WO2021038795A1 - データベースシステム、分散処理装置、データベース装置、分散処理方法、および、分散処理プログラム - Google Patents

データベースシステム、分散処理装置、データベース装置、分散処理方法、および、分散処理プログラム Download PDF

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WO2021038795A1
WO2021038795A1 PCT/JP2019/033914 JP2019033914W WO2021038795A1 WO 2021038795 A1 WO2021038795 A1 WO 2021038795A1 JP 2019033914 W JP2019033914 W JP 2019033914W WO 2021038795 A1 WO2021038795 A1 WO 2021038795A1
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Prior art keywords
query
distributed processing
execution
database
processing device
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English (en)
French (fr)
Japanese (ja)
Inventor
さやか 岩越
誠一郎 持田
山本　直人
真司 柿本
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NTT Inc
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Nippon Telegraph and Telephone Corp
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Priority to PCT/JP2019/033914 priority Critical patent/WO2021038795A1/ja
Priority to JP2021541901A priority patent/JP7295461B2/ja
Priority to US17/638,637 priority patent/US12056124B2/en
Publication of WO2021038795A1 publication Critical patent/WO2021038795A1/ja
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    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
    • G06F16/24—Querying
    • G06F16/245—Query processing
    • G06F16/2453—Query optimisation
    • G06F16/24534—Query rewriting; Transformation
    • G06F16/24542—Plan optimisation

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  • the present invention relates to a database system, a distributed processing device, a database device, a distributed processing method, and a distributed processing program.
  • a method of virtually integrating an external database is known as a technique for processing a query that crosses databases distributed over a network (see Non-Patent Document 1).
  • Non-Patent Document 1 the data of the database distributed via the network is aggregated on one server and then the query is processed.
  • the amount of transferred data becomes large and it takes time to transfer the data.
  • a high transfer cost is incurred.
  • the present invention has been made in view of the above circumstances, and an object of the present invention is to process queries related to a plurality of databases without aggregating data of a plurality of databases via a network into one device. To provide technology.
  • one aspect of the present invention is a database system including a distributed processing device and a plurality of database devices, wherein the distributed processing device is an execution plan for queries related to the plurality of database devices. And a selection unit that selects one of the execution plans based on the data transfer time of each execution plan, divides the query according to the selected execution plan, and transfers the divided query and the execution result of the divided query.
  • the database device includes a transmission unit that transmits an instruction including the above to the corresponding database device, and an output unit that receives and outputs the execution result of the query from the database device, and the database device is provided from the distributed processing device. It includes an execution unit that executes a split query included in the received instruction and transmits the execution result to another database device of the transfer destination included in the instruction or the distributed processing device.
  • the distributed processing device of one aspect of the present invention enumerates execution plans of queries related to a plurality of database devices, and selects one of the execution plans based on the data transfer time of each execution plan.
  • the query is divided according to the execution plan, and an instruction including the divided query and the transfer destination of the execution result of the divided query is transmitted to the corresponding database device, respectively, and the execution result of the query is transmitted from the database device.
  • It includes an output unit for receiving and outputting.
  • the database device of one aspect of the present invention distributes instructions including a split query in which the query is divided and a transfer destination of the execution result of the split query according to a query execution plan related to the own database device and another database device.
  • the execution plan includes a measurement unit that measures and transmits the measured performance information to the distributed processing device, and the total data transfer time of the own database device and other database devices calculated using the performance information is calculated. This is the minimum execution plan.
  • One aspect of the present invention is a distributed processing method for a database system including a distributed processing device and a plurality of database devices, wherein the distributed processing device enumerates execution plans of queries related to the plurality of database devices.
  • the selection step to select one of the execution plans based on the data transfer time of each execution plan, and the query is divided according to the selected execution plan, and the divided query and the transfer destination of the execution result of the divided query are selected.
  • a transmission step of transmitting the including instructions to the corresponding database device and an output step of receiving and outputting the execution result of the query from the database device are performed, and the database device performs the instruction received from the distributed processing device.
