CN108153483A - A kind of time series data compression method based on attribute grouping - Google Patents
A kind of time series data compression method based on attribute grouping Download PDFInfo
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- CN108153483A CN108153483A CN201611106869.XA CN201611106869A CN108153483A CN 108153483 A CN108153483 A CN 108153483A CN 201611106869 A CN201611106869 A CN 201611106869A CN 108153483 A CN108153483 A CN 108153483A
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F3/00—Input arrangements for transferring data to be processed into a form capable of being handled by the computer; Output arrangements for transferring data from processing unit to output unit, e.g. interface arrangements
- G06F3/06—Digital input from, or digital output to, record carriers, e.g. RAID, emulated record carriers or networked record carriers
- G06F3/0601—Interfaces specially adapted for storage systems
- G06F3/0602—Interfaces specially adapted for storage systems specifically adapted to achieve a particular effect
- G06F3/0608—Saving storage space on storage systems
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F3/00—Input arrangements for transferring data to be processed into a form capable of being handled by the computer; Output arrangements for transferring data from processing unit to output unit, e.g. interface arrangements
- G06F3/06—Digital input from, or digital output to, record carriers, e.g. RAID, emulated record carriers or networked record carriers
- G06F3/0601—Interfaces specially adapted for storage systems
- G06F3/0628—Interfaces specially adapted for storage systems making use of a particular technique
- G06F3/0638—Organizing or formatting or addressing of data
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F3/00—Input arrangements for transferring data to be processed into a form capable of being handled by the computer; Output arrangements for transferring data from processing unit to output unit, e.g. interface arrangements
- G06F3/06—Digital input from, or digital output to, record carriers, e.g. RAID, emulated record carriers or networked record carriers
- G06F3/0601—Interfaces specially adapted for storage systems
- G06F3/0628—Interfaces specially adapted for storage systems making use of a particular technique
- G06F3/0646—Horizontal data movement in storage systems, i.e. moving data in between storage devices or systems
- G06F3/065—Replication mechanisms
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F3/00—Input arrangements for transferring data to be processed into a form capable of being handled by the computer; Output arrangements for transferring data from processing unit to output unit, e.g. interface arrangements
- G06F3/06—Digital input from, or digital output to, record carriers, e.g. RAID, emulated record carriers or networked record carriers
- G06F3/0601—Interfaces specially adapted for storage systems
- G06F3/0628—Interfaces specially adapted for storage systems making use of a particular technique
- G06F3/0655—Vertical data movement, i.e. input-output transfer; data movement between one or more hosts and one or more storage devices
- G06F3/0656—Data buffering arrangements
Abstract
The present invention discloses a kind of time series data compression method being grouped based on attribute, and the data of each measuring point are saved in buffering area, time series data sequence is divided into timestamp, value, Quality Codes sequence；Logging timestamp sequence initial time, then the timestamp with the identical change period is sequentially grouped, record the difference of every group of initial time stamp and this buffering area initial time, the period of change of the group and this group of timestamp number；Floating number is resolved into " symbol index " position and " mantissa bit ", the floating number with identical " symbol index " position is sequentially grouped, records " symbol index " position, floating number number and " mantissa " bit sequence being each grouped；Quality Codes sequence with identical quality code value is sequentially grouped, records the value and number being each grouped；Grouped data is copied into compressed buffer, lossless compression is carried out to the buffering area using zlib algorithms.Such method has the characteristics that compression ratio is high and fireballing, can effectively save memory space use.
Description
Technical field
The invention belongs to time series data process field, more particularly to a kind of time series data compression side based on attribute grouping
Method.
Background technology
Time series data treatment technology be widely used in Wide Area Measurement System (WAMS), supervisory control of substation, scheduling, direct current,
In multiple systems such as steady control, meets the needs of it stores magnanimity, high-frequency data.The technology is mainly used for storing second grade, Millisecond
High-frequency data, this type data generally have periodic characteristics, and each storage period, which can generate batch of data, to be needed to store, therefore
Data volume is very big, more demanding to the compression of data, and different from traditional relational database, the storage of time series data will meet
While storing required precision, its compression ratio is improved as possible, maximally utilizes disk space.
Since data volume is big, high, traditional when ordinal number is required to processing speed and compression ratio for storage for time series data
It is as follows according to compression method type and its defect：
(1) revolving door compresses
This method sets " dead band value " and " dead time " before compression, realizes data compression by the two attributes, is
A kind of compression method, it will usually data be made to have a degree of distortion, be not suitable for data precision and accuracy rate requirement
Higher scene.
(2) initial data combines compression algorithm of increasing income
It using zlib or lz series of compression algorithms, is directly compressed to initial data, this method is realized simply, but is pressed
Shrinkage is not high, the characteristics of not making full use of time series data, realizes fully compression.
