CN109033209A - Spark storing process processing method and processing device - Google Patents
Spark storing process processing method and processing device Download PDFInfo
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- CN109033209A CN109033209A CN201810701423.4A CN201810701423A CN109033209A CN 109033209 A CN109033209 A CN 109033209A CN 201810701423 A CN201810701423 A CN 201810701423A CN 109033209 A CN109033209 A CN 109033209A
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Abstract
This disclosure relates to Spark storing process processing method and processing device, the end Driver applied to Spark, the described method includes: obtaining the corresponding sentence collection of Spark storing process, the sentence collection includes operation SQL statement and logic control sentence, the operation SQL statement includes query SQL sentence, and the logic control sentence includes vernier processing sentence；The first logic plan is generated after the query SQL sentence and vernier processing sentence are merged；The first logic plan is sent to working node WorkerNode cluster to execute.By the way that vernier treatment process is abstracted as a logic plan, and it is distributed to the execution of WorkerNode cluster, the end Driver load pressure can reduce according to the Spark storing process processing method and processing device of the embodiment of the present disclosure.
Description
Technical field
This disclosure relates to big data technical field more particularly to a kind of Spark storing process processing method and processing device.
Background technique
SparkSQL is the distributed SQL engine based on Spark.One SQL statement can pass through morphology in SparkSQL
SQL statement is switched to the logic plan that can be executed in a distributed manner, and transfers to WorkerNode (work by the stages such as parsing, syntax parsing
Make node) cluster execution.
Spark storing process can be used to implement specific function, be combined by operation SQL statement and logic control sentence and
At sentence collection.Wherein, operation SQL statement includes inquiry (Select) SQL statement and insertion (Insert) SQL statement, logic
Control statement may include that assignment statement, condition judge sentence and Do statement etc., these sentences are SQL statement or non-SQL language
Sentence.When executing Spark storing process, the end Driver (driver) of Spark will start a PL/SQL (Procedural
Language/SQL, proceduring SQL statement) engine.
In the related technology, in PL/SQL engine to storing process in the process of processing, for non-SQL statement, then directly
It connects and is performed locally the logic control sentence；For SQL statement, then logic plan is generated, and the logic plan of generation is transferred to
WorkerNode cluster executes.WorkerNode cluster can be executed the knot of above-mentioned logic plan by the PL/SQL engine at the end Driver
Fruit pulls to local, then executes the vernier in logic control sentence to the result and handles sentence, leads to the negative of the end Driver
It carries larger.
Summary of the invention
In view of this, can reduce the end Driver the present disclosure proposes a kind of Spark storing process processing method and processing device
Load pressure.
According to the one side of the disclosure, a kind of Spark storing process processing method is provided, applied to Spark's
The end Driver, which comprises obtain the corresponding sentence collection of Spark storing process, the sentence collection includes operation SQL statement
With logic control sentence, the operation SQL statement includes query SQL sentence, and the logic control sentence includes vernier processing language
Sentence；The first logic plan is generated after the query SQL sentence and vernier processing sentence are merged；Described first is patrolled
The plan of collecting is sent to the execution of working node WorkerNode cluster.
According to another aspect of the present disclosure, a kind of Spark storing process processing unit is provided, applied to Spark's
The end Driver, described device include: acquisition module, for obtaining the corresponding sentence collection of Spark storing process, the sentence Ji Bao
Operation SQL statement and logic control sentence are included, the operation SQL statement includes query SQL sentence, the logic control sentence packet
Include vernier processing sentence；First generation module, after merging the query SQL sentence and vernier processing sentence
Generate the first logic plan；First sending module, for the first logic plan to be sent to working node WorkerNode
Cluster executes.
By obtaining the corresponding sentence collection of Spark storing process；The query SQL sentence and vernier that sentence is concentrated handle language
Sentence generates the first logic plan after merging；The first logic plan is sent to WorkerNode cluster to execute, according to
Vernier treatment process can be abstracted as one and patrolled by the Spark storing process processing method and processing device of various aspects of the present disclosure embodiment
Plan is collected, and is distributed to the execution of WorkerNode cluster, reduces the end Driver load pressure.