  • the split query included in is executed, and the execution step of transmitting the execution result to another database device of the transfer destination included in the instruction or the distributed processing device is performed.
  • One aspect of the present invention is a distributed processing program that causes a computer to function as the distributed processing device.
  • One aspect of the present invention is a distributed processing program that functions a computer as the database device.
  • FIG. 1 It is a figure which shows the configuration example of the distributed database system of embodiment of this invention.
  • This is an example of a table stored in a distributed DB of a DB device.
  • This is an example of a table stored in a distributed DB of a DB device.
  • This is an example of a table stored in a distributed DB of a DB device.
  • It is a flowchart which shows the operation of a distributed database system.
  • This is an example of a query tree. It is explanatory drawing explaining the direct transfer and the detour transfer. It is explanatory drawing explaining the execution cost of the execution plan.
  • FIG. 1 is a configuration example of the distributed DB system (database system) of the present embodiment.
  • the illustrated distributed DB system includes a distributed processing device 1 and a plurality of DB devices 2.
  • the distributed processing device 1 and the plurality of DB devices 2 are connected so as to be able to communicate with other devices via a network.
  • the number of DB devices is three, but the number of DB devices is not limited to three.
  • the number of DB devices 2 may be at least two.
  • the distributed processing device 1 and the DB device 2 are also referred to as "nodes".
  • the distributed processing device 1 processes a query that crosses between a plurality of DB devices distributed via the network based on the network performance.
  • the distributed processing device 1 shown in the figure includes a query analysis unit 11, an execution plan selection unit 12, an instruction transmission unit 13, an output unit 14, a collection unit 15, and a storage unit 16.
  • the query analysis unit 11 analyzes the input query 5 and generates a query tree expressing the query 5 in a tree structure.
  • the query 5 of the present embodiment is a query related to a plurality of DB devices 2, that is, a query that cross-processes the plurality of DB devices 2.
  • the execution plan selection unit 12 (selection unit) enumerates the execution plans of the query 5 and selects one of the execution plans based on the data transfer time of each execution plan. Specifically, the execution plan selection unit 12 generates a plurality of execution plans based on the query wooden structure, and selects the optimum execution plan from the plurality of execution plans based on the network performance. The selection unit 12 calculates the data transfer time of each execution plan by using the network performance information collected from the DB device 2 and the transfer data amount of each DB device 2. The execution plan selected is, for example, an execution plan that minimizes the total data transfer time of the plurality of DB devices 2.
  • the instruction transmission unit 13 causes a plurality of DB devices 2 to perform distributed processing of the query 5 according to the selected execution plan. Specifically, the instruction transmission unit 13 divides the query 5 according to the selected execution plan, and transmits an instruction including the divided query and the transfer destination of the execution result of the divided query to the corresponding DB devices 2, respectively. To do.
  • the output unit 14 receives the final execution result of the query 5 from the DB device 2 and outputs it as the query result 6.
  • the output unit 14 of the present embodiment receives the execution result of the query 5 from one DB device set at the end of the selected execution plan.
  • the output unit 14 may visualize the received execution result using a visualization tool such as Tableau, and output the visualized query result 6.
  • the collecting unit 15 collects network performance information (network bandwidth information, etc.) between the DB devices 2 (nodes) from the DB device 2 and stores it in the storage unit 16.
  • the storage unit 16 stores the network performance information collected by the collection unit 15.
  • Each DB device 2 (nodes K, T1, T2) includes a measurement unit 21, an execution unit 22, and a distributed DB 23.
  • the measuring unit 21 measures the network performance with another DB device 2 or the distributed processing device 1, and transmits the measured performance information to the distributed processing device 1. That is, the measuring unit 21 measures the network performance between the nodes.
  • the execution unit 22 executes the divided query included in the instruction received from the distributed processing device 1, and transmits the execution result to another DB device 2 or the distributed processing device 1 of the transfer destination included in the instruction. At least one database is stored in the distributed DB 23.
  • FIG. 2 shows a table stored in the distributed DB 23 of the DB device 2 of the node K.
