2012联邦选举委员会数据库中赞助人和赞助模式的分析统计

2023-11-04 03:20
文章标签 分析 统计 模式 数据库 2012 选举 委员会 赞助 联邦 赞助人

本文主要是介绍2012联邦选举委员会数据库中赞助人和赞助模式的分析统计，希望对大家解决编程问题提供一定的参考价值，需要的开发者们随着小编来一起学习吧！

本例为《利用Python进行数据分析》中第九章最后一节实例，数据来源Github
https://github.com/wesm/pydata-book/tree/2nd-edition/datasets/fec

#2012联邦选举委员会数据库中赞助人和赞助模式的分析统计
import pandas as pd
import numpy as np
from pandas import Series,DataFrame
from numpy.random import randn,rand
import matplotlib.pyplot as plt

fec=pd.read_csv('P00000001-ALL.csv')
fec.head()

D:\application_setup\Anaconda\lib\site-packages\IPython\core\interactiveshell.py:3063: DtypeWarning: Columns (6) have mixed types.Specify dtype option on import or set low_memory=False.interactivity=interactivity, compiler=compiler, result=result)
cmte_idcand_idcand_nmcontbr_nmcontbr_citycontbr_stcontbr_zipcontbr_employercontbr_occupationcontb_receipt_amtcontb_receipt_dtreceipt_descmemo_cdmemo_textform_tpfile_num
0C00410118P20002978Bachmann, MichelleHARVEY, WILLIAMMOBILEAL3.6601e+08RETIREDRETIRED250.020-JUN-11NaNNaNNaNSA17A736166
1C00410118P20002978Bachmann, MichelleHARVEY, WILLIAMMOBILEAL3.6601e+08RETIREDRETIRED50.023-JUN-11NaNNaNNaNSA17A736166
2C00410118P20002978Bachmann, MichelleSMITH, LANIERLANETTAL3.68633e+08INFORMATION REQUESTEDINFORMATION REQUESTED250.005-JUL-11NaNNaNNaNSA17A749073
3C00410118P20002978Bachmann, MichelleBLEVINS, DARONDAPIGGOTTAR7.24548e+08NONERETIRED250.001-AUG-11NaNNaNNaNSA17A749073
4C00410118P20002978Bachmann, MichelleWARDENBURG, HAROLDHOT SPRINGS NATIONAR7.19016e+08NONERETIRED300.020-JUN-11NaNNaNNaNSA17A736166
fec.columns

Index(['cmte_id', 'cand_id', 'cand_nm', 'contbr_nm', 'contbr_city','contbr_st', 'contbr_zip', 'contbr_employer', 'contbr_occupation','contb_receipt_amt', 'contb_receipt_dt', 'receipt_desc', 'memo_cd','memo_text', 'form_tp', 'file_num'],dtype='object')

fec.iloc[123456]

cmte_id                             C00431445
cand_id                             P80003338
cand_nm                         Obama, Barack
contbr_nm                         ELLMAN, IRA
contbr_city                             TEMPE
contbr_st                                  AZ
contbr_zip                          852816719
contbr_employer      ARIZONA STATE UNIVERSITY
contbr_occupation                   PROFESSOR
contb_receipt_amt                          50
contb_receipt_dt                    01-DEC-11
receipt_desc                              NaN
memo_cd                                   NaN
memo_text                                 NaN
form_tp                                 SA17A
file_num                               772372
Name: 123456, dtype: object

unique_cands=fec.cand_nm.unique()

unique_cands

array(['Bachmann, Michelle', 'Romney, Mitt', 'Obama, Barack',"Roemer, Charles E. 'Buddy' III", 'Pawlenty, Timothy','Johnson, Gary Earl', 'Paul, Ron', 'Santorum, Rick','Cain, Herman', 'Gingrich, Newt', 'McCotter, Thaddeus G','Huntsman, Jon', 'Perry, Rick'], dtype=object)

unique_cands[2]

'Obama, Barack'

#竞选人所属政党
parties={'Bachmann, Michelle':'Republican','Romney, Mitt':'Republican','Gingrich, Newt':'Republican','Huntsman, Jon':'Republican','Johnson, Gary Earl':'Republican','McCotter, Thaddeus G':'Republican','Obama, Barack':'Democrat','Paul, Ron':'Republican','Pawlenty, Timothy':'Republican','Perry, Rick':'Republican',"Roemer, Charles E. 'Buddy' III":'Republican','Romney, Mitt':'Republican','Santorum, Rick':'Republican'}

#这里出现了一个问题，就是我辛辛苦苦把竞选人匹配党派敲出来以后，一检查，发现#这里出现了一个问题，
#parties字典长度跟unique_cands的长度不匹配，这不是坑爹么，应该是少敲了一个
len(parties)

12

len(unique_cands)

