CN103077125B - A kind of tower caching method of self-adaptation self-organization of efficiency utilization storage space - Google Patents
A kind of tower caching method of self-adaptation self-organization of efficiency utilization storage space Download PDFInfo
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Abstract
The present invention relates to a kind of tower caching method of self-adaptation self-organization of efficiency utilization storage space, its step comprises: setting up on caching server with data size is the multi-level buffer container of threshold values, caching container at different levels is connected into an array, forms tower buffer structure; Described caching server receives data from data center, and the size according to the data received selects suitable caching container to store automatically; Under the prerequisite keeping storage space volume total amount constant, according to the capability value of the hit rate dynamic adjustments caching container at different levels in caching container at different levels at the appointed time section, the capacity of the caching container making hit rate higher is larger.The present invention can automatically according to the buffer memory that the size Selection of data self is suitable, and the hit rate by automatically regulating the capacity of buffer memory to maximize buffer memory, there is adaptivity and self-organization.
Description
Technical field
The invention belongs to computer memory technical field, relate to a kind of caching method, be specifically related to the tower caching method of self-adaptation self-organization of a kind of reasonable distribution and efficiency utilization storage space.
Background technology
For popular heterogeneous system, buffer memory is a kind of technology generally adopted alleviating client and service end speed difference, as shown in Figure 1, its principle be exactly the data obtained from data center are issued client while, it is cached in the internal memory of service end (or on special buffer service), after receiving the request meeting feature, from buffer memory, directly takes out data return to client, avoid again retrieving, to respond the request of user at high speed.But single buffer memory can not utilize memory headroom well.For data cached list, suppose to be cached with 100M space, buffer memory 100 table datas, if table data is on average greater than 1M, obviously can cause the problem of insufficient space; If table data is on average less than 1M, then spatial cache does not make full use of.In this case, common way is that do not specify can data cached number, comes data cached by the size of data, and this is that the management of buffer memory brings difficulty.
In field of storage, multi-level buffer is also a kind of proven technique, it mainly utilizes the speed difference characteristic of different physical mediums and builds, and is stored in the fastest buffer memory, obtains the lifting of entire system response speed with this by the most used data or instruction.Another kind builds different buffer memorys based on different data, to facilitate management, as patent of invention " the three-dimensional space data self-adaptation multilevel cache system based on the data content " (patent No.: 200910063371.3).But factor data content size differs, size is difficult to estimate, existing multi-level buffer method can not make full use of storage space.
Summary of the invention
The object of the invention is to for the problems referred to above, a kind of tower caching method of self-adaptation self-organization of efficiency utilization storage space is proposed, can automatically according to the buffer memory that the size Selection of data self is suitable, and the hit rate by automatically regulating the capacity of buffer memory to maximize buffer memory, there is adaptivity and self-organization.
What the present invention adopted is multi-level buffer, multi-level buffer refers to by treating that deposit data is of a size of threshold values and carrys out classification, buffer memory at different levels can only preserve size data within the specific limits, level (number of buffer memory) the not restriction of buffer memory, and data cached content does not add differentiation; Make the limited more data of space energy buffer memory like this, reach the maximization of space utilization.
Specifically, the technical solution used in the present invention is:
The tower caching method of a kind of self-adaptation self-organization, its step comprises:
1) setting up on caching server with data size is the multi-level buffer container of threshold values, and caching container at different levels is connected into an array, forms tower buffer structure;
2) described caching server receives data from data center, and the size according to the data received selects suitable caching container to store automatically;
3) under the prerequisite keeping storage space volume total amount constant, according to the capability value of the hit rate dynamic adjustments caching container at different levels in caching container at different levels at the appointed time section, the capacity of the caching container making hit rate higher is larger.
Further, the present invention can set the access privileges of caching container at different levels according to the clicking rate of buffer memory, clicking rate more high priority is higher, like this when access cache data, just can according to priority for sequence travels through caching container at different levels, shorten the time of searching in the buffer from probability, make the maximizing efficiency of query caching.If the total volume of buffer memory is very large, when the efficiency of buffer memory becomes bottleneck, can consider to use this improvement project.
