CN106991137B - The method that time series data is indexed based on Hbase hash summary forest - Google Patents
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Abstract
The invention discloses a kind of methods being indexed based on Hbase hash summary forest to time series data, comprising the following steps: (1) establishes every time quantum tree according to time granularity；(2) hash code of every time quantum tree is sought, and the time quantum tree with hash code is formed into the hash summary forest based on Hbase；(3) time series data of acquisition is inserted into hash summary forest according to hash code；(4) time series data for reading storage is inquired according to time range.The present invention improves the inquiry velocity of time series data converging operation, while providing hash index by generating hash code for cell tree, solves Hbase distributed storage time series data and generate hot issue by combining summary forest tree index scheme.
Description
Technical field
The present invention relates to technical field of memory, and in particular to one kind based on Hbase hash summary forest to time series data into
The method of line index.
Background technique
Time series data is the continuous data indexed with time series, and with popularizing for computer application, time series data is each
A field is also widely used.Such as: as the combination of financial field and internet is more and more closer, financial field is big
It is increasing to the converging operation performance requirement of time series data that the quantization of amount withdraws operation.Such as: in futures when a season
Between market price, disk mouth price or the exchange hand of certain commodity contract etc. in range counted, summed or calculated maximum value
Equal converging operations.How quick and precisely such application scenarios are frequent in finance quantization, and since data volume is huge,
The converging operation result that ground calculates the Financial Time Series in t1~t2 time becomes particularly significant.
In to Au metal forward business data within the scope of certain time for the sum operation of market price:
Select SUM(Last Price)From‘Au’WHERE time>t1AND time<t2
In such an application scenario, it is necessary to which support quickly obtains converging operation result in the time series data of magnanimity.
Traditional Relational DataBase mainly achievees the purpose that accelerate aggregate query by the way of Materialized View or summary table.
Materialized View is pre-processed to the querying command for being related to table connection, and result is stored in view table, and when inquiry is direct
Take out the result pre-processed.Summary table is then to calculate while data are written and save corresponding summary info, to send out
When raw inquiry, directly inquires and return the result from summary table.Such method improves search efficiency, but a disadvantage is that increasing
The expansion rate of database.In NoSQL database, some databases are handled by the way of MapReduce and polymerization pipeline
These converging operations are all the representatives calculated in real time, although not increasing the expansion rate of database, are generated in query process
A large amount of disk and computing cost, inefficient time-consuming are unable to satisfy the demand of extemporaneous inquiry.Some NoSQL databases are by tree-shaped
Index structure fusion, improves search efficiency, reduces magnetic disc access times.
Summary of the invention
In view of above-mentioned, the invention proposes a kind of sides being indexed based on Hbase hash summary forest to time series data
Method accelerates the query time of time series data by establishing tree index, and avoids time series data by hash code and be distributed
The uneven problem of the space distribution that sequential storage generates in formula database.
A method of time series data is indexed based on Hbase hash summary forest, comprising the following steps:
(1) every time quantum tree is established according to time granularity；
(2) hash code of every time quantum tree is sought, and the time quantum tree composition with hash code is based on Hbase
Hash summary forest；
(3) time series data of acquisition is inserted into hash summary forest according to hash code；
(4) time series data for reading storage is inquired according to time range.
In step (1), the process of settling time cell tree are as follows: firstly, predefining the time granularity of time quantum tree；So
Start to carry out recurrence with root node afterwards, establish a new node every time, next, recurrence establishes the left and right child section of this node
Point stops recurrence when the node of creation exceeds the range precalculated, completes the establishment process of whole tree.
In step (1), every time quantum tree is a Kd-Trees, and includes a set time granularity.Pass through control
The tree height of each tree processed controls time granularity.Line segment tree node stores the summary info of the range of nodes, specifically includes that
LBound, RBound, LNode, RNode and Data；Wherein, it includes time model that LBound, RBound, which respectively indicate the node,
The start time point enclosed and termination time point；LNode, RNode respectively indicate the left child of the node and when right child nodes include
Between the midpoint put；Data indicates the summary data value of node storage, each node for the time quantum tree established at this time
Data is empty.
The root node of each tree indicates the polymerization result of time series data in the t time span of this tree carrying, second layer section
Point carries the polymerization result of time series data in t/2 time span, analogizes every node layer and carries upper node layer half the time length
Index data.It is dissipated to realize that the time quantum tree comprising set time granularity can be convenient with same by the way that control tree is high
Column code polymerize the node of each tree, realizes the load balancing of hot spot.
