CN103150327A - Skyline inquiry method based on multi-tenant data base in SaaS environment - Google Patents
Skyline inquiry method based on multi-tenant data base in SaaS environment Download PDFInfo
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Abstract
The invention discloses a skyline inquiry method based on a multi-tenant data base in SaaS environment. The method comprises the following steps that (1) a data base based on multi-tenant facing the SaaS environment is built; (2) a B+ tree index array is built on a pivot table; (3) each candidate link is built according to the B+ tree index array, and candidate nodes are sequentially traversed in turns on each candidate link; and (4) when a Skyline object is traversed, the node is output, in addition, the node is deleted from each candidate link, and the operation returns to the third step if the candidate link is not completed. The skyline inquiry method provided by the invention has great improvement in aspects of reaction time, compatibility and efficiency.
Description
Technical field
The present invention relates to the data processing field of computer technology, relate to specifically under optimized treatment method, especially the SaaS environment of a kind of Skyline inquiry the Skyline querying method based on multi-tenant database.
Background technology
Skyline inquiry is the study hotspot of database field Query Processing Technique on hyperspace in recent years, and Skyline is widely used in preference inquiry, the decision support of many standards and data mining and the aspect such as visual.Skyline inquiry is one of significant problem of needing to be resolved hurrily of high-volume database management domain.The Skyline inquiry refers to select a subset from a given D dimension data object set S, and any one data object in this subset all can not be arranged by any one other data object in S.So-called dominance relation refers in the data acquisition S of D dimension space, if data object p at least on a certain amplitude due to another data object q, and data object p on other dimensions all unlike data object q poor (p be better than or equal q), data object p can arrange data object q so.
The result set of Skyline inquiry is that data centralization is not all by the object that other objects are arranged.In recent years, it makes it become the study hotspot of data management and Data Mining in the applications well prospect in the fields such as online service, decision support and Real-Time Monitoring.
Along with the fast development of internet correlation technique, such as: cloud computing, grid computing etc.software is namely served the software operation service mode that (SaaS) becomes a kind of main flow gradually, manufacturer with the application software unified plan on the server of oneself, the client can be according to own actual demand, order required application software service by the internet to manufacturer, the user need not buy software again, rent software based on Web and use instead to provider, come the management enterprise business activities, and need not software is safeguarded, the service provider understands full powers and administers and maintains software, software vendor is when providing internet, applications to the client, off-line operation and the local datastore of software also are provided, the software and services that allows the user can use it to order whenever and wherever possible.Multi-tenant database system (multi-tenant database system) is one of main way of realization of SaaS, is also the focus that current industrial circle and academia pay close attention to.The tenant is in its data pattern of data center configuration (schema) of SaaS service provider, and it is uploaded.The service provider is responsible for the operation of the system that is stored in of data, and supports the tenant to provide service by Internet to client in the mode of Web service.
Customer relation management, Enterprise Resources Plan, supplier relationship management, business intelligence based on multi-tenant database are in the ascendant in the application of industry member, and the Skyline object excavates significant to city navigation, business intelligence and data mining visualization etc.Vital role in view of Skyline, the researchist studies comprising that Skyline under the environment such as traditional static database data set, data stream, C/S model and P2P pattern calculates, but still lacks research for the Skyline Mining Problems academia based on multi-tenant database under the SaaS environment.
Existing Skyline querying method mainly adopts the pivot table to store, under the pivot table schema repeatedly, a large amount of and unnecessary inevitable from attended operation, so existing method not only wastes time and energy, and can produce a large amount of intermediate result, effect is bad.thus, can't effectively carry out efficient characteristics of inquiring about for existing Skyline querying method under multi-tenant environment, design a kind of efficient querying method: namely on the basis of pivot table schema, set up B+ tree index, be each attribute that is subordinated to different privately owned tables on the pivot table and set up the B+ index, and in conjunction with the characteristics of SaaS environment, use a MDOS based on index structure (Multi-Tenant Database Oriented Skyline) algorithm, and adopt on this basis efficient Pruning strategy, the scale of minimizing candidate chains progressively, and repeatedly the domination test of carrying out fraternal object and superseded ordinary object, thereby design the Skyline querying method of high-efficiency and low-cost, and can guarantee the efficient of Skyline query processing and the accuracy of Query Result.
