WO2017015792A1 - Recommandations et notation de performances sql - Google Patents

Recommandations et notation de performances sql Download PDF

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Publication number
WO2017015792A1
WO2017015792A1 PCT/CN2015/085061 CN2015085061W WO2017015792A1 WO 2017015792 A1 WO2017015792 A1 WO 2017015792A1 CN 2015085061 W CN2015085061 W CN 2015085061W WO 2017015792 A1 WO2017015792 A1 WO 2017015792A1
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WIPO (PCT)
Prior art keywords
queries
scores
cpu
sql
account
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PCT/CN2015/085061
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English (en)
Inventor
Yongcheng Eddy CAI
Diana Cobb
Lu CAO
Xiquan REN
Hao Chen
Jilin JIANG
Original Assignee
Cai Yongcheng Eddy
Diana Cobb
Cao Lu
Ren Xiquan
Hao Chen
Jiang Jilin
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Application filed by Cai Yongcheng Eddy, Diana Cobb, Cao Lu, Ren Xiquan, Hao Chen, Jiang Jilin filed Critical Cai Yongcheng Eddy
Priority to PCT/CN2015/085061 priority Critical patent/WO2017015792A1/fr
Publication of WO2017015792A1 publication Critical patent/WO2017015792A1/fr

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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/90—Details of database functions independent of the retrieved data types
    • G06F16/95—Retrieval from the web
    • G06F16/953—Querying, e.g. by the use of web search engines
    • G06F16/9535—Search customisation based on user profiles and personalisation

Definitions

  • Embodiments of the present disclosure relate generally to data processing and, more particularly, but not by way of limitation, to recommendations and scoring to improve performance of SQL databases and efficiency of SQL queries.
  • Databases including SQL databases are facing more user queries than before, but conventionally any performance tuning and user education have been done case by case, requiring a capacity engineer to analyze each of the separate user queries and determine how the query can be made more efficient.
  • the number of queries to be analyzed eventually grows to exceed the capacity engineer’s capability.
  • a capping rule can only specify a budget at a group level, rather than on an individual user level, which is of limited use when a few users in a group are using more than their share of resources.
  • L2 representatives and database users have very limited insights for query performance on the databases they are querying. Accordingly, there is a need for a user friendly tool to show individual users the opportunities for improving the performance of their SQL queries.
  • FIG. 1 is a block diagram illustrating a networked system, according to some example embodiments.
  • FIG. 2 is a modular diagram of an SQL query execution system
  • FIG. 3 conceptually illustrates an SQL query scoring and recommendation method of some embodiments.
  • FIG. 4 conceptually illustrates a method that generates recommendations based on individual scores of sets of SQL queries
  • FIG. 5 is a block diagram illustrating an example of a software architecture that may be installed on a machine, according to some example embodiments.
  • FIG. 6 illustrates a diagrammatic representation of a machine in the form of a computer system within which a set of instructions may be executed for causing the machine to perform any one or more of the methodologies discussed herein, according to an example embodiment.
  • Some embodiments provide a scoring and automated suggestion system and method for improving the efficiency of SQL searches.
  • the efficiency is measured by one or more metrics including amount of computing resources used versus amount of computing resources that would be used in a reference search.
  • the system of some embodiments determines a score for each of a plurality of characteristics of a search of a database resulting from an SQL search. The scores are then composited as a weighted average to provide an overall score for the SQL search. In some embodiments, the score is ranked (e.g., provided with a ranking value such as a letter grade A-F) against the scores of other SQL queries (e.g., against some portion or all of the SQL queries made on the database) .
  • a ranking value such as a letter grade A-F
  • the system Based on the overall score and/or the individual scores, the system makes one or more suggestions for improving the efficiency of the search.
  • the user who generated the search is then provided with these scores and suggestions for improving the efficiency of the search.
  • the efficiency of an SQL search of a database is a problem that arises solely in the field of computers and the process of ranking the efficiency of such a search requires computers to perform it.
  • the system of some embodiments determines the scores based on two categories of characteristics.
  • the first category of characteristics is CPU efficiency.
  • the second category of characteristics is CPU consumption.
  • the CPU efficiency characteristics may be more heavily weighted (e.g., the CPU efficiency characteristics may total 80% of the overall score for an SQL query) .
  • FIG. 1 illustrates, for example, an SQL query interface 112, an SQL tuning assistant 114, and a programmatic client 116 executing on client device 110.
  • a network 104 e.g., the Internet or wide area network (WAN)
  • FIG. 1 illustrates, for example, an SQL query interface 112, an SQL tuning assistant 114, and a programmatic client 116 executing on client device 110.
  • the client device 110 may comprise, but are not limited to, a mobile phone, desktop computer, laptop, portable digital assistants (PDAs) , smart phones, tablets, ultra books, netbooks, laptops, multi-processor systems, microprocessor-based or programmable consumer electronics, game consoles, set-top boxes, or any other communication device that a user may utilize to access the networked system 102.
  • the client device 110 may comprise a display module (not shown) to display information (e.g., in the form of user interfaces) .
  • the client device 110 may comprise one or more of a touch screens, accelerometers, gyroscopes, cameras, microphones, global positioning system (GPS) devices, and so forth.
  • GPS global positioning system
  • the client device 110 may be a device of a user that is used to perform a transaction involving digital items within the networked system 102.
  • the networked system 102 is a network-based marketplace that responds to requests for product listings, publishes publications comprising item listings of products available on the network-based marketplace, and manages payments for these marketplace transactions.
  • One or more users 106 may be a person, a machine, or other means of interacting with client device 110. In embodiments, the user 106 is not part of the network architecture 100, but may interact with the network architecture 100 via client device 110 or another means.
  • one or more portions of network 104 may be an ad hoc network, an intranet, an extranet, a virtual private network (VPN) , a local area network (LAN) , a wireless LAN (WLAN) , a wide area network (WAN) , a wireless WAN (WWAN) , a metropolitan area network (MAN) , a portion of the Internet, a portion of the Public Switched Telephone Network (PSTN) , a cellular telephone network, a wireless network, a WiFi network, a WiMax network, another type of network, or a combination of two or more such networks.