The leaf node of the polymerization result of one time quantum tree representation, one unit time granularity range, each tree indicates
The polymerization summary info of most fine granularity range.Granularity can adjust according to actual needs.
Time quantum tree cover time (TreeBound) calculation formula:
TreeBound=(2^ (TreeMaxLevel-1)) * Leaf Bound
Wherein, TreeMaxLevel is that maximal tree is high, and LeafBound is the time range that leaf node indicates.Such as: one
Time quantum tree time range is set to one day, and tree height is set to 9 layers, and leaf node indicates 6 minutes summary infos.So one
The aggregated data that condominium is had jurisdiction over 1536 minutes is set, realizes that a time quantum tree covers one day time range.
In step (2), the mode for seeking the hash code of every time quantum tree has very much, passes through preferably, choosing
The processing of md5 transcoding is carried out to the information of each tree and generates the correspondence hash code (Hash) of this time cell tree, and is written into
In tree hash table；Calculate the concrete mode of hash code are as follows:
Hash=md5 (tree Info+tree low bound)
Tree info is Data Identification brief information, such as: this data represents Au metallic element futures data, then marks
It is the start time point of time quantum tree for Au, tree low bound.
More time quantum tree composition hash summary forests, the entire time range for hashing the data that summary forest is indexed
To form the sum of time range represented by his time quantum tree.
Hash summary forest based on Hbase is made of two Hbase tables of tree-hash and tree-node, wherein
Tree-hash table is used to search the hash code of time quantum tree, and tree-node table stores the burl of all time quantum trees
Point, and tree-hash table is individually to store with tree-node table, i.e., by the hash code of every time quantum tree and every time
The time series data that cell tree is loaded with separates individually storage, and the when ordinal number that the time quantum tree for possessing same Hash code is loaded with
According to centrally stored.
In step (3), the time series data can be Financial Time Series, forward business data etc. it is any when ordinal number
According to.
In step (3), time series data is inserted into the detailed process of hash summary forest are as follows:
(3-1) by belonging time of time series data found in tree-hash table where this timing data when
Between cell tree tree hash value；
(3-2) finds tree-node table corresponding to the tree hash value, and time series data recurrence is inserted into this tree-
In node table, detailed process are as follows:
Firstly, recurrence is started according to the root node that tree hash value finds locating time quantum tree, ordinal number when then carrying out
According to time point and current queries node time compare, when the time point of time series data be less than the node time when, to this
Left child's recurrence of node is inserted into time series data, when being greater than the node time at the time point of time series data, then the right side of the node
Child's recurrence is inserted into time series data；Until being inserted into the leaf node of time quantum tree.
In step (4), in data query, hash summary forest is due to the data in cell tree at the same time
Rowkey possesses same hash code, so hbase range query can rapidly find out whole time quantum tree and be stored in memory
In, this greatlys save the disk I/O operation after carrying out recurrence to tree.
Carry out the process of data query are as follows:
(a) judge whether query time range (t1, t2) belongs to cell tree range at the same time, if so, executing step
(b), if it is not, executing step (c)；
(b) inquiry operation is Query (t1, t2)；
(c) inquiry operation is Query (t1, EndUnitTime (t1)), Query (midUnitTime) and Query
(StartUnitTime(t2),t2)；
Wherein, StartUnitTime (t1) is the start time point of time quantum locating for time point t1；
EndUnitTime (t2) is the end time point of time quantum locating for time point t2；
MidUnitTime is the time model of several cell trees between the time range of time quantum tree locating for t1 and t2
It encloses；
Query (t1, t2) is expressed as the execution inquiry behaviour of the query time range (t1, t2) in same time quantum tree
Make；
Query (t1, EndUnitTime (t1)) indicates to execute inquiry behaviour in first cell tree in query context
Make；
Query (midUnitTime) indicates to execute in second to the second from the bottom cell tree in query context
Inquiry operation；
Query (StartUnitTime (t2), t2) indicates to execute inquiry in last cell tree in query context
Operation.