Summary of the invention
Technical matters to be solved by this invention be to provide a kind of under the SaaS environment Skyline querying method based on multi-tenant database, this method can have very large advantage aspect reaction time, compatibility, efficient.
In order to solve above technical matters, the invention provides under a kind of SaaS environment based on the Skyline querying method of multi-tenant database, comprise the steps:
(1) set up a database based on many tenants under the SaaS environment;
(2) set up a B+ tree index array on the pivot table;
(3) set up each candidate chains according to described B+ tree index array, travel through successively in turn candidate's node on each candidate chains;
(4) export this node when traversing the Skyline object, and delete this node on each candidate chains, if candidate chains does not finish to return step (3).
Preferably, under SaaS environment of the present invention, based on the Skyline querying method of multi-tenant database, in step (1), database is with the storage of pivot sheet form, and described many tenants are corresponding privately owned table on described pivot table respectively.
Further, under SaaS environment of the present invention, based on the Skyline querying method of multi-tenant database, step (2) is set up B+ tree index array with each attribute that is subordinated to different privately owned tables on the pivot table.
Preferably, under SaaS environment of the present invention based on the Skyline querying method of multi-tenant database, the described traversal of step (3) candidate node is the method that adopts fraternal object domination test, candidate target is verified, to determine whether also to exist the object of this candidate target of domination, if no, judge that this candidate target is the Skyline object.
Further, based on the Skyline querying method of multi-tenant database, delete the node of Skyline object in step (4) and eliminate simultaneously the node of ordinary object on each candidate chains under SaaS environment of the present invention.
Preferably, under SaaS environment of the present invention, based on the Skyline querying method of multi-tenant database, step (3) comprises two candidate chains, travels through successively in turn each node on the first candidate chains and the second candidate chains.
Further, under SaaS environment of the present invention based on the Skyline querying method of multi-tenant database, when traverse the Skyline object on a candidate chains, export this node, and delete simultaneously this node on each candidate chains, judge whether each candidate chains is empty, if not, the pointer with each candidate chains points to first-in-chain(FIC), returns to step (3).
Preferably, under SaaS environment of the present invention, based on the Skyline querying method of multi-tenant database, the node of described candidate chains itself is according to the size order link from childhood and greatly of key word.
Compared with prior art, the present invention has following technological merit:
1, the first step of the present invention has adopted sharing table shared data bank example architecture (shared tables and shared database instances).Only there is a kind of public shared data bank pattern in this kind scheme, only has accordingly a cover sharing table, the tenant with they data or first ancestral is unified exists in the middle of the sharing table of public database, owing to adopting this pattern, the tenant ID of first ancestral in database under together distinguishes mutually, make this database schema that best extendability be arranged, can excavate and utilize various the segmenting market.
2, the present invention organizes candidate target, is not simply at whole chained list Link
iEnterprising line scanning, but at candidate target collection CLink
iEnterprising line operate.And the scale of candidate target collection can reduce gradually along with the execution of algorithm, and algorithm will be carried out on a much smaller data set like this.This strategy will play very strong beta pruning effect, removal early unnecessary Skyline object and ordinary object, avoided a large amount of repetitive operations.
3, algorithm proposed by the invention adopts the index structure based on the B+ tree, is consistent with traditional database support selection, the method that the basic operation such as connects and the index structure of B+ tree is peculiar.This makes this querying method can be good at being integrated in the traditional database engine, thereby has good compatibility.
4, algorithm proposed by the invention (MDOS) turns execution at the enterprising road wheel of each dimension, and the more excellent result of value can fair output on different dimensional, so just can avoid the skewed popularity to certain dimension.
