce dependencies on specific types of storage/persistence layers (which may enable use with different types of storage/persistence layers). 
       FIG.  2    depicts another example of a mixed query plan  200 , in accordance with some example embodiments. Referring to  FIG.  2   , the query plan  200  may correspond to the query algebra or relational algebra that the query optimizer  110  may generate for a particular query. The query plan  200  may include a plurality of consecutive query operations including, for example, a first query operation  210 , a second query operation  212 , a third query operation  214 , a fourth query operation  216 , and a fifth query operation  218 . As an example, the first query operation  210  may be a table scan operation, the second query operation  212  may be a read operation, the third query operation  214  may be a read dictionary operation, the fourth operation  216  may be a mathematical operation (e.g., addition, subtraction, multiplication, division), and the fifth operation  218  may be a send operation (e.g., sending the results of the query plan  200  to one or more user equipment  102 A-N). The query plan  200  reflects the mixed execution model implemented by the database execution engine  150 . As such, the query plan  200  may include both pre-compiled query operations and code-generating query operations. For instance, as shown in  FIG.  2   , the first query operation  210 , the second query operations  212 , and the fifth query operation  218  may be pre-compiled query operations associated with existing code (e.g., manually generated code in a high-level programming language). Meanwhile, the third query operation  214  and the fourth query operation  216  may be code-generating query operations associated with code (e.g., in a low-level assembly language) that is dynamically generated (e.g., during the translation of the query plan  200  by the query plan compiler  116 ). 
     The compiling of the query plan  200  (e.g., by the query plan compiler  116 ) includes translating, in a sequential manner, the query plan  200  into corresponding code. For instance, the query plan compiler  116  may translate the query plan  200  into corresponding code by at least inserting existing or pre-compiled code (e.g., manually generated code in a high level programming language) into the code for the query plan, when the query plan compiler  110  encounters the first query operation  210 , the second query operation  212 , and/or the fifth query operation  218 . Existing code associated with consecutive pre-compiled query operations (e.g., the first query operation  210  and the second query operation  212 ) may be combined to form a continuous segment of code. Alternately and/or additionally, the query plan compiler  116  may translate the query plan  200  into corresponding code by at least triggering the dynamic generation of code (e.g., low level assembly code by a Low Level Virtual Machine (LLVM) compiler), when the query plan compiler  116  encounters the third query operation  214  and/or the fourth query operation  216 . Dynamically generated code associated with consecutive code-generating query operations (e.g., the third query operation  214  and the fourth query operation  216 ) may also be combined to form a continuous segment of code. 
     The translating of the query plan  200  into corresponding code may also include inserting adaptor code between the code for pre-compiled query operations and code-generating query operations. Adaptor code may be code that is configured to convert the output of one query operation into input that may be processed by a subsequent query operation. For instance, adaptor code can be configured to decompose data chunks into one or more constituent rows of data and/or recompose rows of data into one or more data chunks. The query plan compiler  116  may be configured to track the context of the translation being performed by the query plan compiler  116 . The context of the translation may correspond to whether a query operation currently being translated by the query plan compiler  116  requires the query plan compiler  116  to be in a code generating mode. For instance, the query plan compiler  116  may track the context of the translation via a context flag that corresponds to whether the query plan compiler  116  is in a code generating mode. The context flag may be turned on and/or set to a certain value whenever the query plan compiler  116  is translating a code-generating query operation that requires the query plan compiler  116  to be in a code generating mode. Alternately and/or additionally, the context flag may be turned off and/or set to a different value whenever the query plan compiler  116  is translating a pre-compiled query operation that does not require the query plan compiler  116  to be in a code generating mode. The adaptor code may be inserted whenever the query plan compiler  116  detects (e.g., based on the flag) a change in the context of translation between two consecutive query operations. For instance, when the query plan compiler  116  is translating the first query operation  210  and/or the second query operation  212 , the context may indicate that the query plan compiler  110  is not in a code generating mode since both the first query operation  210  and the second query operation  212  are pre-compiled query operations. Thus, the context flag may be turned off during the translation of the first query operation  210  and the second query operation  212 . However, when the query plan compiler  110  is translating the third query  214 , the query plan compiler  110  may determine that translating the third query operation  214  requires the query plan compiler  110  to be in a code generating mode. As such, the query plan compiler  110  may be required to change the context flag to correspond to the change in the context of translation between the second query operation  212  and the third query operation  214 . The query plan compiler  110  may detect, based on the changing of the context flag, a change in context that necessitates the insertion of adaptor code. For example, when the query plan compiler  110  turns on the context flag and/or changes the value of the context flag to indicate a change from a non-code generating mode to a code generating mode, the query plan compiler  110  may insert adaptor code configured to decompose data chunks (e.g., operated on and output by the second query operation  212 ) into one or more constituent rows of data (e.g., that can be operated on by the third query operation  214 ). 
     Table 2 below depicts pseudocode corresponding to adaptor code configured to decompose data chunks. As shown in Table 2, the adaptor code may be configured to iterate over each row in a data chunk output by one or more pre-compiled query operations (e.g., the first query operation  210  and/or the second query operation  212 ) and provide the data at each row individually as input into one or more code-generating query operations (e.g., the third query operation  214  and/or the fourth query operation  216 ). 
     
