CN111858606A - Data processing method, apparatus and electronic equipment - Google Patents

Abstract

The present invention provides a data processing method, device and electronic equipment, comprising: obtaining a preset index range, wherein the preset index range is a preset range of index key values for indexing data in a database; The preset index range is divided into multiple data index ranges, and a corresponding B-tree is constructed for each data index range; a specified data operation is performed on the data based on each data index range and the B-tree corresponding to each data index range. As can be seen from the above description, after the preset index range is divided into multiple data index ranges, when the specified data operation is performed on the data in the database, it can be executed concurrently by partition, which improves the efficiency of data processing and saves the time of data processing. It alleviates the technical problems of low efficiency and serious time-consuming of existing data processing methods.

Description

Data processing method, apparatus and electronic equipment

technical field

The present invention relates to the technical field of computers, and in particular, to a data processing method, apparatus and electronic device.

Background technique

With the rapid development of mobile Internet, social network, and e-commerce, the amount of information is increasing day by day, and a large amount of data is also stored in the database. Among them, many data tables in the database store tens of millions or even hundreds of millions of data. When processing data, it is often necessary to rely on indexes. At present, commonly used indexes include B-tree or B+ tree.

For a data table with massive data, it corresponds to a B-tree. When a data query or data update is performed, the data query or data update is often implemented by binary search based on the B-tree. The number of queries for a single search above is not many, but in the case of high concurrency (for example, 1000 data queries are performed at the same time), the number of queries will increase exponentially (number of queries = number of queries for a single search * number of concurrent queries). In addition, when a data update (including data writing and data reading) is performed on the data table, the entire B-tree corresponding to the data table is in a locked state. When the data table is updated 1000 times at the same time, The process of data update can only be carried out in series, which seriously affects the efficiency of data update and takes a lot of time.

SUMMARY OF THE INVENTION

In view of this, the purpose of the present invention is to provide a data processing method, device and electronic device, so as to alleviate the technical problems of low efficiency and serious time-consuming of the existing data processing method.

In a first aspect, an embodiment of the present invention provides a data processing method, including: acquiring a preset index range, where the preset index range is a preset range of index key values for indexing data in a database interval; the preset index range is divided into a plurality of data index ranges, and a corresponding B-tree is constructed for each of the data index ranges; based on each of the data index ranges and the corresponding data index ranges The B-tree performs specified data operations on the data.

Further, performing a specified data operation on the data based on each of the data index ranges and the B-tree corresponding to each of the data index ranges includes: when the specified data operation is a data update operation, determining the data of the data to be updated. index key value; determine the data index range to which the data index key value of the data to be updated belongs in the multiple data index ranges; construct the B corresponding to the data index range to which the data index key value of the data to be updated belongs The first target B-tree node on the tree; the data to be updated is updated according to the index key value of the first target B-tree node and the storage location corresponding to the first target B-tree node.

Further, performing a specified data operation on the data based on each of the data index ranges and the B-tree corresponding to each of the data index ranges also includes: when the specified data operation is a data read operation, determining to be read the data index key value of the data; determine the data index range to which the data index key value of the data to be read belongs in the multiple data index ranges; determine the data index to which the data index key value of the data to be read belongs B-tree corresponding to the range; traverse the B-tree corresponding to the data index range to which the data index key value of the data to be read belongs, to obtain the storage location of the data to be read in the database, and according to the The storage location reads the data to be read.

Further, before performing a specified data operation on the data based on each of the data index ranges and the B-tree corresponding to each of the data index ranges, the method further includes: determining target data corresponding to the specified data operation Index range; control the B-tree corresponding to the target data index range to be in a locked state.

Further, controlling the B-tree corresponding to the target data index range to be in a locked state includes: setting a lock flag for the B-tree corresponding to the target data index range; controlling the corresponding B-tree of the target data index range according to the lock flag The B-tree is locked.

In a second aspect, an embodiment of the present invention further provides a data processing method, including: obtaining a data index key value of data to be read; determining a first target data index in a plurality of data index ranges according to the data index key value Scope, wherein each data index scope includes the data index key value of each data in the database; each data index scope corresponds to a B-tree; based on the first target data index scope and its corresponding first target B-tree the storage location of the data to be read in the database, and read the data to be read according to the storage location.

Further, each data index range corresponds to a B-tree pointer; determining the storage location of the data to be read in the database based on the first target data index range and its corresponding first target B-tree includes: according to The B-tree pointer of the first target data index range determines the first target B-tree; and the storage position of the data to be read in the database is determined based on the first target B-tree.

Further, before determining the storage location of the data to be read in the database based on the first target B-tree, the method further includes: setting a lock flag for the first target B-tree; according to the The lock flag controls the first target B-tree to be in a locked state.