Based on traditional time series data compression method there are the defects of, thus this case generates.
Invention content
The purpose of the present invention is to provide a kind of time series data compression method being grouped based on attribute, with compression ratio
High and fireballing feature can effectively save memory space use.
In order to achieve the above objectives, solution of the invention is：
A kind of time series data compression method based on attribute grouping, includes the following steps：
(1) server-side receives and preserves the data of each measuring point to buffering area, by time series data sequence be divided into timestamp,
Value, Quality Codes sequence；
(2) logging timestamp sequence initial time, then the timestamp with the identical change period is sequentially grouped, record is every
Group initial time stamp and the difference of this buffering area initial time, the period of change and this group of timestamp number of the group；
(3) floating number is resolved into " symbol-index " position and " mantissa bit ", there will be the floating of identical " symbol-index " position
Points are sequentially grouped, and record " symbol-index " position, floating number number and " mantissa " bit sequence being each grouped；
(4) the Quality Codes sequence with identical quality code value is sequentially grouped, records the Quality Codes being each grouped and quality
Code number；
(5) grouped data is copied into compressed buffer, lossless compression is carried out to the buffering area using zlib algorithms.
In above-mentioned steps (1), each buffer size acquiescence is set as 1024, and every time series data is a triple<When
Between stab, be worth, Quality Codes>, it is that a buffering area is distributed per data, when buffering area is full, by time series data sequence in buffering area
It is timestamp sequence by three Attribute transpositions, value sequence, Quality Codes sequence, starts compression process.
In above-mentioned steps (2), for the timestamp sequence { t that a length is m1,t2,…tm, it is assumed that it is divided into s grouping,
Sequence after being then grouped becomes { TΔ1,P1,n1},{TΔ2,P2,n2},{TΔ3,P3,n3}…{TΔs,Ps,ns, wherein n1+n2+…ns
=m.
In above-mentioned steps (3), a floating-point Number Sequence with k different " symbol-index " positions is with following form：
{sign1,n1,tail1},{sign2,n2,tail2},…,{signk,nk,tailk, wherein, signiIt is public " symbol-refer to
Number " position, tailiIt is mantissa's bit sequence, niIt is mantissa for sequence length, i=1,2 ..., k.
The detailed content of above-mentioned steps (3) is：It is represented according to the memory of 32 single precision floating datums, floating number is split into
1 bit sign position, 8 exponent bits, 23 mantissa bits extract 9 public " symbol-index " positions, will have identical " symbol-refer to
The floating number of number " position is sequentially grouped, " symbol-index " position, " mantissa bit " sequence, mantissa bit sequence length in record group.
In above-mentioned steps (4), the form of a Quality Codes sequence for having l grouping is：{f1,n1},{f2,n2},{f3,
n3},…,{fl,nl, wherein, fiIt is quality code value, niIt is the identical Quality Codes number of the group, i=1,2 ..., l.
In above-mentioned steps (5), memory distribution in compressed buffer is as follows：Initial time, and timestamp grouping, floating point values grouping,
Quality Codes are grouped }.
After using the above scheme, the invention has the characteristics that：
(1) compression ratio significantly improves, and average loss-less compression ratio is greatly saved memory space and occupied up to 10 times；
(2) data volume for needing to compress the grouping of initial data is greatly reduced, compression efficiency is high；
(3) even for the unconspicuous time series data of periodic regularity, also there is good compression effectiveness.
Description of the drawings
Fig. 1 is that the present invention is grouped schematic diagram by attribute；
Fig. 2 is timestamp sequence of packets schematic diagram of the present invention；
Fig. 3 is floating point values sequence of packets schematic diagram of the present invention；
Fig. 4 is Quality Codes sequence of packets schematic diagram of the present invention；
Fig. 5 is the flow chart of the present invention.
Specific embodiment
Below with reference to attached drawing, technical scheme of the present invention and advantageous effect are described in detail.
As shown in figure 5, the present invention provides a kind of time series data compression method based on attribute grouping, include the following steps：
(1) data that server-side receives and preserves each measuring point, when buffering area is full, buffering area are divided into buffering area
Three timestamp sequence, value sequence and Quality Codes sequence sequences start compression process；
(2) the initial time T of logging timestamp sequencestart, sequential scan timestamp sequence will have the identical change period
Timestamp be classified as one group, record the initial time stamp of the group and this buffering area initial time TstartPoor TΔ, re-record the group
Period of change P and this group of timestamp number, realize timestamp compression；
For the timestamp sequence { t that a length is m1,t2,…tm, it is assumed that it is divided into s grouping, then the sequence after being grouped
Become { TΔ1,P1,n1},{TΔ2,P2,n2},{TΔ3,P3,n3}…{TΔs,Ps,ns, wherein, n1+n2+…ns=m；TstartUse 8
Byte long shaping expression, TΔIt is represented using 4 byte shapings, P is represented using 2 byte shapings, and n is represented using 2 byte shapings.For
Timestamp sequence with identical period of change, occupied byte number is 8+4+2+2=16 bytes after grouping, far fewer than
Original series byte number 8*1024=8192 bytes.