The detailed process of Query (t1, t2) are as follows:
(a) go out the hash code of its affiliated time quantum tree by searching for the time reckoning of item and navigate to this time unit
Tree, and using the root node of this time cell tree as current root node；
(b) recursive query since current root node, query task start from (t1, t2)；
(c) when recursive query is to present node, parse the time range that the node includes start time point LBound,
Middle time point midTime and termination time point RBound；
(d) judge whether t1 and t2 meet t1=LBound and t2=RBound, if so, recording the node result and exiting
Recurrence, if it is not, executing step (e)；
(e) judge whether t1 and t2 meet t1≤midTime and t2≤midTime, if so, the left child of the node is saved
Point is used as current root node, jumps and executes step (b)~step (d), if it is not, executing step (f)；
(f) judge whether t1 and t2 meet t1 >=midTime and t2 >=midTime, if so, the right child of the node is saved
Point is used as current root node, executes step (b)~step (d)；If it is not, executing step (g)；
(g) judge whether time t2 meets LBound < t2 < midTime, if so, using left child as present node, it will
MidTime executes step (b)~step (d) as t2；Using right child nodes as present node, using midTime as t1,
Execute step (b)~step (d).
The present invention improves the inquiry velocity of time series data converging operation, together by combining summary forest tree index scheme
When by generating hash code be that cell tree provides hash index, solve Hbase distributed storage time series data generation hot issue.
Detailed description of the invention
Fig. 1 is that the present invention is based on the flow charts that Hbase hashes the method that summary forest is indexed time series data；
Fig. 2 is the structural schematic diagram for the time quantum tree established；
Fig. 3 is that data throughout comparison diagram is written in the embodiment of the present invention 1；
Fig. 4 is large span time range aggregate query time-consuming comparison diagram in the embodiment of the present invention 2；
Fig. 5 is hash summary depth woods method and the time-consuming comparison of non-hash summary depth woods method inquiry in the embodiment of the present invention 3
Figure.
Specific embodiment
In order to more specifically describe the present invention, with reference to the accompanying drawing and specific embodiment is to technical solution of the present invention
It is described in detail.
As shown in Figure 1, the specific steps for the method being indexed based on Hbase hash summary forest to time series data are as follows:
Step 1, every time quantum tree is established: firstly, predefining the time range of time quantum tree；Then with root section
Point starts to carry out recurrence, establishes a new node every time, next, recurrence establishes the left and right child nodes of this node, works as wound
The node built stops recurrence when exceeding the range precalculated, complete the establishment process of whole tree, and each node is written
In tree node table, the time quantum tree of foundation is as shown in Figure 2；
In this step, every time quantum tree of foundation is a Kd-Trees, and includes a set time granularity.Pass through
The tree height of each tree is controlled to control time granularity.Line segment tree node stores the summary info of the range of nodes, specifically includes that
LBound, RBound, LNode, RNode and Data；Wherein, it includes time model that LBound, RBound, which respectively indicate the node,
The start time point enclosed and termination time point；LNode, RNode respectively indicate the left child of the node and when right child nodes include
Between the midpoint put；Data indicates the summary data value of node storage, each node for the time quantum tree established at this time
Data is empty.
The root node of each tree indicates the polymerization result of time series data in the t time span of this tree carrying, second layer section
Point carries the polymerization result of time series data in t/2 time span, analogizes every node layer and carries upper node layer half the time length
Index data.It is dissipated to realize that the time quantum tree comprising set time granularity can be convenient with same by the way that control tree is high
Column code polymerize the node of each tree, realizes the load balancing of hot spot.
The leaf node of the polymerization result of one time quantum tree representation, one unit time granularity range, each tree indicates
The polymerization summary info of most fine granularity range.Granularity can adjust according to actual needs.
Time quantum tree cover time (TreeBound) calculation formula:
TreeBound=(2^ (TreeMaxLevel-1)) * Leaf Bound
Wherein, TreeMaxLevel is that tree maximal tree is high, and LeafBound is the time range that leaf node indicates.
Step 2, the hash code of every time quantum tree is sought, and the time quantum tree composition with hash code is based on
The hash summary forest of Hbase；
In this step, the concrete mode of hash code is calculated are as follows:
Hash=md5 (tree Info+tree low bound)
Tree info is Data Identification brief information, such as this data represents Au metallic element futures data, then is labeled as
Au；Tree low bound is the start time point of time quantum tree, will be acquired in hash value write-in tree hash table；
Step 3, the time series data of acquisition is inserted into hash summary forest, insertion process are as follows:
Step 3-1, where finding this timing data in tree-hash table by the belonging time of time series data
The tree hash value of time quantum tree；
Step 3-2 finds tree-node table corresponding to the tree hash value, time series data recurrence is inserted into this
In tree-node table, detailed process are as follows:
Firstly, recurrence is started according to the root node that tree hash value finds locating time quantum tree, ordinal number when then carrying out
According to time point and current queries node time compare, when the time point of time series data be less than the node time when, to this
Left child's recurrence of node is inserted into time series data, when being greater than the node time at the time point of time series data, then the right side of the node
Child's recurrence is inserted into time series data；Until being inserted into the leaf node of time quantum tree.