  • VPN virtual private network
  • LAN local area network
  • WLAN wireless LAN
  • WAN wide area network
  • WWAN wireless WAN
  • MAN metropolitan area network
  • PSTN Public Switched Telephone Network
  • PSTN Public Switched Telephone Network
  • Each of the client device 110 may include one or more applications (also referred to as “apps” ) such as, but not limited to, an SQL query interface, messaging application, electronic mail (email) application, an e-commerce site application (also referred to as a marketplace application) , and the like.
  • applications also referred to as “apps”
  • this application is configured to locally provide the user interface and at least some of the functionalities with the application configured to communicate with the networked system 102, on an as needed basis, for data and/or processing capabilities not locally available (e.g., access to a database of items available for sale, to authenticate a user, to verify a method of payment, etc. ) .
  • the client device 110 may still use its an SQL query interface 112 to access a database (or a variant thereof) hosted on the server (s) 140.
  • One or more users 106 may be a person, a machine, or other means of interacting with the client device 110.
  • the user 106 is not part of the network architecture 100, but may interact with the network architecture 100 via the client device 110 or other means.
  • the user provides input (e.g., touch screen input or alphanumeric input) to the client device 110 and the input is communicated to the server (s) 140 via the network 104.
  • the networked system 102 in response to receiving the input from the user, communicates information to the client device 110 via the network 104 to be presented to the user. In this way, the user can interact with the server (s) 140 using the client device 110.
  • An application program interface (APl) server 120 and a web server 122 are coupled to, and provide programmatic and web interfaces respectively to, one or more server (s) 140.
  • the server (s) 140 may host one or more SQL query execution systems, each of which may comprise one or more modules or applications and each of which may be embodied as hardware, software, firmware, or any combination thereof.
  • the server (s) 140 are, in turn, shown to be coupled to one or more database servers 124 that facilitate access to one or more information storage repositories or database (s) 126.
  • the databases 126 are storage devices that store information to be posted (e.g., publications or listings) to a publication system and accessible to SQL queries provided via SQL query interface 112.
  • the databases 126 may also store digital item information in accordance with example embodiments.
  • the SQL query execution system 150 may provide functionality operable to divide the operations commanded by SQL queries into multiple parallel operations to be performed by one or more database servers 124 using the queries supplied by users of the SQL interface 112.
  • the SQL query execution system runs on top of an SQL server (e.g., SQL Server, Oracle, MySQL, or other database server) .
  • the SQL query execution system is part of an execution engine/query plan engine of the SQL server. In either of such embodiments, the SQL query execution system 150 may access the searched for data from the databases 126, and other sources.
  • client-server-based network architecture 100 shown in FIG. 1 employs a client-server architecture
  • present inventive subject matter is of course not limited to such an architecture, and could equally well find application in a distributed, or peer-to-peer, architecture system, for example.
  • the SQL query execution system 150 could also be implemented as standalone software programs, which do not necessarily have networking capabilities.
  • the SQL query interface 112 may access the database via the web interface supported by the web server 122.
  • the SQL tuning assistant 114 may receive recommendations and statistics regarding a set of one or more queries produced via an account (e.g., using SQL query interfaces 112 on one or more client devices.
  • Modules may constitute either software modules (e.g., code embodied on a machine-readable medium) or hardware modules.
  • a “hardware module” is a tangible unit capable of performing certain operations and may be configured or arranged in a certain physical manner.
  • one or more computer systems e.g., a standalone computer system, a client computer system, or a server computer system
  • one or more hardware modules of a computer system e.g., a processor or a group of processors
  • software e.g., an application or application portion
  • a hardware module may be implemented mechanically, electronically, or any suitable combination thereof.
  • a hardware module may include dedicated circuitry or logic that is permanently configured to perform certain operations.
  • a hardware module may be a special-purpose processor, such as a Field-Programmable Gate Array (FPGA) or an Application Specific Integrated Circuit (ASIC) .
  • a hardware module may also include programmable logic or circuitry that is temporarily configured by software to perform certain operations.
  • a hardware module may include software executed by a general-purpose processor or other programmable processor. Once configured by such software, hardware modules become specific machines (or specific components of a machine) uniquely tailored to perform the configured functions and are no longer general-purpose processors. It will be appreciated that the decision to implement a hardware module mechanically, in dedicated and permanently configured circuitry, or in temporarily configured circuitry (e.g., configured by software) may be driven by cost and time considerations.
  • hardware module should be understood to encompass a tangible entity, be that an entity that is physically constructed, permanently configured (e.g., hardwired) , or temporarily configured (e.g., programmed) to operate in a certain manner or to perform certain operations described herein.
  • “hardware-implemented module” refers to a hardware module. Considering embodiments in which hardware modules are temporarily configured (e.g., programmed) , each of the hardware modules need not be configured or instantiated at any one instance in time.
  • a hardware module comprises a general-purpose processor configured by software to become a special-purpose processor
  • the general-purpose processor may be configured as respectively different special-purpose processors (e.g., comprising different hardware modules) at different times.
  • Software accordingly configures a particular processor or processors, for example, to constitute a particular hardware module at one instance of time and to constitute a different hardware module at a different instance of time.
  • Hardware modules can provide information to, and receive information from, other hardware modules. Accordingly, the described hardware modules may be regarded as being communicatively coupled. Where multiple hardware modules exist contemporaneously, communications may be achieved through signal transmission (e.g., over appropriate circuits and buses) between or among two or more of the hardware modules. In embodiments in which multiple hardware modules are configured or instantiated at different times, communications between such hardware modules may be achieved, for example, through the storage and retrieval of information in memory structures to which the multiple hardware modules have access. For example, one hardware module may perform an operation and store the output of that operation in a memory device to which it is communicatively coupled. A further hardware module may then, at a later time, access the memory device to retrieve and process the stored output. Hardware modules may also initiate communications with input or output devices, and can operate on a resource (e.g., a collection of information) .
  • a resource e.g., a collection of information
  • processors may be temporarily configured (e.g., by software) or permanently configured to perform the relevant operations. Whether temporarily or permanently configured, such processors may constitute processor-implemented modules that operate to perform one or more operations or functions described herein.
  • processor-implemented module refers to a hardware module implemented using one or more processors.
  • the methods described herein may be at least partially processor-implemented, with a particular processor or processors being an example of hardware.
  • a particular processor or processors being an example of hardware.
  • the operations of a method may be performed by one or more processors or processor-implemented modules.
  • the one or more processors may also operate to support performance of the relevant operations in a “cloud computing” environment or as a “software as a service” (SaaS) .