In a third aspect, an embodiment of the present invention further provides a data processing method, including: obtaining a data index key value of data to be updated; The second target data index range, wherein each data index range includes the data index key value of each data in the database; each data index range corresponds to a B-tree; constructs the B-tree corresponding to the second target data index range The second target B-tree node on the second target B-tree node; according to the index key value of the second target B-tree node and the storage location corresponding to the second target B-tree node, update the data to be updated.

Further, before constructing the second target B-tree node on the B-tree corresponding to the second target data index range, the method further includes: setting the B-tree corresponding to the second target data index range Locking identifier; controlling the B-tree corresponding to the second target data index range to be in a locked state according to the locking identifier.

In a fourth aspect, an embodiment of the present invention further provides a data processing device, including: a first obtaining unit, configured to obtain a preset index range, wherein the preset index range is a preset pair of data in a database The range interval of the index key value for indexing; the dividing unit is used to divide the preset index range into a plurality of data index ranges, and build a corresponding B-tree for each of the data index ranges; an operation execution unit, using for performing a specified data operation on the data based on each of the data index ranges and the B-tree corresponding to each of the data index ranges.

In a fifth aspect, an embodiment of the present invention further provides a data processing apparatus, including: a second obtaining unit, configured to obtain a data index key value of the data to be read; a first determining unit, configured to obtain a data index key according to the data index key The value determines the first target data index range in multiple data index ranges, wherein each data index range includes the data index key value of each data in the database; each data index range corresponds to a B-tree; the second determination unit, using The storage position of the data to be read in the database is determined based on the first target data index range and the corresponding first target B-tree, and the data to be read is read according to the storage position.

In a sixth aspect, an embodiment of the present invention further provides a data processing device, comprising: a third obtaining unit, configured to obtain a data index key value of the data to be updated; a third determining unit, configured to obtain a data index key value according to the data index key value Determine the second target data index range to which the data to be updated belongs in a plurality of data index ranges, wherein each data index range includes the data index key value of each data in the database; each data index range corresponds to a B-tree; The construction unit is used to construct the second target B-tree node on the B-tree corresponding to the second target data index range; the updating unit is used to construct the second target B-tree node according to the index key value and the The storage location corresponding to the second target B-tree node is updated, and the data to be updated is updated.

In a seventh aspect, an embodiment of the present invention further provides an electronic device, including a memory, a processor, and a computer program stored on the memory and running on the processor, where the processor executes the computer program When the method described in any one of the first aspect, or the second aspect, or the third aspect is implemented.

In an eighth aspect, an embodiment of the present invention further provides a computer-readable medium having a non-volatile program code executable by a processor, the program code causing the processor to execute the first aspect above, or the second aspect, or the method of any one of the third aspects.

In the embodiment of the present invention, first, a preset index range is obtained, where the preset index range is a preset range of index key values for indexing data in the database; the preset index range is divided into multiple The data index range is constructed, and a corresponding B-tree is constructed for each data index range; the specified data operation is performed on the data based on each data index range and the B-tree corresponding to each data index range. It can be seen from the above description that after the preset index range is divided into multiple data index ranges, when the specified data operation is performed on the data in the database, it can be executed concurrently by partition, which improves the efficiency of data processing and saves the time of data processing. The technical problems of low efficiency and serious time-consuming of the existing data processing methods are alleviated.

Other features and advantages of the present invention will be set forth in the description which follows, and in part will be apparent from the description, or may be learned by practice of the invention. The objectives and other advantages of the invention will be realized and attained by the structure particularly pointed out in the description, claims and drawings.

In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, preferred embodiments are given below, and are described in detail as follows in conjunction with the accompanying drawings.

Description of drawings

In order to illustrate the specific embodiments of the present invention or the technical solutions in the prior art more clearly, the following briefly introduces the accompanying drawings that need to be used in the description of the specific embodiments or the prior art. Obviously, the accompanying drawings in the following description The drawings are some embodiments of the present invention. For those of ordinary skill in the art, other drawings can also be obtained based on these drawings without creative efforts.

1 is a flowchart of a data processing method provided by an embodiment of the present invention;

2 is a schematic diagram of a linked list provided by an embodiment of the present invention;

3 is a flowchart of performing a specified data operation on data in a database provided by an embodiment of the present invention;

4 is another flowchart of performing a specified data operation on data in a database provided by an embodiment of the present invention;

5 is another flowchart of performing a specified data operation on data in a database provided by an embodiment of the present invention;

6 is a flowchart of a data processing method provided by an embodiment of the present invention;

7 is a flowchart of a data processing method provided by an embodiment of the present invention;

FIG. 8 is a schematic diagram of a data processing apparatus according to an embodiment of the present invention;

9 is a schematic diagram of another data processing apparatus provided by an embodiment of the present invention;

10 is a schematic diagram of still another data processing apparatus provided by an embodiment of the present invention;

FIG. 11 is a schematic diagram of an electronic device according to an embodiment of the present invention.