  • the distributed DB 23 of FIG. 2 is a database of department stores, and has a CM table (customer management table) and a TM table (purchase history table).
  • the number of records in the CM table is 6M.
  • the number of records in the TM table is 60M.
  • FIG. 3 shows a table stored in the distributed DB 23 of the DB device 2 of the node T1.
  • the distributed DB 23 of FIG. 3 is a database of tenant 1 in a department store, and has a TCM1 table (customer management table) and a TTM1 table (purchase history table).
  • the number of records in the TCM1 table is 50,000.
  • the number of records in the TTM1 table is 500,000.
  • the user ID (TUid) of the tenant 1 and the user ID (Uid) of the CM table of the department store are stored in association with each other.
  • FIG. 4 shows a table stored in the distributed DB 23 of the DB device 2 of the node T2.
  • the distributed DB 23 of FIG. 4 is a database of tenants 2 in a department store, and has a TCM2 table (customer management table) and a TTM2 table (purchase history table).
  • the number of records in the TCM2 table is 20000.
  • the number of records in the TTM2 table is 200,000.
  • the user ID (TUid) of the tenant 2 and the user ID (Uid) of the CM table of the department store are stored in association with each other.
  • FIG. 5 is a flowchart showing the operation of the distributed DB system of the present embodiment.
  • Each DB device 2 measures the network performance between its own DB device 2 and another DB device 2 or the distributed processing device 1 (S11). Then, the DB device 2 transmits the measured network performance information to the distributed processing device 1.
  • the network bandwidth data transfer rate: bps
  • the distributed processing device 1 collects network performance information from each DB device 2 and stores it in the storage unit 16 (S12).
  • FIG. 6 is a diagram schematically showing network performance between nodes.
  • the network bandwidth between node K and node T1 is 10 Mbps.
  • the network bandwidth between node K and node T2 is 10 Mbps, and the network bandwidth between node K and node C is 5 Mbps.
  • S11 and S12 does not have to be performed every time the processing of S13 or later is performed. For example, when the network performance information is already stored in the storage unit 16, S11 and S12 are not performed, and the distributed processing device 1 may use the network performance information stored in the storage unit 16. Further, S11 and S12 may be executed periodically or at a predetermined timing such as an operator's instruction to update the network performance information stored in the storage unit 16.
  • the distributed processing device 1 receives the query input by the user, analyzes the query, and generates a tree-structured query tree (S13).
  • FIG. 7 is an example of a query related to a plurality of DB devices 2.
  • the query illustrated is to test the hypothesis that people who go to a beauty salon are more likely to buy clothes or shoes immediately afterwards, with the product or service classifications "clothes", "shoes” and "esteem” This is a search condition for extracting the purchase history of the department store user who made the purchase.
  • the data targeted by the illustrated query is the table CM of the distributed DB (department store) of node K, the table TTM1 of the distributed DB (tenant 1) of node T1, and the table TTM2 of the distributed DB (tenant 2) of node T2. Yes (see Figures 2- and 4).
  • FIG. 8 is an example of a query tree generated from the query of FIG. 7.
  • the distributed processing device 1 enumerates (generates) at least one execution plan capable of executing the input query (S14). Then, the distributed processing apparatus 1 calculates the execution cost (execution time) of each execution plan, and selects the optimum execution plan based on the execution cost (S15). Specifically, the distributed processing device 1 selects one of the execution plans based on the data transfer time of each execution plan.
  • the execution plans (execution routes) generated are the following six types.
  • Execution plan 1 Node T2 ⁇ Node T1 ⁇ Node K ⁇ Node C
  • Execution plan 2 Node T2 ⁇ Node K ⁇ Node T1 ⁇ Node C
  • Execution plan 3 Node T1 ⁇ Node T2 ⁇ Node K ⁇ Node C
  • Execution plan 4 Node T1 ⁇ Node K ⁇ Node T2 ⁇ Node C
  • Execution plan 5 Node K ⁇ Node T1 ⁇ Node T2 ⁇ Node C
  • Execution plan 6 Node K ⁇ Node T2 ⁇ Node T1 ⁇ Node C
  • FIG. 9 is an explanatory diagram illustrating direct forwarding and detour forwarding.