According to below with reference to the accompanying drawings to detailed description of illustrative embodiments, the other feature and aspect of the disclosure will become
It is clear.
Detailed description of the invention
Comprising in the description and constituting the attached drawing of part of specification and specification together illustrates the disclosure
Exemplary embodiment, feature and aspect, and for explaining the principles of this disclosure.
Fig. 1 shows the flow chart of the Spark storing process processing method according to one embodiment of the disclosure.
Fig. 2 shows the schematic diagrames according to the Spark framework of one embodiment of the disclosure.
Fig. 3 shows the flow chart of the Spark storing process processing method according to one embodiment of the disclosure.
Fig. 4 shows the flow chart of the Spark storing process processing method according to one embodiment of the disclosure.
One of the corresponding sentence collection of Spark storing process according to one embodiment of the disclosure is shown respectively in Fig. 5 a and Fig. 5 b
Example
Fig. 6 shows the block diagram of the Spark storing process processing unit according to one embodiment of the disclosure.
Fig. 7 shows the block diagram of the Spark storing process processing unit according to one embodiment of the disclosure.
Fig. 8 is a kind of block diagram of device for the processing of Spark storing process shown according to an exemplary embodiment.
Specific embodiment
Various exemplary embodiments, feature and the aspect of the disclosure are described in detail below with reference to attached drawing.It is identical in attached drawing
Appended drawing reference indicate element functionally identical or similar.Although the various aspects of embodiment are shown in the attached drawings, remove
It non-specifically points out, it is not necessary to attached drawing drawn to scale.
Dedicated word " exemplary " means " being used as example, embodiment or illustrative " herein.Here as " exemplary "
Illustrated any embodiment should not necessarily be construed as preferred or advantageous over other embodiments.
In addition, giving numerous details in specific embodiment below to better illustrate the disclosure.
It will be appreciated by those skilled in the art that without certain details, the disclosure equally be can be implemented.In some instances, for
Method, means, element and circuit well known to those skilled in the art are not described in detail, in order to highlight the purport of the disclosure.
Fig. 1 shows the flow chart of the Spark storing process processing method according to one embodiment of the disclosure.Fig. 2 shows bases
The schematic diagram of the Spark framework of one embodiment of the disclosure.As shown in Fig. 2, including Driver and WorkerNode in Spark framework
(working node) cluster.Wherein, Driver is responsible for being scheduled application program, is managed to task (task) distribution etc..
The PL/SQL engine at the end Driver is responsible for handling the corresponding sentence collection of storing process.WorkerNode in cluster is responsible for
It creates Executor (actuator), resource and task is further distributed to Executor.Wherein, Executor is working node
On process, be responsible for the distributing to the process of the task is handled.
The process flow of one SQL statement includes morphology parsing, syntax parsing, optimization, policy development and execution.SQL language
Logic plan can be generated by morphology parsing, syntax parsing, optimization and policy development in sentence, generates the process of logic plan by scheming
The end Driver shown in 2 executes.Into after the execution stage, logic plan can be distributed in WorkerNode shown in Fig. 2
Executor is executed.
Spark storing process processing method shown in FIG. 1 can be applied to the end Driver shown in Fig. 2 (end Driver
PL/SQL engine).As shown in Figure 1, the Spark storing process processing method may include:
Step S11, obtains the corresponding sentence collection of Spark storing process, and the sentence collection includes operation SQL statement and logic
Control statement, the operation SQL statement includes query SQL sentence, and the logic control sentence includes vernier processing sentence.
Storing process can be used to implement the specific function of user's needs, such as be inserted into tables of data data, according to giving
Data etc. in the formula that fixes output data table.The corresponding sentence set representations of storing process realize the collection for the sentence that specific function needs
It closes.The corresponding sentence collection of storing process may include operation SQL statement and logic control sentence.Wherein, operation SQL statement includes
Query SQL sentence, logic control sentence include vernier processing sentence.