Detailed ways

In order to make the purposes, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are part of the embodiments of the present invention, but not all of them. example. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

Example 1:

According to an embodiment of the present invention, an embodiment of a data processing method is provided. It should be noted that the steps shown in the flowchart of the accompanying drawings may be executed in a computer system such as a set of computer-executable instructions, and, although A logical order is shown in the flowcharts, but in some cases steps shown or described may be performed in an order different from that herein.

FIG. 1 is a flowchart of a data processing method according to an embodiment of the present invention. As shown in FIG. 1 , the method includes the following steps:

Step S102 , obtaining a preset index range, wherein the preset index range is a preset range of index key values for indexing data in the database.

In the present application, the preset index range may be a user-defined range of an index key value key for indexing data in the database.

For ease of understanding, with reference to Figure 2, the data processing method of the present invention is introduced:

In FIG. 2 , the range from 0 to 10099 is the preset index range. Of course, the above-mentioned preset index range is not specifically limited in this embodiment of the present invention.

Step S104: Divide the preset index range into multiple data index ranges, and build a corresponding B-tree for each data index range.

After the preset index range is obtained, the preset index range is further divided into a plurality of data index ranges. When dividing the data index range, a preset range division interval is obtained first, and then the preset index range is divided into multiple data index ranges according to the preset range division interval, wherein the multiple data index ranges can form a target linked list.

It should be noted that, the above-mentioned preset range division interval may be one or multiple. When there are multiple preset range division intervals, index ranges to be divided corresponding to each preset range division interval may be defined, and then the corresponding to-be-divided index ranges are divided according to each preset range division interval. When the preset range is divided into one interval, the divided data index ranges have the same size; when the preset range is divided into multiple intervals, the data index ranges obtained by dividing the interval according to the same preset range have the same size. , and the ranges of the data index ranges obtained by dividing the ranges according to different preset ranges are different in size.

As shown in Figure 2, the preset range is divided into 100 intervals, and the final data index range obtained by division includes: 0～99, 100～199, 200～299, 300～399, ..., 10000～10099. The target linked list formed by the data index range. Among them, each data index range of the target linked list includes a data field and a pointer field, the data in the data field is used to represent the index range, and the pointer field contains two pointers, one of which points to the B-tree corresponding to the data index range (the The pointer can be called a B-tree pointer), and another pointer points to the next node in the target linked list.

Step S106: Perform a specified data operation on the data based on each data index range and the B-tree corresponding to each data index range.

After multiple data index ranges are obtained, specified data operations can be performed on the data in the database based on each data index range and the B-tree corresponding to each data index range. When executing the above specified data operations, it can be executed concurrently by partition. For example, for 1000 data index ranges, when the specified data operation is performed, the corresponding data index ranges are different, then the specified data operation can be performed on the data within the 1000 data index range at the same time, which improves the efficiency of data processing and saves data. processing time.

In the embodiment of the present invention, first, a preset index range is obtained, where the preset index range is a preset range of index key values for indexing data in the database; the preset index range is divided into multiple The data index range is constructed, and a corresponding B-tree is constructed for each data index range; the specified data operation is performed on the data based on each data index range and the B-tree corresponding to each data index range. It can be seen from the above description that after the preset index range is divided into multiple data index ranges, when the specified data operation is performed on the data in the database, it can be executed concurrently by partition, which improves the efficiency of data processing and saves the time of data processing. The technical problems of low efficiency and serious time-consuming of the existing data processing methods are alleviated.

The above content briefly introduces the data processing method of the present invention, and the specific content involved is described in detail below.

In an optional embodiment of the present application, referring to FIG. 3 , in the above step S106, performing a specified data operation on the data based on each data index range and the B-tree corresponding to each data index range includes the following steps:

Step S301, when the specified data operation is a data update operation, determine the data index key value of the data to be updated, and obtain the first data index key value;

Step S302, in a plurality of data index ranges, determine the data index range to which the data index key value of the data to be updated belongs;

Step S303, constructing the first target B-tree node on the B-tree corresponding to the data index range to which the data index key value of the data to be updated belongs;

Step S304: Update the data to be updated according to the index key value of the first target B-tree node and the storage location corresponding to the first target B-tree node.