  • the detour transfer is a transfer method other than the direct transfer.
  • FIG. 9 shows a plurality of transfer methods from node K to node T2 in the network configuration shown in FIG.
  • the direct transfer 91 the data is directly transferred from K to T2.
  • the detour transfer 92 node K ⁇ node C ⁇ node T2 via C, node K ⁇ node T1 ⁇ node T2 via node T1, and node K ⁇ node C ⁇ node T1 ⁇ via node C and T1. It shows three detour transfers of node T2.
  • FIG. 10 is an explanatory diagram for explaining the calculation of the execution cost of the execution plan 1.
  • the node T2 first executes the split query included in the instruction 51 for the node T2 to the table TTM2, and transmits the execution result TEMP to the node T1.
  • the node T1 executes the split query included in the instruction 52 for the node T1 to the tables TTM1 and TEMP, and transmits the execution result T to the node K.
  • the node K executes the split query included in the instruction 53 for the node K to the tables CM and T, and transmits the final execution result, Result, to the node C (distributed processing device 1).
  • the total of the query processing time of each node (T1, T2, K) calculated by the distributed processing device 1 and the data transfer time of the execution result to other nodes is calculated as the execution cost. Shown as (execution time).
  • the query processing time is calculated using the data size of the target table.
  • the data transfer time is calculated using the number of transfer records and the transfer rate.
  • the distributed processing device 1 estimates the number of transfer records of each node by using the query optimization function.
  • the total time of the data transfer time and the query processing time is taken as the execution cost.
  • the execution cost may be limited to the data transfer time, which accounts for a large proportion, without considering the query processing time.
  • FIG. 11 is a diagram showing an execution cost (execution time) for each execution plan when only the data transfer time is used.
  • the distributed processing apparatus 1 selects the execution plan 1 having the minimum execution cost (61.33) (S15).
  • the distributed processing device 1 divides the input query and generates a divided query for each node according to the selected execution plan 1. Then, the distributed processing device 1 generates an instruction including a division query and a transfer destination of the execution result of the division query for each node T1, T2, K (DB device 2), and issues an instruction corresponding to each node. Transmit (S16).
  • FIG. 10 shows an example 51-53 of the instruction.
  • the split query is the input query divided into the execution contents of each node. Each node executes the split query according to the instruction and transfers the execution result to the instructed transfer destination (S17).
  • FIG. 12 is a diagram showing the processing of the node T2.
  • the node T2 first executes the split query of the instruction 51 on its own table TTM2, and transfers the execution result to the node T1 instructed as TEMP.
  • the node T2 extracts a record having a classification of "clothes”, “shoes”, and “esthetic” from the table TTM2, and transfers the extracted record as a TEMP to the node T1.
  • FIG. 13 is a diagram showing the processing of the node T1.
  • Node T1 executes the split query of instruction 52 on the TEMP (execution result of node T2) received from node T2 and its own table TTM1, and transfers the execution result to node K instructed as T.
  • the node T1 extracts records whose classifications are "clothes”, “shoes”, and “esthetic" from the table TTM1, integrates the extracted records and the TEMP, and transfers them to the node K as T.
  • FIG. 14 is a diagram showing the processing of node K.
  • Node K executes the split query of instruction 53 against T (execution result of node T1) received from node T1 and its own table CM, and transfers the final execution result to node C instructed as Rrsult. ..
  • the node K transfers the purchase history records of "clothes", “shoes”, and “esthetics" for each user of the department store to the node C as Rrsult.
  • the distributed processing device 1 receives the final execution result of the query 5 from the node K and outputs the execution result (S18).
  • the distributed processing device 1 may visualize the received execution result by using a visualization tool such as Tableau, and output the visualized query result.
  • Modification example 1 Next, a modification 1 of the present embodiment will be described.
  • the distributed processing device 1 excludes the execution plan contrary to the policy enforcement.