13

cands=[name for name in parties]#取parties字典的key值，以列表推导式形式输出

cands=np.array(cands)
cands

array(['Bachmann, Michelle', 'Romney, Mitt', 'Gingrich, Newt','Huntsman, Jon', 'Johnson, Gary Earl', 'McCotter, Thaddeus G','Obama, Barack', 'Paul, Ron', 'Pawlenty, Timothy', 'Perry, Rick',"Roemer, Charles E. 'Buddy' III", 'Santorum, Rick'], dtype='<U30')

#np.in1d()为测试一个数组在另一个数组中的成员资格，
np.in1d(unique_cands,cands)

array([ True,  True,  True,  True,  True,  True,  True,  True, False,True,  True,  True,  True])

#np.setdiff1d()集合差函数
np.setdiff1d(unique_cands,unique_cands[np.in1d(unique_cands,cands)])

array(['Cain, Herman'], dtype=object)

#把漏掉的加到parties字典上
parties['Cain, Herman']='Republican'

parties

{'Bachmann, Michelle': 'Republican','Romney, Mitt': 'Republican','Gingrich, Newt': 'Republican','Huntsman, Jon': 'Republican','Johnson, Gary Earl': 'Republican','McCotter, Thaddeus G': 'Republican','Obama, Barack': 'Democrat','Paul, Ron': 'Republican','Pawlenty, Timothy': 'Republican','Perry, Rick': 'Republican',"Roemer, Charles E. 'Buddy' III": 'Republican','Santorum, Rick': 'Republican','Cain, Herman': 'Republican'}

#在原数据集上新增党派关系列
fec['party']=fec.cand_nm.map(parties)

fec.head()
cmte_idcand_idcand_nmcontbr_nmcontbr_citycontbr_stcontbr_zipcontbr_employercontbr_occupationcontb_receipt_amtcontb_receipt_dtreceipt_descmemo_cdmemo_textform_tpfile_numparty
0C00410118P20002978Bachmann, MichelleHARVEY, WILLIAMMOBILEAL3.6601e+08RETIREDRETIRED250.020-JUN-11NaNNaNNaNSA17A736166Republican
1C00410118P20002978Bachmann, MichelleHARVEY, WILLIAMMOBILEAL3.6601e+08RETIREDRETIRED50.023-JUN-11NaNNaNNaNSA17A736166Republican
2C00410118P20002978Bachmann, MichelleSMITH, LANIERLANETTAL3.68633e+08INFORMATION REQUESTEDINFORMATION REQUESTED250.005-JUL-11NaNNaNNaNSA17A749073Republican
3C00410118P20002978Bachmann, MichelleBLEVINS, DARONDAPIGGOTTAR7.24548e+08NONERETIRED250.001-AUG-11NaNNaNNaNSA17A749073Republican
4C00410118P20002978Bachmann, MichelleWARDENBURG, HAROLDHOT SPRINGS NATIONAR7.19016e+08NONERETIRED300.020-JUN-11NaNNaNNaNSA17A736166Republican
#该数据赞助也包括退款，及存在负数出资额
(fec.contb_receipt_amt>0).value_counts()

True     991475
False     10256
Name: contb_receipt_amt, dtype: int64

fec.cand_nm.isin(['Obama, Barack','Romney, Mitt'])

0          False
1          False
2          False
3          False
4          False...  
1001726    False
1001727    False
1001728    False
1001729    False
1001730    False
Name: cand_nm, Length: 1001731, dtype: bool

#筛选出只包含指定两位竞选人的赞助信息
fec_mrbo=fec[fec.cand_nm.isin(['Obama, Barack','Romney, Mitt'])]

fec_mrbo.head()
cmte_idcand_idcand_nmcontbr_nmcontbr_citycontbr_stcontbr_zipcontbr_employercontbr_occupationcontb_receipt_amtcontb_receipt_dtreceipt_descmemo_cdmemo_textform_tpfile_numparty
411C00431171P80003353Romney, MittELDERBAUM, WILLIAMDPOAA3.4023e+08US GOVERNMENTFOREIGN SERVICE OFFICER25.001-FEB-12NaNNaNNaNSA17A780124Republican
412C00431171P80003353Romney, MittELDERBAUM, WILLIAMDPOAA3.4023e+08US GOVERNMENTFOREIGN SERVICE OFFICER110.001-FEB-12NaNNaNNaNSA17A780124Republican
413C00431171P80003353Romney, MittCARLSEN, RICHARDAPOAE9.128e+07DEFENSE INTELLIGENCE AGENCYINTELLIGENCE ANALYST250.013-APR-12NaNNaNNaNSA17A785689Republican
414C00431171P80003353Romney, MittDELUCA, PIERREAPOAE9.128e+07CISCOENGINEER30.021-AUG-11NaNNaNNaNSA17A760261Republican
415C00431171P80003353Romney, MittSARGENT, MICHAELAPOAE9.01201e+07RAYTHEON TECHNICAL SERVICES CORPCOMPUTER SYSTEMS ENGINEER100.007-MAR-12NaNNaNNaNSA17A780128Republican
#根据职业和雇主统计赞助信息
occs=fec['contbr_occupation'].unique()

occs

array(['RETIRED', 'INFORMATION REQUESTED', 'RN', ...,'SAFETY SALES MANAGER', 'PRESIDENT & GENERAL MANAGER','LONGWALL MAINTENANCE FOREMAN'], dtype=object)

len(fec['contbr_occupation'].unique())