Buffer structure described above can be regarded the one-dimentional structure being of a size of threshold values by data as and externally provide unified data acquisition interface, and hiding data storage realizes details.Further, the present invention can build N and tie up buffer structure, wherein N >=2 on the multi-level buffer vessel base taking data size as threshold values, and the details of externally hiding data storage, unified access interface is provided.Such as can build the structure of two dimension on this basis, if the query time that will obtain data from data center is as another threshold values structure two-dimensional structure, the time that the longer data of query time retain in the buffer is also longer.Principle that in like manner can be same builds three-dimensional, the structure of four-dimensional even more multidimensional.
Of the present inventionly towerly refer to that the capacity of buffer memory at different levels is by the proportional distribution of hit rate, hit rate is higher, and the data amount check of buffer memory is more.Of the present invention adaptively should have the implication of three aspects: one when being data cached, automatically according to the buffer memory that the size Selection of data self is suitable, ensures that a new data can only enter a buffer memory; Two is when the capacity of buffer memory reaches in limited time, automatically the data dump of nearest least referenced is gone out buffer memory; Three is when obtaining data from buffer memory, the position (which rank of buffer memory is data be kept in) that automatic decision data are preserved, and provides unified access interface, hides details to user.Self-organization of the present invention refers to that the capacity of buffer memory at different levels can dynamically regulate, according to the hit rate that Real-time Collection arrives, under certain trigger condition, under ensureing the prerequisite that spatial cache total amount is constant, buffer memory capacity at different levels is made to be adjusted to optimum condition, to make wholely to be cached with maximum hit rate.
The tower caching method of self-adaptation self-organization of the present invention, can under the prerequisite of homogeneity storage medium, homogeneous data, automatically according to the buffer memory that the size Selection of data self is suitable, and the hit rate by automatically regulating the capacity of buffer memory to maximize buffer memory, there is adaptivity self-organization, unified access interface is externally provided, the efficiency utilization to storage space can be realized.
Accompanying drawing explanation
Fig. 1 is the data call schematic diagram based on caching technology;
Fig. 2 is tower buffer structure schematic diagram in embodiment;
Fig. 3 is the process flow diagram in embodiment, the data obtained from data center being added to buffer memory;
Fig. 4 is the process flow diagram obtaining data in embodiment from buffer memory;
Fig. 5 is the process flow diagram adjusting buffer memory capacity size at different levels in embodiment.
Embodiment
Below by specific embodiment, and coordinate accompanying drawing, the present invention is described further.
Fig. 2 is the structural representation of tower buffer memory of the present invention, and the data differed in size left in respectively in corresponding buffer memory by its size, externally provide unified access interface, the capacity of every level cache can regulate automatically.Tower caching data structure is made up of a series of caching container that is mutually related, the size of the data that each container can hold, and the number of data forms tower-like.The storage policy of caching container at different levels can be set: comprising in configuration file:
A) caching container (Container): the container storing data;
B) level (Level) is stored: the number of the caching container comprised in structure;
C) container threshold values (Limit): every level container can store the upper and lower bound of size of data:
The container threshold values upper bound (LimitUp): every level container can store the upper limit of size of data, is the lower bound of next stage buffer memory simultaneously; Container threshold values lower bound (LimitDown): every level container can store the lower limit of size of data, is the upper bound of upper level buffer memory simultaneously;
D) container capacity (Size): every level container can store the number of data.
Above-mentioned buffer structure is a kind of adaptive storage organization.After caching server receives new data from data center, suitable caching container is automatically selected to store according to the size of data.Externally provide the mode of service to be according to query characteristics value (querying condition), search in buffer memory at different levels, if had, then hit, return to user, this eigenwert is moved to tail of the queue simultaneously; If all do not found in buffer memory at different levels, then extract from data center, return to called side, store the data in the caching container of corresponding size, for follow-up inquiry simultaneously.