  • SaaS software as a service
  • at least some of the operations may be performed by a group of computers (as examples of machines including processors) , with these operations being accessible via a network (e.g., the Internet) and via one or more appropriate interfaces (e.g., an Application Program Interface (API) ) .
  • API Application Program Interface
  • processors may be distributed among the processors, not only residing within a single machine, but deployed across a number of machines.
  • the processors or processor-implemented modules may be located in a single geographic location (e.g., within a home environment, an office environment, or a server farm) . In other example embodiments, the processors or processor-implemented modules may be distributed across a number of geographic locations.
  • FIG. 2 illustrates an SQL query execution system 150.
  • the system 150 includes a Parsing Module 200 to parse received SQL queries, an Update Module 210, an Insert Module 220, a Delete Module 230, a Create Table as Select Module 240, a Joining Module 250, and an Execution Plan Module 260.
  • the Parsing Module 200 may determine which other modules of the SQL execution system 150 to activate and what parameters to send to the other modules based on a received SQL query.
  • the Update Module 210 replaces a value of an existing database cell with a new value.
  • the Insert Module 220 adds a new structure (e.g., a new column or row in a table) to the database.
  • the Delete Module 230 removes a value from the database.
  • the Create Table as Select Module 240 creates a table in the database by selecting data from an existing table.
  • the Joining Module 250 combines data from more than one table. When the data types of the data being combined are not identical, one or more of the existing sets of data must be translated into a different form of data in order to join the data.
  • the Execution Plan Module 260 determines a plan for searching the data in response to the SQL query. In some systems (e.g., in an Oracle database system) the Execution Plan is accessed for display to a user and/or to another module by a command such as an “Explain Plan for” command.
  • modules 210-260 commands the database servers 126 of FIG. 1 to perform the corresponding query operation.
  • FIG. 3 conceptually illustrates a method 300 of some embodiments for scoring an account that produces multiple SQL queries (i.e., from one or more users of the account) .
  • the scoring system may score sets of queries on a broader or narrower scale. For example, the scoring system may score each of several users of an account separately or score a group of accounts together. Some characteristics are scored on a query by query basis. Other characteristics are scored based on an aggregate of multiple queries.
  • the method 300 scores (at 305) a set of queries based on a first CPU efficiency characteristic, a confidence level of the Execution Plans of the queries (e.g., as accessed in an Oracle Database by an “Explain Plan” command) .
  • a query is parsed (e.g., by a parsing module) which produces some form of abstract syntax tree. This is, in turn, converted to some sort of intermediate code (e.g., bytecode and the like) .
  • This intermediate code is fed to an optimizer which selects an Execution Plan.
  • the Execution Plan is then executed by an execution engine.
  • one or more commands accesses and records the Execution Plan for a query.
  • the Execution Plan can be analyzed to determine whether the database software is choosing the correct indexes and joining tables of the database in the most efficient manner.
  • One characteristic of the data retrieved by the Explain Plan command and/or similar commands in non-Oracle databases is the confidence in the Execution Plan.
  • the confidence in the Execution Plan is determined by the quality of statistics collected by the system relative to the data being searched by the SQL query. The higher the confidence in the Execution Plan, the more efficient the search is.
  • the confidence values of some embodiments fall into one of three levels.
  • the first level is “High Confidence” , in which a) any restricting conditions on the data have collected stats; and b) when a Join operator is used, both input relations of the data have High Confidence.
  • the second level is “Low Confidence” , in which a) restricting conditions exist on one or more indexes or columns that i) have collected statistics but ii) are “AND-ed” together with conditions on non-indexed columns and/or b) restricting conditions exist on one or more indexes or columns that i) have collected statistics but ii) are “OR-ed” together with other conditions on non-indexed columns.
  • the third level is “no confidence, in which a) the input relation has no confidence; and b) when a Join operation is used, statistics do not exist for either Join field.
  • the confidence level score may be based on a calculation of the number of query Execution Plans with fulfilling the “No Confidence” conditions divided by the total number of query explain plans. However, the score may count only those queries with Retrieve or Join operations on Visitor Data Mart (VDM) or volatile tables in determining the number of query plans with “No Confidence” and the total number of query Execution Plans. The higher the percentage of “No Confidence” Execution Plans, the worse the quality of the SQL query. Accordingly, higher percentages of “No Confidence” plans produce a worse overall score.
  • VDM Visitor Data Mart
  • some embodiments adjust the score to account for the percentage of “High Confidence” Execution Plans. For example, in such embodiments, between two sets of queries with the same percentages of “No Confidence” Execution Plans, the set of queries with a higher percentage of “High Confidence” Execution Plans would have a better score (e.g., by subtracting a fraction of the percentage of “High Confidence” Execution Plans to the “No Confidence” percentage) . When the score for the “No Confidence” percentage is poor (i.e., when the percentage is high) , the system of some embodiments recommends that the user who generated the scored SQL queries try to collect stats on the VDM or volatile tables of the queries.
  • the method 300 scores (at 310) a set of queries based on a second CPU efficiency characteristic, the average CPU consumption per database row modified on a merge step.
  • the created table is a SET table.
  • a SET table does not allow duplicate rows (i.e., rows in which every value is identical to the corresponding value in another row) .
  • UPI unique primary index
  • this is not an issue as every row in the table differs from every other row in the table by at least the primary index value.
  • NUPI non-unique primary index
  • any rows with the same primary index value must be checked against every other row with that primary index value in order to prevent duplicate rows.
  • the number of rows to be checked against each other to determine whether there are any duplicate rows is n* (n-1) /2, where “n” is the number of rows with the same NUPI value.
  • a MULTISET table does allow duplicate rows. Because the MULTISET table allows duplicate rows, no CPU resources are used to check for duplicate rows. Therefore, creating a table as a MULTISET table will save resources (e.g., CPU consumption) and result in a better performance.
  • the scoring system may suggest users create MULTISET tables, instead of SET tables as much as possible. In some cases, the particular column of the table used as a NUPI may include larger groups of duplicated NUPI values than would be the case if a different column were chosen. Accordingly, the scoring system may also recommend changing which column is used as the NUPI in order to reduce the number of duplicates or better distribute the number of duplicates.
  • the number of pairs of rows to check against each other to identify duplicates would be reduced from 499, 500 (i.e., 100*99/2 pairs of rows) to 450 (i.e., ten sets of 10*9/2 pairs of rows) .