5, disposal route of the present invention has very large improvement at aspects such as reaction time, data scale, algorithm incremental nature, the impacts of mobile phone dimension after testing.
Description of drawings
Below in conjunction with the drawings and specific embodiments, the present invention is described in further detail.
Fig. 1 is the physical structure of the multi-tenant database system of first step structure of the present invention;
Fig. 2 is sharing table database instance Organization Chart of the present invention;
Fig. 3 is the general flow chart of an embodiment of the inventive method;
Fig. 4 a is the one embodiment of the invention data structure diagram based on the B+ tree used;
Fig. 4 b is the data structure diagram after one embodiment of the invention is exported first skyline object;
Fig. 4 c is the data structure diagram after second skyline object of one embodiment of the invention output;
Fig. 4 d is the data structure diagram after the 3rd skyline object of one embodiment of the invention output;
Fig. 5 is the fraternal object domination test process process flow diagram of the inventive method;
Fig. 6 is the superseded ordinary object process flow diagram flow chart of the inventive method;
Fig. 7 is reaction time experiment comparison diagram of the present invention;
Fig. 8 is incremental nature experiment comparison diagram of the present invention;
Fig. 9 is data scale extensibility experiment comparison diagram of the present invention;
Figure 10 is data dimension extensibility experiment comparison diagram of the present invention.
Embodiment
In order to make purpose of the present invention, technical scheme and advantage clearer, below in conjunction with the drawings and specific embodiments, the invention scheme is described in further detail.Should be appreciated that specific embodiment described herein only in order to explain the present invention, is not intended to limit the present invention.
Process flow diagram of the present invention as shown in the figure, the below provide a kind of under the SaaS environment Application Example based on the Skyline querying method of multi-tenant database.In this Application Example, describe in detail and how to set up the required data structure of querying method and described with legend the concrete grammar that method is carried out.
The first step, as shown in Figure 1, the method that builds multi-tenant database system is:
Be called tenant (tenant) with occupying left enterprise, the final user is called client (client).The tenant is in its data pattern of data center configuration (schema) of SaaS service provider, and its business datum is uploaded.The service provider is responsible for the operation of the system that is stored in of data, and supports the tenant to provide service by Internet to client in the mode of Web service.
As shown in Figure 2, building sharing table database instance framework method is:
Sharing table database instance (STSI) scheme realizes extensibility by many tenants shared data bank table, and its embodiment is mainly by pivot table (Pivot table) pattern.
As shown in following table one and table two, tenant's privately owned table is integrated into the pivot table.Table one is the sample data collection, and table two is tenant 7 pivot table, and this table is stored the data in former table respectively by type, and there is respectively pivot table Pivot in business datum
numAnd Pivot
strNum, Str two row in, attribute Tenant, Col and Row have played the effect of location.
(a) tenant 7 privately owned table (b) tenant's 31 privately owned table
Table one
(a) table Pivot
num(b) table Pivot
str
Table two
Second step is set up a B+ tree index array on the pivot table, set up B for each attribute that is subordinated to different privately owned tables on the pivot table
+The tree index as shown in Fig. 4 a, is that the present invention is subordinated to the data structure exemplary plot that each attribute of tenant's 7 privately owned tables is set up.Specifically, set up two B+ trees: T1, T2.Wherein B+ tree T1, T2 are to pivot table Pivot
numBe subordinated to the index of the data foundation of privately owned table.T1, T2 be the attribute x of corresponding Tenant field respectively, y.
The 3rd step, set up each candidate chains (node of described candidate chains itself is according to the size order link from childhood and greatly of key word) according to described B+ tree index array, set up two chained list Link
2And Link
1, Link wherein
2Chained list is that the leafy node by T2 consists of, Link
1Chained list is that the leafy node by T1 consists of.Set up two pointer L
2And L
1, they point to respectively Link
2And Link
1First-in-chain(FIC).Set up again candidate's chained list Clink
1And CLink
2, wherein, H2 points to CLink
2First-in-chain(FIC), this chain is connected with black arrow; H1 points to CLink
1First-in-chain(FIC), this chain is connected with grey arrow, travels through successively in turn candidate's node on each candidate chains.