The following describes the process of data update with a specific example:

If the data index key value of the data to be updated is 201, it can be determined that the data index range to which the data index key value 201 belongs is the data index range from 200 to 299 in FIG. range, and then establish a corresponding B-tree node (ie, the first target B-tree node) on the B-tree corresponding to the data index range of 200 to 299, that is, according to the construction principle of the B-tree, insert the corresponding B-tree into the B-tree Node. After the first target B-tree node is established, the data to be updated can be updated according to the data index key value of the first target B-tree node and the storage location corresponding to the first target B-tree node.

In another optional embodiment of the present application, referring to FIG. 4 , in the above step S106, performing a specified data operation on the data based on each data index range and the B-tree corresponding to each data index range further includes the following steps:

Step S401, when the specified data operation is a data read operation, determine the data index key value of the data to be read;

Step S402, in a plurality of data index ranges, determine the data index range to which the data index key value of the data to be read belongs;

Step S403, determining the B-tree corresponding to the data index range to which the data index key value of the data to be read belongs;

Step S404, traverse the B-tree corresponding to the data index range to which the data index key value of the data to be read belongs, obtain the storage location of the data to be read in the database, and read the data to be read according to the storage location.

The following describes the process of data query with a specific example:

If the data index key value of the data to be read is 201, it can be determined that the data index range to which the data index key value 201 belongs is the data from 200 to 299 in FIG. index range, and then traverse the B-tree corresponding to the data index range of 200 to 299 to obtain the storage location of the data to be read in the database, and read the data to be read stored in the storage location.

In another optional embodiment of the present application, referring to FIG. 5 , before performing a specified data operation on the data based on each data index range and the B-tree corresponding to each data index range, the method further includes the following steps:

Step S501, determining the target data index range corresponding to the specified data operation;

Specifically, a plurality of data index ranges are obtained by dividing the interval according to a preset range, and then the data index key value corresponding to the specified data operation is determined, and then the target data index to which the data index key value belongs is determined in the plurality of data index ranges. range, the target data index range is the target data index range corresponding to the specified data operation. As shown in FIG. 2 , if the preset range division interval is 100, the divided data index ranges are respectively 0-99, 100-199, 200-299, 300-399, ..., 10000-10099. If the data index key value corresponding to the specified data operation is 201, it can be determined that the target data index range to which 201 belongs is 200-299, that is, the target data index range corresponding to the specified data operation is the data index range of 200-299.

Step S502, the B-tree corresponding to the control target data index range is in a locked state;

Specifically, a lock flag can be set for the B-tree corresponding to the target data index range, wherein the lock flag is used to indicate that the B-tree corresponding to the target data index range is in a locked state; The B-tree is locked.

When the B-tree corresponding to the target data index range is in a locked state, the specified data operation can be performed on the data in the database based on the target data index range and the corresponding B-tree.

If the specified data operation is a data update operation, then construct the corresponding B-tree node on the target B-tree (that is, the B-tree corresponding to the target data index range); if the specified data operation is a data read operation, then the target B-tree ( That is, the B tree corresponding to the target data index range) is traversed and searched to obtain the storage location of the data to be read in the database, and the data to be read is read according to the storage location.

It can be seen from the above process of performing the specified data operation that when processing data, only the B-tree corresponding to the target data index range is controlled to be in a locked state, and it is not necessary to make all the B-trees corresponding to the data index range in a locked state. locked state. In this way, when multiple parallel target data index ranges are different, corresponding operations can be performed concurrently on multiple corresponding B-trees without conflict, which improves the efficiency of data processing.

The data processing method of the present invention has the following advantages: the B-tree corresponding to different data index ranges can be operated simultaneously, the data corresponding to different partitions can be processed concurrently, and the data processing efficiency is improved; the height of the B-tree is reduced, and the query is reduced. The number of times, thereby avoiding useless disk queries and reducing the number of IO queries.

Embodiment 2:

FIG. 6 is a flowchart of a data processing method according to an embodiment of the present invention. As shown in FIG. 6 , the method includes the following steps:

Step S602, obtaining the data index key value of the data to be read;

Step S604, determining a first target data index range in a plurality of data index ranges according to the data index key value, wherein each data index range includes the data index key value of each data in the database; each data index range corresponds to a B-tree .

Referring to FIG. 2 , the first behavior of FIG. 2 is a target linked list, and the target linked list includes a plurality of data index ranges described above. If the data index key value is 201, it can be determined that the first target data index range to which the data index key value of 201 belongs in the target linked list is the data index range of 200 to 299 in FIG. 2 .

Step S606: Determine the storage location of the data to be read in the database based on the first target data index range and the corresponding first target B-tree, and read the data to be read according to the storage location.

In an optional implementation manner, if each data index range corresponds to a B-tree pointer, then determining the storage location of the data to be read in the database based on the first target data index range and its corresponding first target B-tree includes the following steps: The process is as follows:

The first target B-tree is determined according to the B-tree pointer of the first target data index range; the storage position of the data to be read in the database is determined based on the first target B-tree.