  • the average CPU consumption per row on a merge step measures queries that are modifying volatile tables or pet tables.
  • the scoring system may not count all query steps in calculating the average CPU consumption per row on a merge step, but instead may count only query steps with CPU consumption greater than some particular threshold level (e.g., 10K operations or 1 OK sets of operations) .
  • the average CPU consumption per database row modified may be estimated or directly measured.
  • the score for the average CPU consumption per row on a merge step is better the lower the measured (or estimated) value of the average CPU consumption per row on a merge step is.
  • the method 300 scores (at 315) a set of queries based on a third CPU efficiency characteristic, the average parallel efficiency (PE) on a merge step.
  • the last step is a “merge Step” that merges a result of the search into a target table. Ifthe average PE for merge steps in the query is low, then the Primary Index (PI) of target table is not evenly distributed (e.g., some PI values identify many more rows than other PI values) . This affects efficiency for update, insert, delete, create table as select, operations etc.
  • PI Primary Index
  • the method 300 scores (at 320) a set of queries based on a fourth CPU efficiency characteristic, the CPU efficiency ranking percentile among users.
  • This characteristic reflects the percentage of wasted CPU time (or skew overhead) in the total effective CPU time among all users. That is, the CPU efficiency is equal to the sum of the total CPU time divided by the sum of the effective CPU time. This may be the sum across all queries, or the sum across that account’s queries.
  • the average wasted CPU time per query is equal to the sum of the wasted CPU time divided by the query count (i.e., the number of queries by that user) .
  • the scoring system may count only queries with wasted CPU time greater than some threshold amount (e.g., 2000 operations or 2000 sets of operations) .
  • Wasted CPU time is defined in the scoring systems of some embodiments as the effective CPU time spent on failed queries due to “No More Spool” or “Aborted by Turbo Assembler (TASM) ” errors. This characteristic measures the relative ranking of a particular user among all users based on the queries of the particular user as compared to the queries of all users.
  • TASM Turbo Assembler
  • the scores for this characteristic are calculated on a percentile basis, the higher the percentile, the better the score. For example, a user in the 90th percentile is creating queries that are better (on average) , with respect to this characteristic, than the average queries of 90% of the other users of the system.
  • the scoring system may advise a user with a poor score for this characteristic to consider performance enhancement actions on a listed set of queries in order to reduce the skew overhead, or reduce the data volume to be processed in the queries.
  • the method 300 scores (at 325) a set of queries based on a fifth CPU efficiency characteristic fifth, a number of Single Access Module Processor (AMP) Retrieve steps.
  • This characteristic measures the maximum number of single AMP Retrieve steps in a single query. If a particular query requires checking a large number of primary indexes, then that query will involve a correspondingly large number of “single amp” steps. All of these single amp steps run in serial mode, which is very inefficient. For example, first, the query causes a single-AMP Retrieve step from all partitions of a first table, then a single-AMP retrieve step from all partitions of a second table, then a third, and so on.
  • AMP Single Access Module Processor
  • the scoring system may recommend that a user with a poor score for this characteristic determine whether their query has a very long in-list which contains a lot of PI values. If the query has a very long in-list which contains a lot of PI values, then the user should upload those PI values into an auxiliary table (e.g., a pet table) , then join the pet table into the original table to improve efficiency and reduce the maximum number of single AMP Retrieve steps in a single query.
  • an auxiliary table e.g., a pet table
  • the second category of characteristics is CPU consumption.
  • the CPU consumption categories are less heavily weighted than the previously described CPU efficiency categories (e.g., the CPU consumption categories may provide a combined total of 20% of the total score for the account) .
  • the method 300 scores (at 330) a set of queries based on a first CPU consumption characteristic, the percentage of “translate” or “cast” keywords in a Join Condition.
  • An SQL query sometimes requires Joining two tables or columns with different character sets or different data types (e.g., Joining a column of integers with a column of floating point numbers) .
  • Joining different data types or character sets requires converting one or both tables or columns to a common format, which consumes CPU time. Accordingly, Joining operations that include tables or columns with different character sets or data types cost extra CPU seconds versus Joining operations in which the Joined columns or tables have the same character sets and data types.
  • the scoring system may recommend using the same character set and data types in commonly joined tables or columns.
  • the scoring system may make a more specific recommendation, if the query involves Joining specific data types or character sets. For example, the system may recommend that when storing numbers, the user not use the “char” or “varchar” character set.
  • the system may recommend that if a column or table of numbers doesn′t include any numbers with a decimal part that a user set that column or table to use one of the “integer” , “bigint” , “smallint” , “byteint” or “decimal (xx, 0) ” data types.
  • the scoring system may analyze the data in a column or table to determine which of those data types would allow all the values of the table to be stored (e.g., if any numbers are large enough to require a bigint data type to store them, then the system would recommend that all Joined tables or columns use the bigint data type.
  • the method 300 scores (at 335) a set of queries based on a second CPU consumption characteristic, the percentage of queries with a partition filter.
  • Partitioning is the division of large tables in a database into multiple smaller parts. For example a table of sales data may be partitioned by month and year of the sale.
  • a query that filters on the partitioned characteristic requires the system to scan a particular partition rather than the entire table. This reduces the amount of data that much be searched for the query. Accordingly, a query that uses a partition filter is more efficient and therefore faster than a query that does not use a partition filter. Higher percentages of partition filters result in higher scores.
  • the scoring system may score the account based on the number of queries with partition filters versus the total number of queries by that account. This measurement may encourage a user who accesses many non-partitioned tables to request that the tables he accesses be appropriately partitioned.
  • the scoring system may score the account based on the number of queries with partition filters versus the total number of queries made on tables that are actually partitioned tables. This alternative avoids lowering an account’s score based on queries accessing tables which are not partitioned (and which therefore could not benefit from a partition filter.
  • the scores for this characteristic are better for higher percentages of queries with partition filters.
  • the scoring system may advise the user to add filter conditions on the partition columns of tables accessed by a particular set of queries.
  • the method 300 scores (at 340) a set of queries based on a third CPU consumption characteristic, an average CPU consumption per row scanned on window function.
  • Some queries use window functions to identify the values of entries in rows preceding or following a particular row.
  • a table may have a column with first names (in successive rows) of “Able” , “Baker” , “Charlie” , “David” , “Everett” , “Frank” , “George” , and the like.