45074

#许多职业都涉及相同的基本工作类型，或者同一样东西有多种变体。清理一些这类的数据
occ_mapping={'INFORMATION REQUESTED':'NOT PROVIDED','INFORMATION REQUESTED PER BEST EFFORTS':'NOT PROVIDED','INFORMATION REQUESTED(BEST EFFORTS)':'NOT PROVIDED','C.E.O.':'CEO'
}

fec.groupby('contbr_occupation')['party'].count()
contbr_occupationMIXED-MEDIA ARTIST / STORYTELLER    1AREA VICE PRESIDENT                   1RESEARCH ASSOCIATE                    1TEACHER                               1THERAPIST                             3..
ZOOKEEPER                              1
ZOOLOGIST                              3
ZOOLOGY EDUCATION                      1
\NONE\                                 1
~                                      1
Name: party, Length: 45073, dtype: int64


#如果没有提供相关映射，返回x
f=lambda x: occ_mapping.get(x,x)
fec.contbr_occupation=fec.contbr_occupation.map(f)
#对雇主信息进行同样的处理
emp_mapping={'INFORMATION REQUESTED PER BEST EFFORTS':'NOT PROVIDED','INFORMATION REQUESTED':'NOT PROVIDED','SELF':'SELF-EMPLOYED','SELF EMPLOYED':'SELF-EMPLOYED'}
#如果没有提供相关的映射，则返回x
f=lambda x:emp_mapping.get(x,x)
fec.contbr_employer=fec.contbr_employer.map(f)

#根据党派和职业对数据进行整合，滤除总出资额不足200万美金的数据
by_occupation=fec.pivot_table(values='contb_receipt_amt',index='contbr_occupation',columns='party',aggfunc='sum')

by_occupation
partyDemocratRepublican
contbr_occupation
MIXED-MEDIA ARTIST / STORYTELLER100.0NaN
AREA VICE PRESIDENT250.0NaN
RESEARCH ASSOCIATE100.0NaN
TEACHER500.0NaN
THERAPIST3900.0NaN
.........
ZOOKEEPER35.0NaN
ZOOLOGIST400.0NaN
ZOOLOGY EDUCATION25.0NaN
\NONE\NaN250.0
~NaN75.0

45071 rows × 2 columns

#过滤掉总出资额不足200万美金的数据
over_2mm=by_occupation[by_occupation.sum(1)>2000000]

over_2mm
partyDemocratRepublican
contbr_occupation
ATTORNEY11126932.977.343540e+06
CEO2071474.793.932686e+06
CONSULTANT2459812.712.472815e+06
ENGINEER950425.551.780102e+06
EXECUTIVE1355161.053.918557e+06
HOMEMAKER4243394.301.324141e+07
INVESTOR884133.002.320349e+06
LAWYER3159391.873.785903e+05
MANAGER762693.221.404878e+06
NOT PROVIDED4849801.961.975207e+07
OWNER998867.362.279621e+06
PHYSICIAN3732387.443.491657e+06
PRESIDENT1878009.954.469834e+06
PROFESSOR2163571.082.944627e+05
REAL ESTATE528752.091.581747e+06
RETIRED25270507.232.290594e+07
SELF-EMPLOYED667393.401.591758e+06
#用直方图来展现更为清晰
over_2mm.plot(kind='barh')