Before determining the storage location of the data to be read in the database based on the first target B-tree, a lock flag may also be set for the first target B-tree, and the first target B-tree is controlled to be in a locked state according to the lock flag.

In the data processing method of the present invention, when the data index key values of the data to be read are in different data index ranges, the data reading process can be performed in parallel, thereby improving the data reading efficiency.

Embodiment three:

FIG. 7 is a flowchart of a data processing method according to an embodiment of the present invention. As shown in FIG. 7 , the method includes the following steps:

Step S702, obtaining the data index key value of the data to be updated;

Step S704: Determine a second target data index range to which the data to be updated belongs in a plurality of data index ranges according to the data index key value, wherein each data index range includes the data index key value of each data in the database; each data index A range corresponds to a B-tree.

Referring to FIG. 2 , the first behavior of FIG. 2 is a target linked list, and the target linked list includes a plurality of data index ranges described above. If the data index key value is 201, it can be determined that the second target data index range of the data index key value 201 in the target linked list is the data index range of 200-299 in FIG. 2 .

Step S706, constructing the second target B-tree node on the B-tree corresponding to the second target data index range; specifically, constructing the second target B-tree node on the B-tree corresponding to the second target data index range. point, the index key value of the second target B-tree node can be set to the data index key value of the data to be updated, and the storage location corresponding to the second target B-tree node can be set to the data to be updated in the database. The storage location for performing data update operations on the data to be updated.

Step S708: Update the data to be updated according to the index key value of the second target B-tree node and the storage location corresponding to the second target B-tree node.

Wherein, each data index range corresponds to a B-tree pointer. Specifically, the B-tree corresponding to the second target data index range may be determined according to the B-tree pointer of the second target data index range.

In an optional embodiment of the present application, before constructing the second target B-tree node on the B-tree corresponding to the second target data index range, the method further includes: The B-tree is set with a lock flag; the B-tree corresponding to the index range of the second target data is controlled to be in a locked state according to the lock flag.

Specifically, in the present application, a lock flag may be set for the B-tree corresponding to the second target data index range, where the lock flag is used to indicate that the B-tree corresponding to the second target data index range is in a locked state. Then, in the locked state, construct a second target B-tree node on the B-tree corresponding to the second target data index range.

In the data processing method of the present invention, when the data index key values of the data to be updated are in different data index ranges, the data update process can be performed in parallel, thereby improving the data update efficiency.

Embodiment 4:

An embodiment of the present invention further provides a data processing apparatus, which is mainly used to execute the data processing method provided by the first embodiment of the present invention. The data processing apparatus provided by the embodiment of the present invention is described in detail below.

FIG. 8 is a schematic diagram of a data processing apparatus according to an embodiment of the present invention. As shown in FIG. 8 , the data processing apparatus mainly includes: a first obtaining unit 81, a dividing unit 82 and an operation executing unit 83, wherein:

The first obtaining unit 81 is configured to obtain a preset index range, wherein the preset index range is a preset range of index key values for indexing data in the database;

a dividing unit 82, configured to divide the preset index range into a plurality of data index ranges, and construct a corresponding B-tree for each of the data index ranges;

The operation execution unit 83 is configured to perform a specified data operation on the data based on each of the data index ranges and the B-tree corresponding to each of the data index ranges.

In the embodiment of the present invention, first, a preset index range is obtained, where the preset index range is a preset range of index key values for indexing data in the database; the preset index range is divided into multiple The data index range is constructed, and a corresponding B-tree is constructed for each data index range; the specified data operation is performed on the data based on each data index range and the B-tree corresponding to each data index range. It can be seen from the above description that after the preset index range is divided into multiple data index ranges, when the specified data operation is performed on the data in the database, it can be executed concurrently by partition, which improves the efficiency of data processing and saves the time of data processing. The technical problems of low efficiency and serious time-consuming of the existing data processing methods are alleviated.

Optionally, the operation execution unit 83 is further configured to: when the specified data operation is a data update operation, determine the data index key value of the data to be updated; The data index range to which the data index key value belongs; construct the first target B-tree node on the B-tree corresponding to the data index range to which the data index key value of the data to be updated belongs; according to the first target B-tree node The index key value of the point and the storage location corresponding to the target B-tree node are used to update the data to be updated.

Optionally, the operation execution unit 83 is further configured to: when the specified data operation is a data read operation, determine the data index key value of the data to be read; determine the to-be-read data in the multiple data index ranges Get the data index range to which the data index key value of the data belongs; determine the B tree corresponding to the data index range to which the data index key value of the data to be read belongs; The B-tree corresponding to the data index range is traversed and searched to obtain the storage location of the data to be read in the database, and the data to be read is read according to the storage location.