  • a window function operating on the row including “Baker” and identifying an ordered list of a window including the immediately preceding and immediately following name would provide the ordered list “Able, Baker, Charlie” .
  • window function operating on the row including “Everett” would provide the ordered list “David, Everett, Frank” .
  • Other window functions may include n preceding rows and m following rows where n and m are integers.
  • the window function can be used to generate a moving average from a given month and the month (s) preceding and/or following the given month.
  • the average CPU consumption per row scanned for queries that use window functions can be measured and a score provided.
  • the scoring system may only count query steps with CPU consumption greater than some threshold (e.g., greater than 10K operations or 10K sets of operations) .
  • some threshold e.g., greater than 10K operations or 10K sets of operations
  • the scoring system may recommend that a user collect stats on the relevant column in the window function in order to get better performance (e.g., by using a diagnostic command before running the actual query.
  • a diagnostic command for queries without such collected statistics (via the diagnostic command) an all-rows scan is assumed to be redistributed by value to all AMPs.
  • an all-rows scan is redistributed by hash code to all AMPs, for greater efficiency.
  • the method 300 scores (at 345) a set of queries based on a fourth CPU consumption characteristic, a maximum daily total query count. As the name suggests, this is the maximum of the daily total number of queries from a particular account.
  • the scoring system may count only “Insert” , “Update” , “Delete” and “Merge” queries, rather than counting some or all other types of queries (as in alternate embodiments) . It is not efficient to execute large amount of transactional statements in an analytic platform. Accordingly, some SQL database systems provide one or more utilities to load data in a batch mode rather than loading each value as a separate query.
  • Some database systems include a user interface that allows a user to upload a large volume of data into the database in one or more combined operations. The performance of such utilities operated by the user interfaces is more efficient than a multiple single-row-operations, each performed as a separate query.
  • the scoring system may recommend that the user use such a data loader utility to upload data to a datahub and/or other database structure.
  • a maximum daily query count other embodiments base the score on maximum counts for other periods, such as a maximum count for a longer period (e.g., per week) or for a shorter period (e.g., maximum count per hour) .
  • the method 300 then generates (at 350) a composite score.
  • the composite score is a weighted average of the individual scores for each characteristic described above.
  • the weights of the average may be evenly distributed or may be heavily weighted toward one characteristic (e.g., one characteristic is weighted at 70% and the rest at 3%-5%)
  • the method identifies (at 355) a set of one or more query improvements that would improve the account’s score.
  • the method may identify which improvements would improve the composite score the most. For example, if an account was scoring poorly on a heavily weighted characteristic (e.g., Average Query Wasted CPUTime Ranking Percentile among All Users, in an embodiment that weighs that characteristic at 70%) , the scoring system would identify an improvement to that characteristic as potentially producing the greatest improvement.
  • a heavily weighted characteristic e.g., Average Query Wasted CPUTime Ranking Percentile among All Users, in an embodiment that weighs that characteristic at 70%
  • the method 300 then provides (at 360) a set of recommended improvements to the queries of that account.
  • the recommended improvements may be provided in order of greatest potential improvement to least potential improvement.
  • the recommended improvements may alternatively be provided in order of heaviest weighted characteristic score to lightest weighted characteristic score.
  • FIG. 4 conceptually illustrates a method 400 of some embodiments of automatically making recommendations for improvements based on a particular score.
  • the method 400 scores (at 410) a set of one or more queries according to a particular characteristic.
  • the characteristic may be any of the characteristics described above with respect to FIG. 3.
  • the method determines (at 420) whether the score is worse than a threshold score. For some characteristics, a score higher than the threshold score is worse than the threshold score. For other characteristics, a score less than the threshold score is worse than the threshold score. Ifthe score is worse than the threshold score, the method recommends (at 430) an improvement to the query/queries in the set of queries, then proceeds to operation 440. Otherwise, the method does not make such a recommendation for that characteristic and proceeds to operation 440.
  • the method 400 determines (at 440) whether another characteristic has yet to be scored. If another characteristic has not been scored, the method 400 then returns to operation 410 to score the next characteristic. If all characteristics have been scored, the method 400 ends.
  • FIGs. 1-4 The modules, methods, applications and so forth described in conjunction with FIGs. 1-4 are implemented in some embodiments in the context of a set of machines and an associated software architecture.
  • the sections below describe representative software architecture (s) and machine (e.g., hardware) architecture that are suitable for use with the disclosed embodiments.
  • Software architectures are used in conjunction with hardware architectures to create devices and machines tailored to particular purposes. For example, a particular hardware architecture coupled with a particular software architecture will create a mobile device, such as a mobile phone, tablet device, or so forth. A slightly different hardware and software architecture may yield a smart device for use in the “internet of things. ” While yet another combination produces a server computer for use within a cloud computing architecture. Not all combinations of such software and hardware architectures are presented here as those of skill in the art can readily understand how to implement the invention in different contexts from the disclosure contained herein.
  • FIG. 5 is a block diagram 500 illustrating a representative software architecture 502, which may be used in conjunction with various hardware architectures herein described.
  • FIG. 5 is merely a non-limiting example of a software architecture and it will be appreciated that many other architectures may be implemented to facilitate the functionality described herein.
  • the software architecture 502 may be executing on hardware such as machine 600 of FIG. 6 that includes, among other things, processors 610, memory 630, and I/O components 650.
  • a representative hardware layer 504 is illustrated and can represent, for example, the machine 600 of FIG. 6.
  • the representative hardware layer 504 comprises one or more processing units 506 having associated executable instructions 508.
  • Executable instructions 508 represent the executable instructions of the software architecture 502, including implementation of the methods, modules and so forth of FIGs. 1-4.
  • Hardware layer 504 also includes memory and/or storage modules 510, which also have executable instructions 508.
  • Hardware layer 504 may also comprise other hardware as indicated by 512 which represents any other hardware of the hardware layer 504, such as the other hardware illustrated as part of machine 600.
  • the software 502 may be conceptualized as a stack of layers where each layer provides particular functionality.
  • the software 502 may include layers such as an operating system 514, libraries 516, frameworks/middleware 518, applications 520 and presentation layer 522.
  • the applications 520 and/or other components within the layers may invoke application programming interface (API) calls 524 through the software stack and receive a response, returned values, and so forth illustrated as messages 526 in response to the API calls 524.