Step 4, the time series data for reading storage, the process of inquiry are inquired according to time range are as follows:
Step 4-1, judges whether query time range (t1, t2) belongs to cell tree range at the same time, if so, executing
Step 4-2, if it is not, executing step 4-3；
Step 4-2, inquiry operation are Query (t1, t2)；
Step 4-3, inquiry operation be Query (t1, EndUnitTime (t1)), Query (midUnitTime) and
Query(StartUnitTime(t2),t2)。
Embodiment 1
Using the method for the present invention, Opentsdb open source time series database method and original Hbase method to it is identical when
Ordinal number records the write-in handling capacity of every kind of method according to progress data write-in, as shown in figure 3, from the available present invention side Fig. 3
Method is due to having index (hash code) and achievement process, so writing speed is directly stored in original number than original Hbase method
According to slow but faster than opentsdb open source time series database method writing speed.
When the method for the present invention progress data write-in is shown in table 1, the division situation of the region in Hbase.
Hash summary forest is split into after the region division of triggering Hbase by hashed value multiple as can be seen from Table 1
region.Same time quantum tree for possessing same Hash value can divide in the same region.Hashed value generates at random, newly
It hashes in different region to the tree uniform load built, avoids hot issue.
Embodiment 2
Data are carried out using the method for the present invention, Opentsdb open source time series database method and original Hbase method
The aggregate query of large span time range operates, and Fig. 4 is three kinds of methods in Long time scale aggregate query time-consuming comparison diagram, from figure
4 is available in the aggregate query operation of large span time range, and the method for the present invention performance is preferable.It is simultaneously it can be seen that original
Hbase method is when inquiring the polymerization result of 200000 datas, and time-consuming tens of seconds are unable to satisfy quickly extemporaneous query demand.
Opentsdb increases income time series database method since caching and Indexing Mechanism speed greatly promote.The method of the present invention ratio
Opentsdb inquiry velocity faster, and as query context increases, it is slower compared with other schemes to inquire time-consuming amplification.Illustrate to utilize
The rowkey of hbase is designed, and whole tree is found in use scope inquiry to be put into memory and have optimization function to query performance, is saved
Disk expense.
Embodiment 3
Aggregate query operation is carried out to data using the method for the present invention and non-ashing technique, Fig. 5 is hash summary forest side
Method and non-ashing technique inquire time-consuming comparison diagram.Lack Hash fields from the available non-Hash scheme rowkey of Fig. 5, and
Random challenge is carried out to hbase when query search Kd-Trees each recurrence.Same Kd-Trees may be dispersed in different
In region, the inquiry time-consuming of the method for the present invention is less than non-ashing technique, and as the increase advantage of query context is more obvious.
Technical solution of the present invention and beneficial effect is described in detail in above-described specific embodiment, Ying Li
Solution is not intended to restrict the invention the foregoing is merely presently most preferred embodiment of the invention, all in principle model of the invention
Interior done any modification, supplementary, and equivalent replacement etc. are enclosed, should all be included in the protection scope of the present invention.
Claims (4)
1. a kind of method being indexed based on Hbase hash summary forest to time series data, comprising the following steps:
(1) time range that high, leaf node includes according to tree establishes every time quantum tree comprising set time granularity；
(2) hash code of every time quantum tree is sought, and the time quantum tree with hash code is formed into dissipating based on Hbase
Column summary forest；
(3) time series data of acquisition is inserted into hash summary forest according to hash code；
(4) time series data for reading storage is inquired according to time range；
Every time quantum tree is a Kd-Trees, and line segment tree node stores the summary info of the range of nodes, comprising:
LBound, RBound, LNode, LNode and Data；Wherein, it includes time model that LBound, RBound, which respectively indicate the node,
The start time point enclosed and termination time point；LNode, LNode respectively indicate the left child of the node and when right child nodes include
Between range midpoint；Data indicates the summary data value of node storage, each node for the time quantum tree established at this time
Data is empty；
The hash code Hash's of every time quantum tree seeks formula are as follows:
Hash=md5 (tree Info+tree low bound)
Tree info is Data Identification brief information；Tree low bound is the start time point of time quantum tree；Md5 is
A kind of transcoding mode；
The hash summary forest based on Hbase is made of two Hbase tables of tree-hash and tree-node, wherein
For tree-hash table for storing the corresponding hash code of all time quantum trees, each tree-node table has corresponding time quantum
All leaf nodes of tree, and tree-hash table is individually to store, and will possess same Hash code with tree-node table
The time series data that time quantum tree is loaded with is centrally stored.