(3) floating-point Number Sequence according to sign bit, exponent bits, decimal place is decomposed, extracts public " symbol-index
Position ", and mantissa is blocked, retain 16 mantissa, composition mantissa sequence realizes the compression to floating-point Number Sequence；
Particular content is：Represented according to the memory of 32 single precision floating datums, by floating number split into 1 bit sign position, 8
Exponent bits, 23 mantissa bits, according to the characteristics of time series data, within one continuous time, the sign bit and index of floating number
Position will not generally change, therefore extract 9 public " symbol-index " positions, will have the floating of identical " symbol-index " position
Points are sequentially grouped, " symbol-index " position, " mantissa bit " sequence, mantissa bit sequence length in record group.23 mantissa bits can root
It is blocked according to different storage precision：It is very high for required precision, do not allow the memory requirement of any loss of significance, without
It is any to block；And exchange the requirement of more high compression rate for for certain storage precision can be sacrificed, mantissa bit is usually truncated into 16
Position is even less.One floating-point Number Sequence with k different " symbol-index " positions is with following form：{sign1,n1,
tail1},{sign2,n2,tail2},…,{signk,nk,tailk, wherein signiIt is public " symbol-index " position, tailiIt is
Mantissa's bit sequence, niIt is mantissa for sequence length, i=1,2 ..., k.
Sign is represented using 2 byte shapings, is 16 by mantissa rounding, is represented tail using 2 byte shapings, is used 2 words
It saves shaping and represents n.For the floating-point value sequence with identical " symbol-index " position, shared byte number is 2+2+ after grouping
2*1024=2052, original occupancy byte number is 4*1024=4096, realizes the compression connect by about one time.
(4) Quality Codes sequence is sequentially traversed, the Quality Codes sequence with identical quality code value is sequentially grouped, and is recorded every
The Quality Codes of a grouping and its Quality Codes number, realize the compression to Quality Codes；
One Quality Codes sequence for having l grouping has this form：{f1,n1},{f2,n2},{f3,n3..., { fl,nlIts
In, fiIt is quality code value, niIt is the identical Quality Codes number of the group, i=1,2 ..., l.
F is represented using 4 byte shapings, n is represented using 2 byte shapings, for the sequence with identical Quality Codes, is divided
Shared byte number is 4+2=6 after group, and original occupancy byte number is 4*1024=4096.
(5) it by the above-mentioned data by grouping, copies in one piece of continuous compressed buffer, compressed buffer memory point
Cloth is schematically as follows：{ initial time, timestamp grouping, floating point values grouping, Quality Codes grouping }, buffers compression using zlib algorithms
Area carries out lossless compression.
Below with reference to the realization step of specific embodiment, illustrate technical scheme of the present invention：
(1) as shown in Figure 1, receiving and caching time series data to buffering area, which has following characteristics：When
Between stamp from 2016-9-2800:00:00 starts (UTC timestamp is expressed as in memory), and storing frequencies are 1 second, and floating point values is initially
3.14, each cycle increases by 0.01, and Quality Codes are constant for 3.According to the three of time series data sequence attributes, it is divided into the time
Three stamp, value, Quality Codes sequences, are one per data<Timestamp, value, Quality Codes>Triple, when buffering area reaches 1024
During data, start compression process.
(2) as shown in Fig. 2, in the form of UTC timestamp logging timestamp sequence initial time Tstart=
1474992000000000, timestamp sequence is divided into one group { 0,1000,1024 }, wherein rising by sequential scan timestamp sequence
Beginning timestamp and TstartDifference TΔ=0, the period of change P=1000 (ms) of this group, this group of timestamp number n=1024.
(3) as shown in figure 3, " symbol-index " position of floating-point Number Sequence is extracted, due to the value variation range of floating-point Number Sequence
Smaller, which has identical " symbol-index " position, and " mantissa " position is blocked, and retains 16 mantissa, whole with 2 bytes
Shape stores, and forms 3 groupings：{ 128,87, tail1, { 129,399, tail2, { 130,538, tail3}。
(4) as shown in figure 4, due to Quality Codes all same, it is only necessary to the Quality Codes and its number that record is uniquely grouped, this point
Group form is { 3,1024 }.