  • API application programming interface
  • the layers illustrated are representative in nature and not all software architectures have all layers. For example, some mobile or special purpose operating systems may not provide a frameworks/middleware layer 518, while others may provide such a layer. Other software architectures may include additional or different layers.
  • the operating system 514 may manage hardware resources and provide common services.
  • the operating system 514 may include, for example, a kernel 528, services 530, and drivers 532.
  • the kernel 528 may act as an abstraction layer between the hardware and the other software layers.
  • the kernel 528 may be responsible for memory management, processor management (e.g., scheduling) , component management, networking, security settings, and so on.
  • the services 530 may provide other common services for the other software layers.
  • the drivers 532 may be responsible for controlling or interfacing with the underlying hardware.
  • the drivers 532 may include display drivers, camera drivers, drivers, flash memory drivers, serial communication drivers (e.g., Universal Serial Bus (USB) drivers) , drivers, audio drivers, power management drivers, and so forth depending on the hardware configuration.
  • USB Universal Serial Bus
  • the libraries 516 may provide a common infrastructure that may be utilized by the applications 520 and/or other components and/or layers.
  • the libraries 516 typically provide functionality that allows other software modules to perform tasks in an easier fashion than to interface directly with the underlying operating system 514 functionality (e.g., kernel 528, services 530 and/or drivers 532) .
  • the libraries 516 may include system 534 libraries (e.g., C standard library) that may provide functions such as memory allocation functions, string manipulation functions, mathematic functions, and the like.
  • the libraries 516 may include APl libraries 536 such as media libraries (e.g., libraries to support presentation and manipulation of various media format such as MPREG4, H.
  • graphics libraries e.g., an OpenGL framework that may be used to render 2D and 3D in a graphic content on a display
  • database libraries e.g., SQLite that may provide various relational database functions
  • web libraries e.g., WebKit that may provide web browsing functionality
  • the libraries 516 may also include a wide variety of other libraries 538 to provide many other APIs to the applications 520 and other software components/modules.
  • the frameworks 518 may provide a higher-level common infrastructure that may be utilized by the applications 520 and/or other software components/modules.
  • the frameworks 518 may provide various graphic user interface (GUI) functions, high-level resource management, high-level location services, and so forth.
  • GUI graphic user interface
  • the frameworks 518 may provide a broad spectrum of other APIs that may be utilized by the applications 520 and/or other software components/modules, some of which may be specific to a particular operating system or platform.
  • the applications 520 includes built-in applications 540 and/or third party applications 542.
  • built-in applications 540 may include, but are not limited to, a contacts application, a browser application, a book reader application, a location application, a media application, a messaging application, and/or a game application.
  • Third party applications 542 may include any of the built in applications as well as a broad assortment of other applications. In one specific example, the third party applications may include an SQL query interface and/or an SQL tuning assistant.
  • the third party application 542 may be mobile software running on a mobile operating system such as iOS TM , Android TM , Phone, or other mobile operating systems.
  • the third party application 542 may invoke the API calls 524 provided by the mobile operating system such as operating system 514 to facilitate functionality described herein.
  • the applications 520 may utilize built in operating system functions (e.g., kernel 528, services 530 and/or drivers 532) , libraries (e.g., system 534, APIs 536, and other libraries 538) , frameworks/middleware 518 to create user interfaces to interact with users of the system. Alternatively, or additionally, in some systems interactions with a user may occur through a presentation layer, such as presentation layer 544. In these systems, the application/module “logic” can be separated from the aspects of the application/module that interact with a user.
  • a virtual machine creates a software environment where applications/modules can execute as if they were executing on a hardware machine (such as the machine of FIG. 6, for example) .
  • a virtual machine is hosted by a host operating system (operating system 514 in FIG. 6) and typically, although not always, has a virtual machine monitor 546, which manages the operation of the virtual machine as well as the interface with the host operating system (i.e., operating system 514) .
  • a software architecture executes within the virtual machine such as an operating system 550, libraries 552, frameworks/middleware 554, applications 556 and/or presentation layer 558. These layers of software architecture executing within the virtual machine 548 can be the same as corresponding layers previously described or may be different.
  • FIG. 6 is a block diagram illustrating components of a machine 600, according to some example embodiments, able to read instructions from a machine-readable medium (e.g., a machine-readable storage medium) and perform any one or more of the methodologies discussed herein.
  • FIG. 6 shows a diagrammatic representation of the machine 600 in the example form of a computer system, within which instructions 616 (e.g., software, a program, an application, an applet, an app, or other executable code) for causing the machine 600 to perform any one or more of the methodologies discussed herein may be executed.
  • instructions 616 e.g., software, a program, an application, an applet, an app, or other executable code
  • the instructions may cause the machine to execute the flow diagrams of FIGs. 3-4.
  • the instructions may implement the Parsing Module 200, Update Module 210, Insert Module 220, Delete Module 230, Create Table as Select Module 240, Joining Module 250, and/or Execution Plan Module 260 of FIG. 2, and so forth.
  • the instructions transform the general, non-programmed machine into a particular machine programmed to carry out the described and illustrated functions in the manner described.
  • the machine 600 operates as a standalone device or may be coupled (e.g., networked) to other machines. In a networked deployment, the machine 600 may operate in the capacity of a server machine or a client machine in a server-client network environment, or as a peer machine in a peer-to-peer (or distributed) network environment.
  • the machine 600 may comprise, but not be limited to, a server computer, a client computer, a personal computer (PC) , a tablet computer, a laptop computer, a netbook, a set-top box (STB) , a personal digital assistant (PDA) , an entertainment media system, a cellular telephone, a smart phone, a mobile device, a wearable device (e.g., a smart watch) , a smart home device (e.g., a smart appliance) , other smart devices, a web appliance, a network router, a network switch, a network bridge, or any machine capable of executing the instructions 616, sequentially or otherwise, that specify actions to be taken by machine 600.
  • a server computer a client computer
  • PC personal computer
  • PDA personal digital assistant
  • 1-4 may be implemented on one or more servers with only the display of the results being implemented on a client device.
  • the scoring system may collect data using servers but analyze that data on a client device and display the results on the client device.
  • machine shall also be taken to include a collection of machines 600 that individually or jointly execute the instructions 616 to perform any one or more of the methodologies discussed herein.
  • the machine 600 may include processors 610, memory 630, and I/O components 650, which may be configured to communicate with each other such as via a bus 602.