2. the method being indexed according to claim 1 based on Hbase hash summary forest to time series data, feature are existed
In: time series data is inserted into the detailed process of hash summary forest are as follows:
Time where (3-1) finds this timing data by the belonging time of time series data in tree-hash table is single
The hash code of member tree；
(3-2) finds tree-node table corresponding to the hash code, and time series data recurrence is inserted into this tree-node table,
Detailed process are as follows:
Start recurrence according to the root node that hash code finds locating time quantum tree, then the time point of progress time series data with work as
The time range of preceding query node compares, and is less than the middle time point of the time range of the node when the time point of time series data
When, into the Data of the left child nodes of the node, recurrence is inserted into time series data, is greater than the node when the time point of time series data
Time range middle time point when, then in the Data of the right child nodes of the node recurrence be inserted into time series data；Until inserting
Enter until the leaf node of time cell tree.
3. the method being indexed according to claim 1 based on Hbase hash summary forest to time series data, feature are existed
In: carry out the process of data query are as follows:
(a) judge whether query time range (t1, t2) belongs to cell tree range at the same time, if so, step (b) is executed,
If it is not, executing step (c)；
(b) inquiry operation Query (t1, t2) is executed
(c) inquiry operation Query (t1, EndUnitTime (t1)), Query (midUnitTime) and Query are executed
(StartUnitTime(t2),t2)；
Wherein, StartUnitTime (t2) is the initial time for the time range that time quantum tree locating for time point t2 includes
Point；
EndUnitTime (t1) is the end time point for the time range that time quantum tree locating for time point t1 includes；
MidUnitTime is the time range between the time range of time quantum tree locating for t1 and t2；
Query (t1, t2) is expressed as executing inquiry operation in the same time quantum tree belonging to query context t1~t2；
Query (t1, EndUnitTime (t1)) indicates first unit in query context t1~EndUnitTime (t1)
Inquiry operation is executed in tree；
Query (midUnitTime) indicates second to the second from the bottom cell tree in query context midUnitTime
Middle execution inquiry operation；
Query (StartUnitTime (t2), t2) indicates last in query context StartUnitTime (t2)~t2
Inquiry operation is executed in cell tree.
4. the method being indexed according to claim 3 based on Hbase hash summary forest to time series data, feature are existed
In: the detailed process of Query (t1, t2) are as follows:
(a) go out the hash code of its affiliated time quantum tree by searching for the time reckoning of item and navigate to this time cell tree, and
Using the root node of this time cell tree as current root node；
(b) recursive query since current root node, query task start from (t1, t2)；
(c) when recursive query is to present node, start time point LBound, the centre of the time range that the node includes are parsed
Time point midTime and termination time point RBound；
(d) judge whether t1 and t2 meet t1=LBound and t2=RBound, passed if so, recording the node result and exiting
Return, if it is not, executing step (e)；
(e) judge whether t1 and t2 meet t1≤midTime and t2≤midTime, if so, the left child nodes of the node are made
It for current root node, jumps and executes step (b)~step (d), if it is not, executing step (f)；
(f) judge whether t1 and t2 meet t1 >=midTime and t2 >=midTime, if so, the right child nodes of the node are made
For current root node, step (b)~step (d) is executed；If it is not, executing step (g)；
(g) judge whether time t2 meets LBound < t2 < midTime, if so, using left child as present node, it will
MidTime executes step (b)~step (d) as t2；Using right child nodes as present node, using midTime as t1,
Execute step (b)~step (d).
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| 支持时序数据聚合函数的索引;黄向东等;《清华大学（自然科学）》;20160331;第229-236,245页 * |
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| CN106991137A (en) | 2017-07-28 |
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