Above-mentioned buffer structure can carry out accurate capacity planning according to the size of available storage space (Capacity), neither wasting space, is unlikely to again to cause the problems such as Memory Leaks to occur.Its storage space takies formula:
Wherein, Capacity is the size of total storage space; LimitUp
iit is the upper bound that i-th grade of caching container can store data size; Size
iit is the number that can store data of i-th grade of caching container; Level is the number of caching container;
represent caching container maximum storage at different levels and.
Above-mentioned buffer structure externally provides unified data acquisition interface getData (key); Hiding data storage realizes details, and from the angle of user, data obtain from data center or obtain from buffer memory, without any difference on method of calling, ensure that the transparency to user.
Above-mentioned buffer structure is also a kind of storage organization of self-organization, in order to ensure the highest hit rate, in operational process, according to the hit rate (Hitrate in each caching container at the appointed time section
i) capability value (SizeNew that regulates buffer memory at different levels new dynamically
i); Its computing formula is as follows:
Hitrate
i＝Hitcount
i/Size
i（2）
SizeNew
i=Hitrate
i*Right*Size
i（4）
Wherein, Hitrate
iit is the hit rate of i-th grade of caching container; Hitcount
iit is the hit-count of i-th grade of caching container; Size
iit is the number that can store data of i-th grade of caching container; Right represents power (or adjustment factor); Capacity is the size of total storage space; LimitUp
iit is the upper bound that i-th grade of caching container can store data size; SizeNew
iit is the number that can store data after i-th grade of caching container regulates.
After operation after a while, under the prerequisite keeping storage space volume total amount constant, the capacity of the container making hit rate higher is larger, so just can ensure all the time to be cached with maximum hit rate.
Above-mentioned tower buffer structure has four kinds of operations, is specifically described below:
(1) structure initialization
Initiation parameter is read from configuration file, the number of levels (Level) of the caching container of initialisation structures, the upper bound of the storage data scale of container at different levels is (if the maximal value of the record number of the List stored, LimitUp), container at different levels can store the number (if the number of the List stored, Size) of data.
Each caching container contains the eigenwert queue (Queue) of a data-mapping (Map) and data, and the data upper bound and capacity (namely container at different levels recited above can store the number of data) two attributes, the upper bound that every one-level caching container can store size of data is also the lower bound that next stage caching container can store size of data simultaneously.This mapping is for preserving the key-value pair of data, and " key " is exactly the eigenwert of data, and " value " is exactly data itself; The data of this eigenwert queue for adopting lru algorithm (lru algorithm, Least Recently Use, least recently used algorithm) to manage buffer memory.
Be together in series container at different levels composition array, just constitutes tower buffer structure.
(2) data are added to buffer memory
Fig. 3 adds the process flow diagram of data to buffer memory, and its detailed step is as follows:
A. the feature (as querying condition, Key) of the data obtained from data center (as the value list inquired from database, Value) and data is assembled into structure Data, iteration variable i(is represented i-th grade of caching container) be initialized as 0;
Whether the scale (Data.size) B. judging to obtain data is less than the upper bound (LimitUp) of current container; If not, continue to compare with next container; Until condition is set up, carry out next step;
C. judge whether data amount check (Count) that current container comprises is less than the capacity (Size) of container; If not, D step is entered; If so, E step is entered;
D. from queue, delete head of the queue, from mapping, delete data value corresponding to head of the queue eigenwert, forward step C to, the data amount check of current container subtracts one; Its operation is as follows:
Delete current cache container queue heads, eigenwert is assigned to Key:Key=Cache [i] .queque.pop ();
Removing eigenwert in current cache container mappings is the data of Key: Cache [i] .map.remove (key);
The data amount check of current container subtracts one: Cache [i] .count--;
E. data are added to mapping; Add the eigenwert of data to tail of the queue, container data number adds one.Its operation is as follows;
The data inquired and eigenwert are added in the mapping of current cache container: Cache [i] .Map.add (Data.key, Data.value);
Query characteristics value is added to the tail of the queue of the queue of current cache container: Cache [i] .queue.add (Data.key);
The data amount check of current container adds one: Cache [i] .count++.