  • the processors 610 e.g., a Central Processing Unit (CPU) , a Reduced Instruction Set Computing (RISC) processor, a Complex Instruction Set Computing (CISC) processor, a Graphics Processing Unit (GPU) , a Digital Signal Processor (DSP) , an Application Specific Integrated Circuit (ASIC) , a Radio-Frequency Integrated Circuit (RFIC) , another processor, or any suitable combination thereof
  • the processors 610 may include, for example, processor 612 and processor 614 that may execute instructions 616.
  • processor is intended to include multi-core processor that may comprise two or more independent processors (sometimes referred to as “cores” ) that may execute instructions contemporaneously.
  • FIG. 6 shows multiple processors, the machine 600 may include a single processor with a single core, a single processor with multiple cores (e.g., a multi-core process) , multiple processors with a single core, multiple processors with multiples cores, or any combination thereof.
  • the memory/storage 630 may include a memory 632, such as a main memory, or other memory storage, and a storage unit 636, both accessible to the processors 610 such as via the bus 602.
  • the storage unit 636 and memory 632 store the instructions 616 embodying any one or more of the methodologies or functions described herein.
  • the instructions 616 may also reside, completely or partially, within the memory 632, within the storage unit 636, within at least one of the processors 610 (e.g., within the processor’s cache memory) , or any suitable combination thereof, during execution thereof by the machine 600. Accordingly, the memory 632, the storage unit 636, and the memory of processors 610 are examples of machine-readable media.
  • machine-readable medium means a device able to store instructions and data temporarily or permanently and may include, but is not be limited to, random-access memory (RAM) , read-only memory (ROM) , buffer memory, flash memory, optical media, magnetic media, cache memory, other types of storage (e.g., Erasable Programmable Read-Only Memory (EEPROM) ) and/or any suitable combination thereof.
  • RAM random-access memory
  • ROM read-only memory
  • buffer memory flash memory
  • optical media magnetic media
  • cache memory other types of storage
  • EEPROM Erasable Programmable Read-Only Memory
  • machine-readable medium should be taken to include a single medium or multiple media (e.g., a centralized or distributed database, or associated caches and servers) able to store instructions 616.
  • machine-readable medium shall also be taken to include any medium, or combination of multiple media, that is capable of storing instructions (e.g., instructions 616) for execution by a machine (e.g., machine 600) , such that the instructions, when executed by one or more processors of the machine 600 (e.g., processors 610) , cause the machine 600 to perform any one or more of the methodologies described herein.
  • a “machine-readable medium” refers to a single storage apparatus or device, as well as “cloud-based” storage systems or storage networks that include multiple storage apparatus or devices.
  • the term “machine-readable medium” excludes signals per se.
  • the I/O components 650 may include a wide variety of components to receive input, provide output, produce output, transmit information, exchange information, capture measurements, and so on.
  • the specific I/O components 650 that are included in a particular machine will depend on the type of machine. For example, portable machines such as mobile phones will likely include a touch input device or other such input mechanisms, while a headless server machine will likely not include such a touch input device. It will be appreciated that the I/O components 650 may include many other components that are not shown in FIG. 6.
  • the I/O components 650 are grouped according to functionality merely for simplifying the following discussion and the grouping is in no way limiting. In various example embodiments, the I/O components 650 may include output components 652 and input components 654.
  • the output components 652 may include visual components (e.g., a display such as a plasma display panel (PDP) , a light emitting diode (LED) display, a liquid crystal display (LCD) , a projector, or a cathode ray tube (CRT) ) , acoustic components (e.g., speakers) , haptic components (e.g., a vibratory motor, resistance mechanisms) , other signal generators, and so forth.
  • visual components e.g., a display such as a plasma display panel (PDP) , a light emitting diode (LED) display, a liquid crystal display (LCD) , a projector, or a cathode ray tube (CRT)
  • acoustic components e.g., speakers
  • haptic components e.g., a vibratory motor, resistance mechanisms
  • the input components 654 may include alphanumeric input components (e.g., a keyboard, a touch screen configured to receive alphanumeric input, a photo-optical keyboard, or other alphanumeric input components) , point based input components (e.g., a mouse, a touchpad, a trackball, a joystick, a motion sensor, or other pointing instrument) , tactile input components (e.g., a physical button, a touch screen that provides location and/or force of touches or touch gestures, or other tactile input components) , audio input components (e.g., a microphone) , and the like.
  • alphanumeric input components e.g., a keyboard, a touch screen configured to receive alphanumeric input, a photo-optical keyboard, or other alphanumeric input components
  • point based input components e.g., a mouse, a touchpad, a trackball, a joystick, a motion sensor, or other pointing instrument
  • tactile input components e.
  • the I/O components 650 may include biometric components 656, motion components 658, environmental components 660, or position components 662 among a wide array of other components.
  • the biometric components 656 may include components to detect expressions (e.g., hand expressions, facial expressions, vocal expressions, body gestures, or eye tracking) , measure biosignals (e.g., blood pressure, heart rate, body temperature, perspiration, or brain waves) , identify a person (e.g., voice identification, retinal identification, facial identification, fingerprint identification, or electroencephalogram based identification) , and the like.
  • the motion components 658 may include acceleration sensor components (e.g., accelerometer) , gravitation sensor components, rotation sensor components (e.g., gyroscope) , and so forth.
  • the environmental components 660 may include, for example, illumination sensor components (e.g., photometer) , temperature sensor components (e.g., one or more thermometer that detect ambient temperature) , humidity sensor components, pressure sensor components (e.g., barometer) , acoustic sensor components (e.g., one or more microphones that detect background noise) , proximity sensor components (e.g., infrared sensors that detect nearby objects) , gas sensors (e.g., gas detection sensors to detection concentrations of hazardous gases for safety or to measure pollutants in the atmosphere) , or other components that may provide indications, measurements, or signals corresponding to a surrounding physical environment.
  • illumination sensor components e.g., photometer
  • temperature sensor components e.g., one or more thermometer that detect ambient temperature
  • humidity sensor components e.g
  • the position components 662 may include location sensor components (e.g., a Global Position System (GPS) receiver component) , altitude sensor components (e.g., altimeters or barometers that detect air pressure from which altitude may be derived) , orientation sensor components (e.g., magnetometers) , and the like.