Operation SQL statement can be used to indicate that the SQL statement operated to the data in database, such as query SQL
Sentence, insertion SQL statement etc..Wherein, the result set of query SQL sentence can be handled by vernier.Result set is to execute
The set of the All Datarows obtained after query SQL sentence.Vernier is a kind of mechanism of processing result collection, by vernier
Operation can read the data of each row in result set.Vernier can will read result and be assigned to its dependent variable.
Logic control sentence can indicate that sentence concentrates the sentence in addition to operating SQL statement.It is wrapped in logic control sentence
Include vernier processing sentence.Vernier processing sentence can indicate that the language of query SQL statement result collection can be handled by specifying variable
Sentence, wherein specifying variable can be used for storing by vernier read from the result set of query SQL sentence as a result, i.e. vernier
Specifying variable can will be assigned to from a line result of the reading of the result set of query SQL sentence.In a kind of possible realization side
In formula, vernier, which handles sentence, can be located inside loop body and/or in the cycling condition of loop body.Wherein, loop body can be
While loop body, in loop loop body etc., with no restrictions to this disclosure.
In one possible implementation, vernier processing sentence may include: the language using specifying variable as filter condition
Sentence and/or the sentence that specifying variable is handled.It can be SQL statement that vernier, which handles sentence, or non-SQL statement.
It in one example, can be query SQL sentence, insertion SQL statement by the sentence of filter condition of specifying variable
Deng, such as Insert into table1 (select*from table2 where table2.id=v_id), wherein
Table2.id is the id of table table2, and v_id is specifying variable.In one example, sentence specifying variable handled
Can be includes sentencing sentence, assignment statement, operation (for example, addition subtraction multiplication and division etc.) sentence etc. using specifying variable as condition.
Step S12 generates the first logic meter after merging the query SQL sentence and vernier processing sentence
It draws.
Query SQL sentence provides result set for vernier processing, and vernier processing sentence is that certain a line result mentions in result set
Data processing method is supplied, query SQL sentence and vernier processing sentence realize the processing of result set jointly.Therefore, it can incite somebody to action
Query SQL sentence and vernier processing sentence merge, and generate the first logic plan based on amalgamation result.
The first logic plan is sent in WorkerNode cluster and executes by step S13.
In the related technology, SQL statement can be generated logic plan by the PL/SQL engine at the end Driver, by the logic plan
Whole implementing results are pulled to local, are then successively handled by vernier every implementing result, in this way by implementing result
Pull and locally execute, be equivalent to single node processing, cluster resource, which is unable to get, to be made full use of, cause the load at the end Driver compared with
Big and lower execution efficiency problem.
In the embodiments of the present disclosure, by the way that vernier treatment process is abstracted as a logic plan, in WorkerNode collection
The logic plan is executed in group, realizes in WorkerNode cluster through processing of the vernier to implementing result, can fill in this way
Divide and utilize cluster resource, had not only reduced the load of the end Driver, but also improve the execution efficiency of Spark storing process.
Fig. 3 shows the flow chart of the Spark storing process processing method according to one embodiment of the disclosure.As shown in figure 3, should
Spark storing process processing method further include:
Step S14, for sentence concentration each of in addition to the vernier handles sentence and the query SQL sentence
Sentence generates the second logic plan based on the SQL statement if the sentence is SQL statement.
The second logic plan is sent in the WorkerNode cluster and executes by step S15.
Sentence concentrates the sentence in addition to the vernier handles sentence and the query SQL sentence to can be SQL statement, example
Such as Select into sentence (sentence only returns to data line, and is assigned to some variable), Insert sentence etc..These SQL
Vernier progress circular treatment is not usually required in sentence can be at local (end Driver) to each SQL for these SQL statements
Sentence carries out morphology parsing, syntax parsing, optimization and policy development, generates logic plan, and the logic plan of generation is distributed
It is executed to the Executor in WorkerNode shown in Fig. 2.