       
         
           
               
               
             
               
                   
                 TABLE 2 
               
               
                   
                   
               
             
            
               
                   
                 For Row in InputChunk 
               
               
                   
                 { 
               
            
           
           
               
               
            
               
                   
                 vidA = row.column[0] 
               
               
                   
                 vidB = row.column[1] 
               
               
                   
                 valueA = dictionary[vidA] 
               
               
                   
                 valueB = dictionary[vidB] 
               
            
           
           
               
               
            
               
                   
                 } 
               
               
                   
                   
               
            
           
         
       
     
     In some example embodiments, the query plan compiler  110  may detect another change in context when the query plan compiler  110  is translating the fifth query operation  218  subsequent to translating the third query operation  214  and/or fourth query operation  216 . For instance, when the query plan compiler  110  is translating the third query operation  214  and/or the fourth query operation  216 , the context may indicate that query plan compiler  110  is in a code generating because both the third query operation  214  and the fourth query operation  216  are code-generating query operations. When the query plan compiler  110  is translating the fifth query  218 , the query plan compiler  110  may determine that the translating of the fifth query  218  no longer requires the query plan compiler  210  to be in a code generating mode. Accordingly, the query plan compiler  110  may be required to change the context flag to correspond to the change in the context between the fourth query  216  and the fifth query  218 . The query plan compiler  110  may detect, based on the changing of the context flag, another change in context that necessitates the insertion of adaptor code. In particular, when the query plan compiler  110  turns off the context flag and/or changes the value of the context flag to indicate a change from a code generating mode to a non-code generating mode, the query plan compiler  110  may insert adaptor code configured to recompose rows of data (e.g., operated on and output by the fourth query operation  216 ) into data chunks (e.g., that can be operated on by the fifth query operation  218 ). 
     Table 3 below depicts pseudocode corresponding to adaptor code configured to decompose data chunks. As shown in Table 3, the adaptor code may be configured to populate each row in a data chunk (e.g., that can be operated on by the fifth query operation  218 ) with individual rows of data (e.g., operated on and output by the fourth query operation  216 ). 
     
       
         
           
               
               
             
               
                   
                 TABLE 3 
               
               
                   
                   
               
             
            
               
                   
                 For Row in InputChunk 
               
               
                   
                 { 
               
            
           
           
               
               
            
               
                   
                 result = valueA + valueB * 7 
               
               
                   
                 outRow = OutputChunk.addRow( ) 
               
               
                   
                 outRow.column[0] = result 
               
            
           
           
               
               
            
               
                   
                 } 
               
               
                   
                   
               
            
           
         
       