  • Policy enforcement includes, for example, data before query processing cannot be transferred to other nodes.
  • the distributed processing device 1 transfers only the execution result of the split query to another node for the data.
  • FIG. 15 is a diagram showing an execution plan when there is a policy enforcement that prohibits the transfer of the table CM of the node K.
  • the distributed processing device 1 (execution plan selection unit 12) has a policy for the execution plan in which the node K is set as the transfer source of the A line in FIG. 15 and the execution plan in which the node K is set as the transfer source of the B line. Judge that the execution plan is against the enforcement and exclude it. Therefore, in S14 of FIG. 5, the distributed processing apparatus 1 excludes the execution plans 2 and 4-6, lists only the execution plans 1 and 3, and selects the execution plan 1 having the lowest cost.
  • the query can be executed without transferring the data before executing the query of the predetermined node to another node. That is, in the present embodiment including the present modification, since it is not necessary to aggregate the data of the distributed DB in one place, there is a policy enforcement that prohibits the transfer of confidential data such as personal information. Can also be applied to queries related to a plurality of distributed DBs containing data whose transfer is prohibited. Therefore, in the present embodiment, it is possible to analyze the data for which the transfer to the outside is prohibited.
  • FIG. 16 is a configuration example of the distributed DB system of the second modification of the present embodiment.
  • the distributed DB system of the second modification is different from the distributed DB system shown in FIG. 1 in that the distributed processing device 1 does not have the collecting unit 15 and the DB device 2 does not have the measuring unit 21.
  • the storage unit 16 of the distributed processing device 1 stores network performance information between nodes that has been measured or designed in advance.
  • the DB device 2 may or may not include the measuring unit 21.
  • the distributed processing device 1 enumerates the execution plans of the queries related to the plurality of DB devices 2, and selects one of the execution plans based on the data transfer time of each execution plan. Instruction transmission that divides the query according to the selected execution plan selection unit 12 and the selected execution plan, and transmits the instructions including the divided query and the transfer destination of the execution result of the divided query to the corresponding DB devices 2, respectively.
  • a unit 13 and an output unit 14 that receives and outputs a query execution result from the DB device 2 are provided, and the DB device 2 executes a divided query included in the instruction received from the distributed processing device 1 and outputs the execution result.
  • An execution unit 22 for transmitting to another DB device 2 or a distributed processing device 1 of the transfer destination included in the instruction is provided.
  • the present embodiment it is possible to process the queries related to the plurality of DB devices 2 without aggregating the data of the plurality of DB devices 2 via the network into one device. Therefore, in the present embodiment, it is possible to avoid concentration of the load on a specific network and execute the query efficiently. In addition, data transfer time and data transfer cost can be reduced.
  • one of the execution plans is selected based on the data transfer time of the listed execution plans.
  • the optimum execution plan of the query can be selected according to the network performance, and the execution cost of the query can be reduced.
  • the distributed processing device 1 excludes the execution plan contrary to the policy enforcement from the execution plan.
  • the execution result is transmitted and received between each node to execute the query without aggregating the data of the plurality of DB devices 2 into one device, so that the policy enforcement prohibits the data transfer to the outside.
  • the distributed query processing method of the present embodiment can be applied even when is present.
  • Comparative Examples 17 and 18 are explanatory views for explaining Comparative Examples 1 and 2 of the present embodiment.
  • the distributed DB-A of node A and the distributed DB-B of node B each exist on-premises, and the distributed DB-A has 1 million records and the distributed DB-B has 100 records.
  • the operation when executing a query that crosses the distributed DB-A and the distributed DB-B so that the number of records is about 50 will be described.
  • Comparative example 1 shown in FIG. 17 is a comparative example using a BI (Business Intelligence) tool.
  • each node transfers all the records of its own distributed DB to the aggregation node, and the aggregation node executes a query for the transferred records.
  • the data since the data is aggregated in one aggregation node, the amount of data to be transferred increases, and the data transfer time and transfer cost increase.