Fig. 4 shows the flow chart of the Spark storing process processing method according to one embodiment of the disclosure.As shown in figure 4, should
Spark storing process processing method further include:
Step S16, for sentence concentration each of in addition to the vernier handles sentence and the operation SQL statement
Sentence is performed locally the non-SQL statement if the sentence is non-SQL statement.
Sentence is concentrated can be with right and wrong SQL statement, such as filtering rod except vernier processing sentence and the query SQL sentence
Part, assignment statement, arithmetic statement, display statement etc..It does not include vernier in these non-SQL statements, for these non-SQL statements,
It can directly be executed at local (end Driver).
Using example
One of the corresponding sentence collection of Spark storing process according to one embodiment of the disclosure is shown respectively in Fig. 5 a and Fig. 5 b
Example.
Example 1, the corresponding sentence collection of Spark storing process as shown in Figure 5 a.In the embodiments of the present disclosure, cycling condition
(select aaa from tab2) is the query SQL sentence in step S12, arithmetic statement (the set s=in loop body
Cur.aaa*cur.aaa sentence) is handled for the vernier in step S12, and cur is vernier, and the PL/SQL engine at the end Driver can be with
Arithmetic statement in cycling condition and loop body is merged, the first logic plan is generated based on amalgamation result, and by first
Logic plan is sent in WorkerNode cluster and executes.The end Driver can be performed locally display statement (print (' aaa
Square be ' | | s)), realize and show work.In this way, reducing the load at the end Driver.
Example 2, the corresponding sentence collection of Spark storing process as shown in Figure 5 b.In the embodiments of the present disclosure, in loop body
Insert SQL statement (insert into t2 (select*from t3where t3.id=v_id)) is that vernier handles language
Sentence, including specifying variable (v_id), which passes through vernier (cur) from query SQL sentence (select for storing
Id from tab1) result set in read as a result, the PL/SQL engine at the end Driver can be by Insert in loop body
SQL statement is merged with query SQL sentence, generates the first logic plan based on amalgamation result, and the first logic plan is sent out
It send into WorkerNode cluster and executes.
Fig. 6 shows the block diagram of the Spark storing process processing unit according to one embodiment of the disclosure.The device 60 can answer
The end Driver for Spark.As shown in fig. 6, the device 60 can include:
Module 61 is obtained, for obtaining the corresponding sentence collection of Spark storing process, the sentence collection includes operation SQL language
Sentence and logic control sentence, the operation SQL statement includes query SQL sentence, and the logic control sentence includes vernier processing
Sentence；
First generation module 62, for being generated after merging the query SQL sentence and vernier processing sentence
First logic plan；
First sending module 63 is held for the first logic plan to be sent to working node WorkerNode cluster
Row.
In the embodiments of the present disclosure, by the way that vernier treatment process is abstracted as a logic plan, in WorkerNode collection
Logic plan is executed in group, is realized in WorkerNode cluster through processing of the vernier to implementing result, it in this way can be abundant
Using cluster resource, the load of the end Driver was not only reduced, but also improve the execution efficiency of Spark storing process
Fig. 7 shows the block diagram of the Spark storing process processing unit according to one embodiment of the disclosure.As shown in fig. 7, one
In the possible implementation of kind, the device 60 further include:
Second generation module 64, for concentrating the sentence except vernier processing sentence and the query SQL language
Each sentence other than sentence generates the second logic plan based on the SQL statement if the sentence is SQL statement；
Second sending module 65 is executed for the second logic plan to be sent in the WorkerNode cluster.
In one possible implementation, the device 60 further include:
Execution module 66, for for the sentence concentrate except the vernier processing sentence and the operation SQL statement with
Outer each sentence is performed locally the non-SQL statement if the sentence is non-SQL statement.
In one possible implementation, the vernier processing sentence includes: the language using specifying variable as filter condition
Sentence and/or to the sentence that specifying variable is handled, the specifying variable is for storing through vernier from the query SQL sentence
Result set in the result that reads.