     
       FIG.  3    depicts a flowchart illustrating a process  300  for translating a query plan into corresponding code, in accordance with some example embodiments. Referring to  FIG.  1   - FIG.  3   , the process  300  may be performed by the database execution engine  150 . 
     The database execution engine  150  may translate, into corresponding code, a query operation in a query plan that includes a sequence of query operations ( 302 ). For example, the database execution engine  150  may implement a mixed execution model. As such, the query operation may be a pre-compiled query operation that is associated existing code (e.g., manually generated code in a high-level programming language such as C++ and/or the like) and the database execution engine  150  may translate the query operation by at least inserting pre-compiled operator or code for the query operation into the code for the query plan or, alternately, a a code-generating query operation associated with dynamically generated code (and the database execution engine  150  may translate the query operation by at least triggering the dynamic generation of the corresponding code for the query operation by for example the use of a low-level assembly language compiler). 
     The database execution engine  150  may determine a context for the translating of the query operation ( 304 ). For instance, the database execution engine  150  (e.g., the query plan compiler  110 ) may determine whether the translating of the query operation requires the database execution engine  150  to be in a code generating mode or a non-code generating mode. 
     The database execution engine  150  may not detect, based at least on the context for translating the query operation, a change in context between the translating of the query operation and a context for translating one or more previous query operations in the sequence of query operations ( 305 -N). For example, the database execution engine  150  (e.g., the query plan compiler  110 ) may maintain a context flag in order to track the context for translating the query operation. The database execution engine  150  may turn on the context flag and/or set the context flag to a certain value when the database execution engine  150  is in a code generating mode while translating a code-generating query operation. Alternately, the database execution engine  150  may turn off the context flag and/or set the context flag to a different value when the database execution engine  150  is in a non-code generating mode while translating a pre-compiled query operation. Thus, the database execution engine  150  may detect a change in context based at least on whether the database exertion engine  150  is required to change the on/off state and/or value of the context flag from one or more previous query operations. 
     When the database execution engine  150  does not detect a change in context, the database execution engine  150  may combine code for the query operation with code for the one or more previous query operations to form a continuous segment of code for the query plan ( 306 ). For example, the query operation and the one or more previous query operations may all be code-generating query operations. As such, the database execution engine  150  is not required to change the context flag because the database execution engine  150  remains in the same code generating mode when the database execution engine  150  is translating the query operation as when the database execution engine  150  is translating the one or more previous query operations. Alternately and/or additionally, the query operation and the one or more previous query operations may all be pre-compiled query operations. Here, the database exertion engine  150  is also not required to change the context flag because the database execution engine remains in the same non-code generating mode when the database execution engine  150  is translating the query operation as when the database execution engine  150  is translating the one or more previous query operations. In both scenarios, the database execution engine  150  may combine code for the query operation with code for the one or more previous query operations to form a continuous segment of code for the query plan. 
     Alternately and/or additionally, the database execution engine  150  may detect, based at least on the context for translating the query operation, a change in context between the query operation and one or more previous query operations in the sequence of query operations ( 305 -Y). As such, the database execution engine  150  may insert, based at least on the change in context, adaptor code between the code for the query operation and the code for the one or more previous query operations ( 308 ). For instance, the query operation may be a code-generating query operation that is preceded by one or more pre-compiled query operations. Alternately and/or additionally, the query operation may be a pre-compiled query operation that is preceded by one or more code-generating query operations. Here, the database execution engine  150  (e.g., the query plan compiler  110 ) may be required to change the context flag to reflect a change in context between the translating of the query operation and the one or more previous query operations in the query plan. Accordingly, the database execution engine  150  may insert adaptor code between the code for the query operation and the code for the one or more previous query operations. According to some example embodiments, the adaptor code may be code that is configured to decompose data chunks into one or more constituent rows of data and/or recompose rows of data into one or more data chunks. 
       FIG.  4    depicts a block diagram illustrating a computing system  500  consistent with implementations of the current subject matter. Referring to  FIG.  1    and  FIG.  5   , the computing system  500  can be used to implement the execution engine  150  and/or any components therein. 
     As shown in  FIG.  5   , the computing system  500  can include a processor  510 , a memory  520 , a storage device  530 , and input/output devices  540 . The processor  510 , the memory  520 , the storage device  530 , and the input/output devices  540  can be interconnected via a system bus  550 . The processor  510  is capable of processing instructions for execution within the computing system  500 . Such executed instructions can implement one or more components of, for example, the execution engine  150 . In some implementations of the current subject matter, the processor  510  can be a single-threaded processor. Alternately, the processor  510  can be a multi-threaded processor. The processor  510  is capable of processing instructions stored in the memory  520  and/or on the storage device  530  to display graphical information for a user interface provided via the input/output device  540 . 