(3) search from buffer memory and obtain data
Fig. 4 is the process flow diagram searching and obtain data from buffer memory, and its detailed step is as follows:
A. receive the request inquired about from buffer memory from foreground, iteration variable is set to 0;
B. judge whether to comprise query characteristics value (Key) in the queue of current cache container, if not, iteration variable adds 1, continues to judge next stage buffer memory, until condition is set up, or has traveled through all buffer memorys; If so, then D step is entered into;
C. the hit of current cache is counted (Hitcount) add 1; The object of this operation is record hit-count, for the capacity adjusting buffer memory makes data encasement; Data value is obtained from the mapping of buffer memory.Its operation is as follows:
The hit of current cache is counted (Hitcount) add 1:Cache [i] .hitcount++;
Data value is obtained: Data.value=Cache [i] .map.get (key) from the mapping of current cache;
Query characteristics value is assigned to the Key attribute of data object: Data.key=key;
D. query characteristics value is moved to the tail of the queue of buffer queue; The object of this operation is the activity (i.e. LRU principle: finally accessed recently, removes at first) making the data of up-to-date hit keep maximum, and make the data for longer periods terminate-and-stay-resident of the most frequently accessing, the data of least referenced clear out buffer memory in time.Its operation is as follows:
Query characteristics value is removed: Cache [i] .queque.remove (key) from the queue of current cache container;
Query characteristics value is added to the tail of the queue of queue: Cache [i] .queue.add (key);
If E. inquired data in buffer memory, then data are returned to called side, otherwise from data center, go inquiry to obtain data.
(4) container capacity is adjusted
Fig. 5 is the process flow diagram of adjustment container capacity, and its detailed step is as follows:
A. the capacity total amount (Capacity) of the whole buffer memory of initialization, the value of temporal cache total amount (CapacityTemp) are 0; If specify capacity total amount in configuration file, then can not calculate this value; Initialization iteration variable value is 0.Its operation is as follows:
Initialization iteration variable value is 0:i=0;
The capacity total amount of the whole buffer memory of initialization is 0:Capacity=0;
The value of initialization temporal cache total amount is 0:CapacityTemp=0;
B. each caching container of iteration, calculates the hit rate of each buffer memory; Cumulative calculation buffer memory capacity total amount; The each buffer memory of cumulative calculation is multiplied by hit rate and unweighted capacity total amount; Its operation is as follows:
Hit rate (Hitrate)=hit number (Hitcount)/buffer memory capacity (Size) is namely:
Hitrate[i]=Cache[i].Hitcount/Cache[i].size；
Non-weighting buffer memory capacity (CapacityTemp)=each buffer memory capacity * hit rate, that is:
Capacity=Capacity+Cache[i].LimitUp*Cache[i].size；
Non-weighting buffer memory capacity sum=buffer memory at different levels non-weighting buffer memory capacity sum, that is:
CapacityTemp=CapacityTemp+Hitrate[i]*Cache[i].LimitUp*Cache[i].size；
C. iteration complete after, calculate weighted number; The object calculating flexible strategy keeps buffer memory capacity problem before and after adjustment constant.Its operation is as follows:
Flexible strategy (Right)=capacity total amount (Capacity)/non-weighting buffer memory capacity total amount, namely
Right=Capacity/CapacityTemp；
Putting iteration variable is 0, i.e. i=0;
D. iteration gives every level cache adjustment capability value again.It is operating as:
Capacity (Size) after adjustment=clicking rate * former capacity * flexible strategy, namely
Cache[i].size=Hitrate[i]*Cache[i].size*Right。
After operation above, can make the capacity of each container and hit rate in direct ratio, to make wholely to be cached with maximum hit rate.
In such scheme, whether start adjustment container capacity function, and how to touch adjustment container capacity function and can be configured at configuration file, make it the self-organization realizing capacity adjustment by predetermined strategy.