Optionally, the device is further configured to: before performing a specified data operation on the data based on each of the data index ranges and the B-tree corresponding to each of the data index ranges, determine a target corresponding to the specified data operation Data index range; control the B-tree corresponding to the target data index range to be in a locked state.

Optionally, the operation execution unit is further configured to: set a lock flag for the B-tree corresponding to the target data index range; and control the B-tree corresponding to the target data index range to be in a locked state according to the lock flag.

Embodiment 5:

An embodiment of the present invention further provides a data processing apparatus, which is mainly used to execute the data processing method provided by the second embodiment of the present invention. The following describes the data processing apparatus provided by the embodiment of the present invention.

FIG. 9 is a schematic diagram of a data processing apparatus according to an embodiment of the present invention. As shown in FIG. 9 , the data processing apparatus mainly includes: a second obtaining unit 91, a first determining unit 92 and a second determining unit 93, wherein:

The second obtaining unit 91 is used to obtain the data index key value of the data to be read;

The first determining unit 92 is configured to determine a first target data index range in a plurality of data index ranges according to the data index key value, wherein each data index range includes the data index key value of each data in the database; each The data index range corresponds to a B-tree;

The second determining unit 93 is configured to determine the storage location of the data to be read in the database based on the first target data index range and its corresponding first target B-tree, and read the storage location according to the storage location. Data to be read.

Optionally, each data index range corresponds to a B-tree pointer; the second determining unit 93 is further configured to: determine the first target B-tree according to the B-tree pointer of the first target data index range; A target B-tree determines the storage location of the data to be read in the database.

Optionally, the device is further configured to: before the storage location of the data to be read in the database is determined based on the first target B-tree, set a lock flag for the first target B-tree; The lock flag controls the first target B-tree to be in a locked state.

In the data processing device of the present invention, when the data index key values of the data to be read are in different data index ranges, the data reading process can be performed in parallel, thereby improving the data reading efficiency.

Embodiment 6:

An embodiment of the present invention further provides a data processing apparatus, which is mainly used to execute the data processing method provided by the third embodiment of the present invention. The following describes the data processing apparatus provided by the embodiment of the present invention.

FIG. 10 is a schematic diagram of a data processing apparatus according to an embodiment of the present invention. As shown in FIG. 10 , the data processing apparatus mainly includes: a third acquisition unit 101, a third determination unit 102, a construction unit 103 and an update unit 104, in:

The third obtaining unit 101 is used for obtaining the data index key value of the data to be updated;

A third determining unit 102, configured to determine a second target data index range to which the data to be updated belongs in a plurality of data index ranges according to the data index key value, wherein each data index range includes the data of each data in the database Data index key value; each data index range corresponds to a B-tree;

Construction unit 103, for constructing the second target B-tree node on the B-tree corresponding to the second target data index range;

The updating unit 104 is configured to update the data to be updated according to the index key value of the second target B-tree node and the storage location corresponding to the second target B-tree node.

Optionally, the device is further configured to: before constructing the second target B-tree node on the B-tree corresponding to the second target data index range, create a B-tree corresponding to the second target data index range Setting a lock flag; controlling the B-tree corresponding to the second target data index range to be in a locked state according to the lock flag.

In the data processing device of the present invention, when the data index key values of the data to be updated are in different data index ranges, the data update process can be performed in parallel, thereby improving the data update efficiency.

Embodiment 7:

11, an embodiment of the present invention further provides an electronic device 100, including: a processor 110, a memory 111, a bus 112 and a communication interface 113, the processor 110, the communication interface 113 and the memory 111 are connected through the bus 112; the processor 110 For executing executable modules, such as computer programs, stored in the memory 111 .

The memory 111 may include a high-speed random access memory (RAM, Random Access Memory), and may also include a non-volatile memory (non-volatile memory), such as at least one disk memory. The communication connection between the network element of the system and at least one other network element is realized through at least one communication interface 113 (which may be wired or wireless), which may use the Internet, a wide area network, a local network, a metropolitan area network, and the like.

The bus 112 may be an ISA bus, a PCI bus, an EISA bus, or the like. The bus can be divided into an address bus, a data bus, a control bus, and the like. For ease of representation, only one bidirectional arrow is shown in FIG. 11, but it does not mean that there is only one bus or one type of bus.

The memory 111 is used to store a program, and the processor 110 executes the program after receiving the execution instruction. The method executed by the device defined by the stream process disclosed in any of the foregoing embodiments of the present invention can be applied to processing in the processor 110 , or implemented by the processor 110 .