In the 4th step, export this node when traversing the Skyline object, and delete this node on each candidate chains, if candidate chains did not finish to return the 3rd step.
As shown in Figure 6, traversal is eliminated ordinary object process prescription: MDOS (Multi-Tenant Database Oriented Skyline) method master routine at candidate chains CLink
iUpper traversal candidate node, according to from left to right, namely from small to large mode is tested the sign node one by one and is found the solution.In case find a satisfied Skyline object, be about to the sign node of its association from candidate chains Clink
iIn remove, but other site positions on candidate chains remain unchanged.
1) at first, distribute to Clink under original state
i7 the sign nodes, for simplicity, we only consider Clink
1And Clink
2Be the fraternal object domination test process of the inventive method as shown in Figure 5, at first computing machine can scan Clink
2, first node wherein is N2, it is related candidate target h2.Value on its attribute y is 0.3, due to Link
2In there is no other less than or equal to 0.3 element, be the Skyline object therefore export immediately h2.And simultaneously with N2 from CLink
2And CLink
1In remove.
2) then, call superseded ordinary object program and eliminate the object of being arranged by h2.Method can find according to the connecting relation of N2 the value 110 related with 0.3, and this closes tie-point zone bit N2, and next step is along CLink
1To the right scanning (scan backward successively node N5-＞N3-＞N4, determine ordinary object h5, h3, h4), then eliminate all ordinary objects.
The process that specifically realizes with software is exemplified below: Nv arranges four attributes, Nv.tag, Nv.id, Nv.clockin and Nv.cnt for the sign node.
● Nv.tag is the identity sign of object, and it is 0,1,2 that three kinds of values are arranged, and represents that respectively its associated object is ordinary object, candidate target and Skyline object, and its initial value is 1.
● Nv.id storage and v made the ID of the Skyline object of domination test, played the effect of reservation " finger mark ", and initial value is null.For example, for certain Skyline object u, find u.vall≤v.vall when in the upper scanning of first attribute (Linkl), will be assigned to Nv.id with regard to the ID of u, place a wheal by upper " finger mark ".After this scan Linki (i ∈ 1 ..., in the time of D}), will test " finger mark " can enter.
● Nv.clockin plays the effect of " checking card ", the number of times (" turning out for work " number of times) that record " finger mark " is proved to be successful.
Nv.cnt, initial value is 0, plays the effect of counter.When algorithm was carried out, when finding v.vali=u.vali, Nv.cnt added 0; When finding v.vali＞u.vall, Nv.cnt adds 1.Like this when for the equal been scanned of whole attributes, if Nv.id=u.id and Nv.clockin=D and Nv.cnt＞1, mean for
V.vali u.vali, and
V.vali＞u.vali.Be also u v, declaration v is ordinary object.
With N5.id, the equal assignment of N3.idN4.id is h2, N5.clockin, and N3.clockin, N4.clockin are 1, N5.cnt, N3.cnt, N4.cnt are 1.
3) then, computing machine can be at Link
2On navigate to element 0.3, the sign node of this elements correlation is similarly N2.Then along CLink
2To the right scanning, if find N3.id=h2, and N3.clockin=2, N2.cnt＞0 equals N3.tag to 0 so, and h3 is declared as ordinary object, and N3 is removed from candidate chains.
4) last because h4, the situation of h5 similarly, and the value of N1.id, N6.id, N7.id is not all h2, therefore do not do any processing.After completing above step, then N2 is removed from candidate chains.So just can export first Skyline object.Export the 1st situation after the Skyline object as shown in Fig. 4 b.Skyline, candidate and ordinary object are represented by Dark grey circle, light grey circle and open circles respectively.