  • Comparative Example 2 shown in FIG. 18 is a method of pushing down (delegating) the aggregation operation to the distributed DB-A of node A and the distributed DB-B of node B using PostgreSQL's Foreign Data Wrapper (FDW). .. That is, in Comparative Example 2, some queries can be pushed down, and the data of the result processed by each node for its own distributed DB is transferred to the aggregation node. The aggregation node combines the data transferred from each node.
  • FDW PostgreSQL's Foreign Data Wrapper
  • node A reduces the records of the distributed DB-A to 500,000 records by the query processing of PushDown, and transfers them to the aggregation node.
  • node B reduces the records of the distributed DB-B to 70 records and transfers them to the aggregation node.
  • the aggregation node combines the records transmitted from each node into 50 records.
  • Comparative Example 2 by pushing down a part of the query to the lower node, the data to be transferred to the aggregation node can be filtered (reduced), and the data transfer time can be reduced.
  • the filtering process that can be performed by the distributed DB-A alone can be pushed down to the distributed DB-A, but the filtering process that requires the data of the distributed DB-B cannot be pushed down to the distributed DB-A. Therefore, the effect of the filtering process by Push Down is limited. That is, in Comparative Example 2, it is necessary to collect the data to be processed in the aggregation node for data combination between a plurality of DBs, and the load is concentrated on a specific network.
  • the optimum execution plan is selected in consideration of the network performance between the nodes and the amount of transferred data, the data is transferred between the nodes, and the final execution result is obtained. Only is transmitted to the distributed processing device 1. Thereby, in the present embodiment, the data transfer time can be reduced.
  • FIG. 20 a general-purpose computer system as shown in FIG. 20 can be used.
  • the computer system shown is a CPU (Central Processing Unit, processor) 901, memory 902, storage 903 (HDD: Hard Disk Drive, SSD: Solid State Drive), communication device 904, input device 905, and output device. 906 and.
  • the memory 902 and the storage 903 are storage devices.
  • each function of each device is realized by executing a predetermined program loaded on the memory 902 by the CPU 901.
  • the CPU of the distributed processing device 1 is used in the case of the program for the distributed processing device 1
  • the CPU of the DB device 2 is used in the case of the program for the DB device 2. It is realized by executing.
  • the distributed processing device 1 and the DB device 2 may be mounted on one computer, or may be mounted on a plurality of computers. Further, the distributed processing device 1 and the DB device 2 may be virtual machines mounted on a computer.
  • the program for the distributed processing device 1 and the program for the DB device 2 are stored in a computer-readable recording medium such as an HDD, SSD, USB (Universal Serial Bus) memory, CD (Compact Disc), or DVD (Digital Versatile Disc). It can be delivered over the network.
  • a computer-readable recording medium such as an HDD, SSD, USB (Universal Serial Bus) memory, CD (Compact Disc), or DVD (Digital Versatile Disc). It can be delivered over the network.

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PCT/JP2019/033914 2019-08-29 2019-08-29 データベースシステム、分散処理装置、データベース装置、分散処理方法、および、分散処理プログラム Ceased WO2021038795A1 (ja)

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JP2021541901A JP7295461B2 (ja) 2019-08-29 2019-08-29 データベースシステム、分散処理装置、データベース装置、分散処理方法、および、分散処理プログラム
US17/638,637 US12056124B2 (en) 2019-08-29 2019-08-29 Database system, distributed processing apparatus, database apparatus, distributed processing method and distributed processing program

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WO2017138062A1 (ja) * 2016-02-08 2017-08-17 株式会社日立製作所 分散データベースシステム、及び、分散データベースシステムの管理方法

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JP2022011746A (ja) * 2020-06-30 2022-01-17 富士通株式会社 情報処理プログラム、情報処理装置及び情報処理方法
US11960938B2 (en) 2020-06-30 2024-04-16 Fujitsu Limited Information processing program, information processing apparatus, and information processing method that optimize access to an external database based on calculated minimum processing load
JP7485934B2 (ja) 2020-06-30 2024-05-17 富士通株式会社 情報処理プログラム、情報処理装置及び情報処理方法

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