The processor 110 may be an integrated circuit chip with signal processing capability. In the implementation process, each step of the above-mentioned method may be completed by an integrated logic circuit of hardware in the processor 110 or an instruction in the form of software. The above-mentioned processor 110 may be a general-purpose processor, including a central processing unit (Central Processing Unit, CPU for short), a network processor (Network Processor, NP for short), etc.; it may also be a digital signal processor (Digital Signal Processing, DSP for short) , Application Specific Integrated Circuit (ASIC for short), Field-Programmable Gate Array (FPGA for short) or other programmable logic devices, discrete gate or transistor logic devices, and discrete hardware components. Various methods, steps, and logical block diagrams disclosed in the embodiments of the present invention can be implemented or executed. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like. The steps of the method disclosed in conjunction with the embodiments of the present invention may be directly embodied as executed by a hardware decoding processor, or executed by a combination of hardware and software modules in the decoding processor. The software modules may be located in random access memory, flash memory, read-only memory, programmable read-only memory or electrically erasable programmable memory, registers and other storage media mature in the art. The storage medium is located in the memory 111, and the processor 110 reads the information in the memory 111, and completes the steps of the above method in combination with its hardware.

In addition, in the description of the embodiments of the present invention, unless otherwise expressly specified and limited, the terms "installed", "connected" and "connected" should be understood in a broad sense, for example, it may be a fixed connection or a detachable connection , or integrally connected; it can be a mechanical connection or an electrical connection; it can be a direct connection, or an indirect connection through an intermediate medium, or the internal communication between the two components. For those of ordinary skill in the art, the specific meanings of the above terms in the present invention can be understood in specific situations.

In the description of the present invention, it should be noted that the terms "center", "upper", "lower", "left", "right", "vertical", "horizontal", "inner", "outer", etc. The indicated orientation or positional relationship is based on the orientation or positional relationship shown in the accompanying drawings, which is only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the indicated device or element must have a specific orientation or a specific orientation. construction and operation, and therefore should not be construed as limiting the invention. Furthermore, the terms "first", "second", and "third" are used for descriptive purposes only and should not be construed to indicate or imply relative importance.

Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working process of the above-described systems, devices and units may refer to the corresponding processes in the foregoing method embodiments, which will not be repeated here.

In the several embodiments provided in this application, it should be understood that the disclosed system, apparatus and method may be implemented in other manners. The apparatus embodiments described above are only illustrative. For example, the division of the units is only a logical function division. In actual implementation, there may be other division methods. For example, multiple units or components may be combined or Can be integrated into another system, or some features can be ignored, or not implemented. On the other hand, the shown or discussed mutual coupling or direct coupling or communication connection may be through some communication interfaces, indirect coupling or communication connection of devices or units, which may be in electrical, mechanical or other forms.

The units described as separate components may or may not be physically separated, and components displayed as units may or may not be physical units, that is, may be located in one place, or may be distributed to multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution in this embodiment.

In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically alone, or two or more units may be integrated into one unit.

The functions, if implemented in the form of software functional units and sold or used as stand-alone products, may be stored in a processor-executable non-volatile computer-readable storage medium. Based on this understanding, the technical solution of the present invention can be embodied in the form of a software product in essence, or the part that contributes to the prior art or the part of the technical solution. The computer software product is stored in a storage medium, including Several instructions are used to cause a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, Read-Only Memory (ROM, Read-Only Memory), Random Access Memory (RAM, Random Access Memory), magnetic disk or optical disk and other media that can store program codes .

Finally, it should be noted that the above-mentioned embodiments are only specific implementations of the present invention, and are used to illustrate the technical solutions of the present invention, but not to limit them. The protection scope of the present invention is not limited thereto, although referring to the foregoing The embodiment has been described in detail the present invention, those of ordinary skill in the art should understand: any person skilled in the art who is familiar with the technical field within the technical scope disclosed by the present invention can still modify the technical solutions described in the foregoing embodiments. Or can easily think of changes, or equivalently replace some of the technical features; and these modifications, changes or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should be covered in the present invention. within the scope of protection. Therefore, the protection scope of the present invention should be based on the protection scope of the claims.

Claims (15)

1. A data processing method, comprising:

acquiring a preset index range, wherein the preset index range is a preset range interval of index key values for indexing data in a database;

dividing the preset index range into a plurality of data index ranges, and constructing a corresponding B-tree for each data index range;

and executing specified data operation on the data based on each data index range and the B tree corresponding to each data index range.

2. The method of claim 1, wherein performing the specified data operation on the data based on each of the data index ranges and the B-tree corresponding to each of the data index ranges comprises:

when the specified data operation is a data updating operation, determining a data index key value of data to be updated;

determining a data index range to which a data index key value of the data to be updated belongs in the plurality of data index ranges;

constructing a first target B-tree node on the B-tree corresponding to the data index range to which the data index key value of the data to be updated belongs;

and updating the data to be updated according to the index key value of the first target B-tree node and the storage position corresponding to the first target B-tree node.