Computing machine then scans CLink
1, if the related candidate target h1 of first node is Link
1In there is no other less than or equal to 0.8 element, because Link
1In data all connect from small to large, so output h1 be second Skyline object.Then (a same embodiment of method is along corresponding CLink to call superseded ordinary object
2The N1 node scan to the right, 0.8 back has not had object), and output Skyline object is as shown in Fig. 4 c.
Computing machine is then taken turns flyback retrace CLink again
2, the related candidate target of first node is h6.Equally, if do not have other elements to equal 0.5, in like manner can get, output h6 is the 3rd Skyline object.And then call and eliminate ordinary object (along CLink
1 Respective value 70 scan N 7 to the right, eliminate ordinary object h7).As shown in Fig. 4 d.
Computing machine is at two chained list CLink
1And CLink
2Between carry out to eliminate in turn ordinary object and fraternal object test domination process, the efficient like this Skyline object of progressively exporting.
At last, if find candidate's chained list CLink
1And CLink
2When being all empty, algorithm finishes, as shown in Fig. 4 d.
So-called B
+Tree is the tree with following three characteristics:
1. contain n key word in the node that has the n stalk to set.
2. comprised the information of whole key words in all leafy nodes, and pointed to the pointer that contains these keyword record, and leafy node itself is according to the size order link from childhood and greatly of key word.
3. all non-terminal nodes can be regarded index part as, only contain maximum (or minimum) key word in its subtree (root node) in node.Usually on the B+ tree, two head pointers are arranged, one is pointed to root node, a leafy node that points to the key word minimum.
Set up the method for the required data structure of querying method:
At first set up a B+ tree index array on the pivot table, each attribute that is subordinated to different privately owned tables on the pivot table is set up the B+ index array.For example embodiment shown in Figure 4, set up two B+ trees: T1, T2.Wherein B+ tree T1, T2 are to pivot table Pivot
numBe subordinated to the index of the data foundation of privately owned table.T1, T2 be the attribute x of corresponding Tenant field respectively, y.
Set up at last two chained list Link
2And Link
1, Link wherein
2Chained list is that the leafy node by T2 consists of, Link
1Chained list is that the leafy node by T1 consists of.Set up two pointer L
2And L
1, they all point to Link
2And Link
1First-in-chain(FIC).
In the present embodiment, to each sign node (N
V) increase several required attributes:
1) for sign node Nv increases several required attributes, establishing attribute is N
vptr
i, it is the pointer that points to the sign node.
2) will indicate that node is organized into D chained list (CLink
i), be referred to as candidate target sign node chain.
With node N
vAt candidate chains CLink
iIn the consistent and N of relative position
vAssociated element is at Link
iIn relative position consistent, and pointer H
iAll the time point to CLink
iFirst-in-chain(FIC).
Compared with prior art, the present invention has following technological merit:
1, the first step of the present invention has adopted sharing table shared data bank example architecture (shared tables and shared database instances).Only there is a kind of public shared data bank pattern in this kind scheme, only has accordingly a cover sharing table, the tenant with they data or first ancestral is unified exists in the middle of the sharing table of public database, owing to adopting this pattern, the tenant ID of first ancestral in database under together distinguishes mutually, make this database schema that best extendability be arranged, can excavate and utilize various the segmenting market.
2, the present invention organizes candidate target, is not simply at whole chained list Link
iEnterprising line scanning, but at candidate target collection CLink
iEnterprising line operate.And the scale of candidate target collection can reduce gradually along with the execution of algorithm, and algorithm will be carried out on a much smaller data set like this.This strategy will play very strong beta pruning effect, removal early unnecessary Skyline object and ordinary object, avoided a large amount of repetitive operations.
3, algorithm proposed by the invention adopts based on B
+The index structure of tree, and B
+The index structure of tree is peculiar to be consistent with traditional database support selection, the method that the basic operation such as connects.This makes this querying method can be good at being integrated in the traditional database engine, thereby has good compatibility.