Fig. 8 is a kind of frame of device 900 for the processing of Spark storing process shown according to an exemplary embodiment
Figure.Referring to Fig. 8, which may include processor 901, the machine readable storage medium for being stored with machine-executable instruction
902.Processor 901 can be communicated with machine readable storage medium 902 via system bus 903.Also, processor 901 passes through reading
Take machine-executable instruction corresponding with Spark storing process processing logic in machine readable storage medium 902 above to execute
The Spark storing process processing method.
Machine readable storage medium 902 referred to herein can be any electronics, magnetism, optics or other physical stores
Device may include or store information, such as executable instruction, data, etc..For example, machine readable storage medium may is that
RAM (Radom Access Memory, random access memory), volatile memory, nonvolatile memory, flash memory, storage are driven
Dynamic device (such as hard disk drive), solid state hard disk, any kind of storage dish (such as CD, dvd) or similar storage are situated between
Matter or their combination.
The presently disclosed embodiments is described above, above description is exemplary, and non-exclusive, and
It is not limited to disclosed each embodiment.Without departing from the scope and spirit of illustrated each embodiment, for this skill
Many modifications and changes are obvious for the those of ordinary skill in art field.The selection of term used herein, purport
In the principle, practical application or technological improvement to the technology in market for best explaining each embodiment, or lead this technology
Other those of ordinary skill in domain can understand each embodiment disclosed herein.
Claims (8)
1. a kind of Spark storing process processing method, which is characterized in that applied to the end Driver of Spark, the method packet
It includes:
The corresponding sentence collection of Spark storing process is obtained, the sentence collection includes operation SQL statement and logic control sentence, institute
Stating operation SQL statement includes query SQL sentence, and the logic control sentence includes vernier processing sentence；
The first logic plan is generated after the query SQL sentence and vernier processing sentence are merged；
The first logic plan is sent to working node WorkerNode cluster to execute.
2. the method according to claim 1, wherein the method also includes:
Each sentence in addition to the vernier handles sentence and the query SQL sentence is concentrated for the sentence, if the language
Sentence is SQL statement, then generates the second logic plan based on the SQL statement；
The second logic plan is sent in the WorkerNode cluster and is executed.
3. the method according to claim 1, wherein the method also includes:
Each sentence in addition to the vernier handles sentence and the operation SQL statement is concentrated for the sentence, if the language
Sentence is non-SQL statement, then is performed locally the non-SQL statement.
4. according to the method described in claim 3, it is characterized in that, vernier processing sentence includes: to be with specifying variable
The sentence of filter condition and/or the sentence handled specifying variable, the specifying variable is for storing through vernier from described
The result read in the result set of query SQL sentence.
5. a kind of Spark storing process processing unit, which is characterized in that applied to the end Driver of Spark, described device packet
It includes:
Module is obtained, for obtaining the corresponding sentence collection of Spark storing process, the sentence collection includes operation SQL statement and patrols
Control statement is collected, the operation SQL statement includes query SQL sentence, and the logic control sentence includes vernier processing sentence；
First generation module is patrolled for generating first after merging the query SQL sentence and vernier processing sentence
Collect plan；
First sending module is executed for the first logic plan to be sent to working node WorkerNode cluster.
6. device according to claim 5, which is characterized in that described device further include:
Second generation module, for being concentrated in addition to the vernier handles sentence and the query SQL sentence for the sentence
Each sentence, if the sentence be SQL statement, based on the SQL statement generate the second logic plan；
Second sending module is executed for the second logic plan to be sent in the WorkerNode cluster.
7. device according to claim 5, which is characterized in that described device further include:
Execution module, it is every in addition to the vernier handles sentence and the operation SQL statement for being concentrated for the sentence
A sentence is performed locally the non-SQL statement if the sentence is non-SQL statement.
8. device according to claim 7, which is characterized in that the vernier processing sentence includes: to be with specifying variable
The sentence of filter condition and/or the sentence handled specifying variable, the specifying variable is for storing through vernier from described
The result read in the result set of query SQL sentence.
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