(5) it after the completion of above-mentioned grouping, copies data in the buffer of compressed buffer, the buffer structures after replicating
Continuous arrangement for above-mentioned grouping：{ 1474992000000000 } { 0,1000,1024 } { 128,87, tail1129,399,
tail2{ 130,538, tail3{ 3,1024 }, lossless compression is carried out to the buffering area using zlib algorithms.
The present invention is suitable for the compression storing data of timing sequence library server-side, has the characteristics that compression ratio is high, compression speed is fast,
Under the premise of not influencing to store precision, solve the problems, such as that magnanimity time series data memory space utilization rate is low, breach tradition
The bottleneck of lossless compression mode.
Above example is merely illustrative of the invention's technical idea, it is impossible to protection scope of the present invention is limited with this, it is every
According to technological thought proposed by the present invention, any change done on the basis of technical solution each falls within the scope of the present invention
Within.
Claims (7)
1. a kind of time series data compression method based on attribute grouping, it is characterised in that include the following steps：
(1) server-side receives and preserves the data of each measuring point to buffering area, by time series data sequence be divided into timestamp, value,
Quality Codes sequence；
(2) logging timestamp sequence initial time, then the timestamp with the identical change period is sequentially grouped, it records every group and rises
Beginning timestamp and the difference of this buffering area initial time, the period of change and this group of timestamp number of the group；
(3) floating number is resolved into " symbol-index " position and " mantissa bit ", the floating number that there will be identical " symbol-index " position
It is sequentially grouped, records " symbol-index " position, floating number number and " mantissa " bit sequence being each grouped；
(4) the Quality Codes sequence with identical quality code value is sequentially grouped, records the Quality Codes being each grouped and Quality Codes
Number；
(5) grouped data is copied into compressed buffer, lossless compression is carried out to the buffering area using zlib algorithms.
2. a kind of time series data compression method based on attribute grouping as described in claim 1, it is characterised in that：The step
(1) in, each buffer size acquiescence is set as 1024, and every time series data is a triple<Timestamp, value, Quality Codes>,
A buffering area is distributed for every data, when buffering area is full, is by three Attribute transpositions by time series data sequence in buffering area
Timestamp sequence, value sequence, Quality Codes sequence start compression process.
3. a kind of time series data compression method based on attribute grouping as described in claim 1, it is characterised in that：The step
(2) in, for the timestamp sequence { t that a length is m1,t2,…tm, it is assumed that it is divided into s grouping, then the sequence after being grouped becomes
Into { TΔ1,P1,n1},{TΔ2,P2,n2},{TΔ3,P3,n3}…{TΔs,Ps,ns, wherein n1+n2+…ns=m.
4. a kind of time series data compression method based on attribute grouping as described in claim 1, it is characterised in that：The step
(3) in, a floating-point Number Sequence with k different " symbol-index " positions is with following form：{sign1,n1,tail1},
{sign2,n2,tail2},…,{signk,nk,tailk, wherein, signiIt is public " symbol-index " position, tailiIt is mantissa bit
Sequence, niIt is mantissa for sequence length, i=1,2 ..., k.
5. a kind of time series data compression method based on attribute grouping as described in claim 1, it is characterised in that：The step
(3) detailed content is：Represented according to the memory of 32 single precision floating datums, by floating number split into 1 bit sign position, 8 refer to
Numerical digit, 23 mantissa bits extract 9 public " symbol-index " positions, and the floating number with identical " symbol-index " position is pressed
Sequence is grouped, " symbol-index " position, " mantissa bit " sequence, mantissa bit sequence length in record group.
6. a kind of time series data compression method based on attribute grouping as described in claim 1, it is characterised in that：The step
(4) in, the form of a Quality Codes sequence for having l grouping is：{f1,n1},{f2,n2},{f3,n3},…,{fl,nl, wherein,
fiIt is quality code value, niIt is the identical Quality Codes number of the group, i=1,2 ..., l.
7. a kind of time series data compression method based on attribute grouping as described in claim 1, it is characterised in that：The step
(5) in, memory distribution in compressed buffer is as follows：{ initial time, timestamp grouping, floating point values grouping, Quality Codes grouping }.
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| CN110504974A (en) * | 2019-08-20 | 2019-11-26 | 北京四方继保自动化股份有限公司 | D-PMU measurement data segmentation slice mixing compression and storage method and device |
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| CN116069743A (en) * | 2023-03-06 | 2023-05-05 | 齐鲁工业大学(山东省科学院) | Fluid data compression method based on time sequence characteristics |
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| CN114595270A (en) * | 2022-02-23 | 2022-06-07 | 南京云蝙信息技术有限公司 | Time sequence data efficient compression method based on big data |
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