3. The method of claim 1, wherein performing the specified data operation on the data based on each of the data index ranges and the B-tree corresponding to each of the data index ranges further comprises:

when the specified data operation is a data reading operation, determining a data index key value of data to be read;

determining a data index range to which a data index key value of the data to be read belongs in the plurality of data index ranges;

determining a B tree corresponding to a data index range to which a data index key value of the data to be read belongs;

and traversing and searching the B tree corresponding to the data index range to which the data index key value of the data to be read belongs to obtain the storage position of the data to be read in the database, and reading the data to be read according to the storage position.

4. The method of any of claims 1-3, wherein prior to performing the specified data operation on the data based on each of the data index ranges and the B-tree to which each of the data index ranges corresponds, the method further comprises:

determining a target data index range corresponding to the specified data operation;

and controlling the B tree corresponding to the target data index range to be in a locked state.

5. The method of claim 4, wherein controlling the B-tree corresponding to the target data index range to be in a locked state comprises:

setting a locking identifier for the B tree corresponding to the target data index range;

and controlling the B tree corresponding to the target data index range to be in a locking state according to the locking identifier.

6. A data processing method, comprising:

acquiring a data index key value of data to be read;

determining a first target data index range in a plurality of data index ranges according to the data index key values, wherein each data index range contains the data index key value of each data in the database; each data index range corresponds to a B tree;

and determining the storage position of the data to be read in the database based on the first target data index range and the corresponding first target B-tree, and reading the data to be read according to the storage position.

7. The method of claim 6, wherein each data index range corresponds to a B-tree pointer;

determining the storage location of the data to be read in the database based on the first target data index range and the corresponding first target B-tree thereof comprises:

determining the first target B-tree according to the B-tree pointers of the first target data index range;

and determining the storage position of the data to be read in the database based on the first target B-tree.

8. The method of claim 7, wherein prior to said determining a storage location of said data to be read in a database based on said first target B-tree, said method further comprises:

setting a locking identifier for the first target B-tree;

and controlling the first target B-tree to be in a locking state according to the locking identifier.

9. A data processing method, comprising:

acquiring a data index key value of data to be updated;

determining a second target data index range to which the data to be updated belongs in a plurality of data index ranges according to the data index key values, wherein each data index range contains the data index key value of each data in the database; each data index range corresponds to a B tree;

constructing a second target B-tree node on the B-tree corresponding to the second target data index range;

and updating the data to be updated according to the index key value of the second target B-tree node and the storage position corresponding to the second target B-tree node.

10. The method of claim 9, wherein prior to constructing the second target B-tree node on the B-tree corresponding to the second target data index range, the method further comprises:

setting a locking identifier for the B-tree corresponding to the second target data index range;

and controlling the B tree corresponding to the second target data index range to be in a locking state according to the locking identifier.

11. A data processing apparatus, comprising:

the device comprises a first obtaining unit, a second obtaining unit and a third obtaining unit, wherein the first obtaining unit is used for obtaining a preset index range, and the preset index range is a preset range interval of index key values for indexing data in a database;

the dividing unit is used for dividing the preset index range into a plurality of data index ranges and constructing a corresponding B-tree for each data index range;

and the operation execution unit is used for executing specified data operation on the data based on each data index range and the B tree corresponding to each data index range.

12. A data processing apparatus, comprising:

the second acquisition unit is used for acquiring a data index key value of data to be read;

a first determining unit, configured to determine a first target data index range in multiple data index ranges according to the data index key values, where each data index range includes data index key values of respective data in a database; each data index range corresponds to a B tree;

and the second determining unit is used for determining the storage position of the data to be read in the database based on the first target data index range and the corresponding first target B-tree thereof, and reading the data to be read according to the storage position.

13. A data processing apparatus, comprising:

the third acquisition unit is used for acquiring a data index key value of the data to be updated;

a third determining unit, configured to determine, according to the data index key values, a second target data index range to which the data to be updated belongs in multiple data index ranges, where each data index range includes the data index key value of each data in the database; each data index range corresponds to a B tree;

the construction unit is used for constructing a second target B-tree node on the B-tree corresponding to the second target data index range;

and the updating unit is used for updating the data to be updated according to the index key value of the second target B-tree node and the storage position corresponding to the second target B-tree node.

14. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor implements the data processing method of any of claims 1-10 when executing the computer program.

15. A computer-readable medium having non-volatile program code executable by a processor, characterized in that the program code causes the processor to perform the data processing method of any of the preceding claims 1-10.

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