  • location sensor components e.g., a Global Position System (GPS) receiver component
  • altitude sensor components e.g., altimeters or barometers that detect air pressure from which altitude may be derived
  • orientation sensor components e.g., magnetometers
  • the I/O components 650 may include communication components 664 operable to couple the machine 600 to a network 680 or devices 670 via coupling 682 and coupling 672 respectively.
  • the communication components 664 may include a network interface component or other suitable device to interface with the network 680.
  • communication components 664 may include wired communication components, wireless communication components, cellular communication components, Near Field Communication (NFC) components, components (e.g., Low Energy) , components, and other communication components to provide communication via other modalities.
  • the devices 670 may be another machine or any of a wide variety of peripheral devices (e.g., a peripheral device coupled via a Universal Serial Bus (USB) ) .
  • USB Universal Serial Bus
  • the communication components 664 may detect identifiers or include components operable to detect identifiers.
  • the communication components 664 may include Radio Frequency Identification (RFID) tag reader components, NFC smart tag detection components, optical reader components (e.g., an optical sensor to detect one-dimensional bar codes such as Universal Product Code (UPC) bar code, multi-dimensional bar codes such as Quick Response (QR) code, Aztec code, Data Matrix, Dataglyph, MaxiCode, PDF417, Ultra Code, UCC RSS-2D bar code, and other optical codes) , or acoustic detection components (e.g., microphones to identify tagged audio signals) .
  • RFID Radio Frequency Identification
  • NFC smart tag detection components e.g., an optical sensor to detect one-dimensional bar codes such as Universal Product Code (UPC) bar code, multi-dimensional bar codes such as Quick Response (QR) code, Aztec code, Data Matrix, Dataglyph, MaxiCode, PDF417, Ultra Code, UCC RSS-2D bar code, and other optical codes
  • one or more portions of the network 680 may be an ad hoc network, an intranet, an extranet, a virtual private network (VPN) , a local area network (LAN) , a wireless LAN (WLAN) , a wide area network (WAN) , a wireless WAN (WWAN) , a metropolitan area network (MAN) , the Internet, a portion of the Internet, a portion of the Public Switched Telephone Network (PSTN) , a plain old telephone service (POTS) network, a cellular telephone network, a wireless network, a network, another type of network, or a combination of two or more such networks.
  • VPN virtual private network
  • LAN local area network
  • WLAN wireless LAN
  • WAN wide area network
  • WWAN wireless WAN
  • MAN metropolitan area network
  • PSTN Public Switched Telephone Network
  • POTS plain old telephone service
  • the network 680 or a portion of the network 680 may include a wireless or cellular network and the coupling 682 may be a Code Division Multiple Access (CDMA) connection, a Global System for Mobile communications (GSM) connection, or other type of cellular or wireless coupling.
  • CDMA Code Division Multiple Access
  • GSM Global System for Mobile communications
  • the coupling 682 may implement any of a variety of types of data transfer technology, such as Single Carrier Radio Transmission Technology (1xRTT) , Evolution-Data Optimized (EVDO) technology, General Packet Radio Service (GPRS) technology, Enhanced Data rates for GSM Evolution (EDGE) technology, third Generation Partnership Project (3 GPP) including 3 G, fourth generation wireless (4G) networks, Universal Mobile Telecommunications System (UMTS) , High Speed Packet Access (HSPA) , Worldwide Interoperability for Microwave Access (WiMAX) , Long Term Evolution (LTE) standard, others defined by various standard setting organizations, other long range protocols, or other data transfer technology.
  • 1xRTT Single Carrier Radio Transmission Technology
  • GPRS General Packet Radio Service
  • EDGE Enhanced Data rates for GSM Evolution
  • 3 GPP Third Generation Partnership Project
  • 4G fourth generation wireless (4G) networks
  • Universal Mobile Telecommunications System (UMTS) Universal Mobile Telecommunications System
  • High Speed Packet Access HSPA
  • WiMAX Worldwide Intero
  • the instructions 616 may be transmitted or received over the network 680 using a transmission medium via a network interface device (e.g., a network interface component included in the communication components 664) and utilizing any one of a number of well-known transfer protocols (e.g., hypertext transfer protocol (HTTP) ) .
  • HTTP hypertext transfer protocol
  • the instructions 616 may be transmitted or received using a transmission medium via the coupling 672 (e.g., a peer-to-peer coupling) to devices 670.
  • the term “transmission medium” shall be taken to include any intangible medium that is capable of storing, encoding, or carrying instructions 616 for execution by the machine 600, and includes digital or analog communications signals or other intangible medium to facilitate communication of such software.
  • inventive subject matter has been described with reference to specific example embodiments, various modifications and changes may be made to these embodiments without departing from the broader scope of embodiments of the present disclosure.
  • inventive subject matter may be referred to herein, individually or collectively, by the term “invention” merely for convenience and without intending to voluntarily limit the scope of this application to any single disclosure or inventive concept if more than one is, in fact, disclosed.
  • the term “or” may be construed in either an inclusive or exclusive sense. Moreover, plural instances may be provided for resources, operations, or structures described herein as a single instance. Additionally, boundaries between various resources, operations, modules, engines, and data stores are somewhat arbitrary, and particular operations are illustrated in a context of specific illustrative configurations. Other allocations of functionality are envisioned and may fall within a scope of various embodiments of the present disclosure. In general, structures and functionality presented as separate resources in the example configurations may be implemented as a combined structure or resource. Similarly, structures and functionality presented as a single resource may be implemented as separate resources. These and other variations, modifications, additions, and improvements fall within a scope of embodiments of the present disclosure as represented by the appended claims. The specification and drawings are, accordingly, to be regarded in an illustrative rather than a restrictive sense.

Abstract

L'invention concerne un système et un procédé de notation et de recommandation de requête SQL. Le système et le procédé génèrent de multiples notes individuelles pour des ensembles de requêtes conformément à diverses caractéristiques se rapportant à l'efficacité de l'UC et à la consommation de l'UC. Les notes individuelles pour chaque ensemble de requêtes sont ensuite utilisées pour générer une note composite pour les requêtes de l'ensemble. En se basant sur la note composite et/ou les notes individuelles, le système et le procédé recommandent des étapes pour améliorer les notes et l'efficacité des requêtes.
PCT/CN2015/085061 2015-07-24 2015-07-24 Recommandations et notation de performances sql WO2017015792A1 (fr)

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