     The memory  520  is a computer readable medium such as volatile or non-volatile that stores information within the computing system  500 . The memory  520  can store data structures representing configuration object databases, for example. The storage device  530  is capable of providing persistent storage for the computing system  500 . The storage device  530  can be a floppy disk device, a hard disk device, an optical disk device, or a tape device, or other suitable persistent storage means. The input/output device  540  provides input/output operations for the computing system  500 . In some implementations of the current subject matter, the input/output device  540  includes a keyboard and/or pointing device. In various implementations, the input/output device  540  includes a display unit for displaying graphical user interfaces. 
     According to some implementations of the current subject matter, the input/output device  540  can provide input/output operations for a network device. For example, the input/output device  540  can include Ethernet ports or other networking ports to communicate with one or more wired and/or wireless networks (e.g., a local area network (LAN), a wide area network (WAN), the Internet). 
     In some implementations of the current subject matter, the computing system  500  can be used to execute various interactive computer software applications that can be used for organization, analysis and/or storage of data in various (e.g., tabular) format (e.g., Microsoft Excel®, and/or any other type of software). Alternatively, the computing system  500  can be used to execute any type of software applications. These applications can be used to perform various functionalities, e.g., planning functionalities (e.g., generating, managing, editing of spreadsheet documents, word processing documents, and/or any other objects, etc.), computing functionalities, communications functionalities, etc. The applications can include various add-in functionalities (e.g., SAP Integrated Business Planning add-in for Microsoft Excel as part of the SAP Business Suite, as provided by SAP SE, Walldorf, Germany) or can be standalone computing products and/or functionalities. Upon activation within the applications, the functionalities can be used to generate the user interface provided via the input/output device  540 . The user interface can be generated and presented to a user by the computing system  500  (e.g., on a computer screen monitor, etc.). 
     One or more aspects or features of the subject matter described herein can be realized in digital electronic circuitry, integrated circuitry, specially designed application specific integrated circuits (ASICs), field programmable gate arrays (FPGAs) computer hardware, firmware, software, and/or combinations thereof. These various aspects or features can include implementation in one or more computer programs that are executable and/or interpretable on a programmable system including at least one programmable processor, which can be special or general purpose, coupled to receive data and instructions from, and to transmit data and instructions to, a storage system, at least one input device, and at least one output device. The programmable system or computing system may include clients and servers. A client and server are generally remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other. 
     These computer programs, which can also be referred to as programs, software, software applications, applications, components, or code, include machine instructions for a programmable processor, and can be implemented in a high-level procedural and/or object-oriented programming language, and/or in assembly/machine language. As used herein, the term “machine-readable medium” refers to any computer program product, apparatus and/or device, such as for example magnetic discs, optical disks, memory, and Programmable Logic Devices (PLDs), used to provide machine instructions and/or data to a programmable processor, including a machine-readable medium that receives machine instructions as a machine-readable signal. The term “machine-readable signal” refers to any signal used to provide machine instructions and/or data to a programmable processor. The machine-readable medium can store such machine instructions non-transitorily, such as for example as would a non-transient solid-state memory or a magnetic hard drive or any equivalent storage medium. The machine-readable medium can alternatively or additionally store such machine instructions in a transient manner, such as for example as would a processor cache or other random access memory associated with one or more physical processor cores. 
     To provide for interaction with a user, one or more aspects or features of the subject matter described herein can be implemented on a computer having a display device, such as for example a cathode ray tube (CRT) or a liquid crystal display (LCD) or a light emitting diode (LED) monitor for displaying information to the user and a keyboard and a pointing device, such as for example a mouse or a trackball, by which the user may provide input to the computer. Other kinds of devices can be used to provide for interaction with a user as well. For example, feedback provided to the user can be any form of sensory feedback, such as for example visual feedback, auditory feedback, or tactile feedback; and input from the user may be received in any form, including, but not limited to, acoustic, speech, or tactile input. Other possible input devices include, but are not limited to, touch screens or other touch-sensitive devices such as single or multi-point resistive or capacitive trackpads, voice recognition hardware and software, optical scanners, optical pointers, digital image capture devices and associated interpretation software, and the like. 
     The subject matter described herein can be embodied in systems, apparatus, methods, and/or articles depending on the desired configuration. The implementations set forth in the foregoing description do not represent all implementations consistent with the subject matter described herein. Instead, they are merely some examples consistent with aspects related to the described subject matter. Although a few variations have been described in detail above, other modifications or additions are possible. In particular, further features and/or variations can be provided in addition to those set forth herein. For example, the implementations described above can be directed to various combinations and sub-combinations of the disclosed features and/or combinations and sub-combinations of several further features disclosed above. In addition, the logic flows depicted in the accompanying figures and/or described herein do not necessarily require the particular order shown, or sequential order, to achieve desirable results. Other implementations may be within the scope of the following claims. 
     The illustrated methods are exemplary only. Although the methods are illustrated as having a specific operational flow, two or more operations may be combined into a single operation, a single operation may be performed in two or more separate operations, one or more of the illustrated operations may not be present in various implementations, and/or additional operations which are not illustrated may be part of the methods.