4, algorithm proposed by the invention (MDOS) turns execution at the enterprising road wheel of each dimension, and the more excellent result of value can fair output on different dimensional, so just can avoid the skewed popularity to certain dimension.
5, disposal route of the present invention has very large improvement at aspects such as reaction time, data scale, algorithm incremental nature, the impacts of mobile phone dimension after testing.
Below give out at Windows XP, the contrast properties test of carrying out in the configuration of dominant frequency 2.0G Pentium 4 processor and 1G internal memory, contrast between MDOS disposal route in this paper and existing two kinds of disposal route BBS and Benchmark Performance Ratio.
1) reaction time: data scale is fixed as 500K bar tuple, and core buffer is fixed as 1M Bytes, and data dimension progressively is increased to 6 by 2, and experiment comparison diagram 7 has provided experimental result.
2) algorithm incremental nature: data scale is fixed as 500K bar tuple, and dimension is fixed as 4, and core buffer is fixed as 1M Bytes.The as a result spent time of examination algorithm after output 20%, 40%, 60%, 80% and 100%, Fig. 8 has provided experimental result.
3) impact of data scale: data dimension is fixed as 4, and core buffer is fixed as 1M Bytes, and data scale progressively is increased to 2M by 100K bar tuple, and Fig. 9 has provided the experimental result of algorithm under the different distributions data set.
4) impact of data dimension: data scale is fixed as 500K bar tuple, and core buffer is fixed as 1MBytes, data dimension progressively is increased to 6, Figure 10 by 2 has provided the experimental result of algorithm under the different distributions data set.
Claims (8)
- Under a SaaS environment based on the Skyline querying method of multi-tenant database, it is characterized in that, comprise the steps:(1) set up a database based on many tenants under the SaaS environment;(2) set up a B+ tree index array on the pivot table;(3) set up each candidate chains according to described B+ tree index array, travel through successively in turn candidate's node on each candidate chains;(4) export this node when traversing the Skyline object, and delete this node on each candidate chains, if candidate chains does not finish to return step (3).
- Under SaaS environment as claimed in claim 1 based on the Skyline querying method of multi-tenant database, it is characterized in that, database is with the storage of pivot sheet form in step (1), described many tenants are corresponding privately owned table on described pivot table respectively.
- Under SaaS environment as claimed in claim 2 based on the Skyline querying method of multi-tenant database, it is characterized in that, step (2) is set up B+ tree index array with each attribute that is subordinated to different privately owned tables on the pivot table.
- Under SaaS environment as claimed in claim 1 based on the Skyline querying method of multi-tenant database, it is characterized in that, the described traversal of step (3) candidate node is the method that adopts fraternal object domination test, candidate target is verified, to determine whether also to exist the object of this candidate target of domination, if no, judge that this candidate target is the Skyline object.
- Under SaaS environment as claimed in claim 4 based on the Skyline querying method of multi-tenant database, it is characterized in that, in step (4) on each candidate chains the node of deletion Skyline object eliminate simultaneously the node of ordinary object.
- Under SaaS environment as claimed in claim 1 based on the Skyline querying method of multi-tenant database, it is characterized in that, step (3) comprises two candidate chains, travels through successively in turn each node on the first candidate chains and the second candidate chains.
- Under SaaS environment as claimed in claim 6 based on the Skyline querying method of multi-tenant database, it is characterized in that, when traverse the Skyline object on a candidate chains, export this node, and delete simultaneously this node on each candidate chains, judge whether each candidate chains is empty, if not, the pointer with each candidate chains points to first-in-chain(FIC), returns to step (3).
- Under SaaS environment as claimed in claim 1 based on the Skyline querying method of multi-tenant database, it is characterized in that, the node of described candidate chains itself according to the size of key word from childhood and large order link.
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Application publication date: 20130612 |