Above embodiment is only in order to illustrate technical scheme of the present invention but not to be limited; those of ordinary skill in the art can modify to technical scheme of the present invention or equivalent replacement; and not departing from the spirit and scope of the present invention, protection scope of the present invention should be as the criterion with described in claim.
Claims (9)
1. the tower caching method of self-adaptation self-organization, its step comprises:
1) setting up on caching server with data size is the multi-level buffer container of threshold values, and caching container at different levels is connected into an array, forms tower buffer structure;
2) described caching server receives data from data center, and the size according to the data received selects suitable caching container to store automatically;
3) under the prerequisite keeping storage space volume total amount constant, according to the capability value of the hit rate dynamic adjustments caching container at different levels in caching container at different levels at the appointed time section, the capacity of the caching container making hit rate higher is larger; The computing formula of the capability value of described dynamic adjustments caching container at different levels is:
Hitrate
i＝Hitcount
i/Size
i；
SizeNew
i＝Hitrate
i*Right*Size
i；
Wherein, Hitrate
iit is the hit rate of i-th grade of caching container; Hitcount
iit is the hit-count of i-th grade of caching container; Size
iit is the number that can store data of i-th grade of caching container; Right represents flexible strategy; Capacity is the size of total storage space; LimitUp
iit is the upper bound that i-th grade of caching container can store data size; SizeNew
iit is the number that can store data after i-th grade of caching container regulates.
2. the method for claim 1, is characterized in that: the access privileges setting caching container at different levels according to buffer memory clicking rate, and the higher then priority of clicking rate is higher.
3. the method for claim 1, is characterized in that: described multi-level buffer container externally provides unified data acquisition interface, and hiding data storage realizes details.
4. the method for claim 1, is characterized in that: every grade of caching container contains one for preserving the data-mapping of the key-value pair of data, and a data feature values queue.
5. method as claimed in claim 4, is characterized in that: described data feature values queue adopts lru algorithm management data cached.
6. the method for claim 1, is characterized in that: on the described multi-level buffer vessel base taking data size as threshold values, build N tie up buffer structure, wherein N >=2.
7. method as claimed in claim 6, it is characterized in that: on the described multi-level buffer vessel base taking data size as threshold values, the query time obtaining data from data center is built two-dimentional buffer structure as another threshold values, and the time that the data that wherein query time is longer retain in the buffer is longer.
8. the method for claim 1, is characterized in that: in configuration file, set the storage level of caching container at different levels, container threshold values and container capacity.
9. the method for claim 1, is characterized in that: configure in configuration file and whether start adjustment container capacity function and how to touch adjustment container capacity function, to realize the self-organization of capacity adjustment.
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| CN104424119B (en) * | 2013-08-26 | 2018-07-06 | 联想(北京)有限公司 | Memory space configuration method and device |
| CN103778071A (en) * | 2014-01-20 | 2014-05-07 | 华为技术有限公司 | Cache space distribution method and device |
| WO2017031637A1 (en) * | 2015-08-21 | 2017-03-02 | 华为技术有限公司 | Memory access method, apparatus and system |
| CN107305531B (en) * | 2016-04-20 | 2020-10-16 | 阿里巴巴（中国）有限公司 | Method and device for determining limit value of cache capacity and computing equipment |
| CN107977165B (en) * | 2017-11-22 | 2021-01-08 | 用友金融信息技术股份有限公司 | Data cache optimization method and device and computer equipment |
| CN110968562B (en) * | 2019-11-28 | 2023-05-12 | 国网上海市电力公司 | Cache self-adaptive adjustment method and equipment based on ZFS file system |
| CN112395322B (en) * | 2020-12-07 | 2021-06-01 | 湖南新云网科技有限公司 | List data display method and device based on hierarchical cache and terminal equipment |
| CN112988619A (en) * | 2021-02-08 | 2021-06-18 | 北京金山云网络技术有限公司 | Data reading method and device and electronic equipment |
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| US5394531A (en) * | 1989-04-03 | 1995-02-28 | International Business Machines Corporation | Dynamic storage allocation system for a prioritized cache |
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