<matplotlib.axes._subplots.AxesSubplot at 0x1700e31efc8>

在这里插入图片描述

#统计对Romney, Mitt和Obama, Barack总出资额最高的职业和企业家

fec_mrbo
cmte_idcand_idcand_nmcontbr_nmcontbr_citycontbr_stcontbr_zipcontbr_employercontbr_occupationcontb_receipt_amtcontb_receipt_dtreceipt_descmemo_cdmemo_textform_tpfile_numparty
411C00431171P80003353Romney, MittELDERBAUM, WILLIAMDPOAA3.4023e+08US GOVERNMENTFOREIGN SERVICE OFFICER25.001-FEB-12NaNNaNNaNSA17A780124Republican
412C00431171P80003353Romney, MittELDERBAUM, WILLIAMDPOAA3.4023e+08US GOVERNMENTFOREIGN SERVICE OFFICER110.001-FEB-12NaNNaNNaNSA17A780124Republican
413C00431171P80003353Romney, MittCARLSEN, RICHARDAPOAE9.128e+07DEFENSE INTELLIGENCE AGENCYINTELLIGENCE ANALYST250.013-APR-12NaNNaNNaNSA17A785689Republican
414C00431171P80003353Romney, MittDELUCA, PIERREAPOAE9.128e+07CISCOENGINEER30.021-AUG-11NaNNaNNaNSA17A760261Republican
415C00431171P80003353Romney, MittSARGENT, MICHAELAPOAE9.01201e+07RAYTHEON TECHNICAL SERVICES CORPCOMPUTER SYSTEMS ENGINEER100.007-MAR-12NaNNaNNaNSA17A780128Republican
......................................................
701381C00431445P80003338Obama, BarackMOUNTS, ROBERTAPOZZ962043077HQ USFK (FKDC-SA)GS-15 INTERNATIONAL RELATIONS OFFICER25.026-FEB-12NaNNaNNaNSA17A787803Democrat
701382C00431445P80003338Obama, BarackTAITANO, TYRONEHAGATNAZZ969323373NOT EMPLOYEDRETIRED250.020-JAN-12NaNNaNNaNSA17A775668Democrat
701383C00431445P80003338Obama, BarackTUCKER, PAMELAAPOZZ963190030DODEAEDUCATOR3.020-JAN-12NaNNaNNaNSA17A775668Democrat
701384C00431445P80003338Obama, BarackMOUNTS, ROBERTAPOZZ962043077HQ USFK (FKDC-SA)GS-15 INTERNATIONAL RELATIONS OFFICER25.026-APR-12NaNNaNNaNSA17A785239Democrat
701385C00431445P80003338Obama, BarackNEAL, AMBERAPOZZ091022065THE DEPARTMENT OF DEFENSE EDUCATION ACTEACHER135.004-SEP-11NaNX* OBAMA VICTORY FUND 2012SA18756218Democrat

700975 rows × 17 columns

grouped=fec_mrbo.groupby('cand_nm')

def get_top_amounts(group,key,n=5):totals=group.groupby(key)['contb_receipt_amt'].sum()return(totals.sort_values(ascending=False)[:n])
grouped.apply(get_top_amounts,'contbr_occupation',n=7)

cand_nm        contbr_occupation
Obama, Barack  RETIRED              25270507.23ATTORNEY             11126932.97NOT PROVIDED          4849801.96HOMEMAKER             4243394.30PHYSICIAN             3732387.44LAWYER                3159391.87CONSULTANT            2459812.71
Romney, Mitt   RETIRED              11266949.23NOT PROVIDED         11173374.84HOMEMAKER             8037250.86ATTORNEY              5302578.82PRESIDENT             2403439.77CEO                   2247242.03EXECUTIVE             2230653.79
Name: contb_receipt_amt, dtype: float64

grouped.apply(get_top_amounts,'contbr_employer',n=10)

cand_nm        contbr_employer   
Obama, Barack  RETIRED               22665902.20SELF-EMPLOYED         18584277.16NOT EMPLOYED           8584118.70NOT PROVIDED           5036178.37HOMEMAKER              2599987.04STUDENT                 318831.45VOLUNTEER               257104.00MICROSOFT               215585.36SIDLEY AUSTIN LLP       168254.00REFUSED                 149516.07
Romney, Mitt   NOT PROVIDED          11827237.12RETIRED               11264701.35HOMEMAKER              8037000.86SELF-EMPLOYED          7265136.53STUDENT                 488642.82CREDIT SUISSE           265650.00MORGAN STANLEY          262266.00GOLDMAN SACH & CO.      233250.00BARCLAYS CAPITAL        162750.00H.I.G. CAPITAL          139500.00
Name: contb_receipt_amt, dtype: float64

#对出资额分组
bins=np.array([0,1,10,100,1000,10000,100000,1000000,10000000])

labels=pd.cut(fec_mrbo.contb_receipt_amt,bins)

labels

411         (10, 100]
412       (100, 1000]
413       (100, 1000]
414         (10, 100]
415         (10, 100]...     
701381      (10, 100]
701382    (100, 1000]
701383        (1, 10]
701384      (10, 100]
701385    (100, 1000]
Name: contb_receipt_amt, Length: 700975, dtype: category
Categories (8, interval[int64]): [(0, 1] < (1, 10] < (10, 100] < (100, 1000] < (1000, 10000] < (10000, 100000] < (100000, 1000000] < (1000000, 10000000]]

grouped=fec_mrbo.groupby(['cand_nm',labels])

grouped.size()

cand_nm        contb_receipt_amt  
Obama, Barack  (0, 1]                    493(1, 10]                 40070(10, 100]              372280(100, 1000]            153991(1000, 10000]           22284(10000, 100000]             2(100000, 1000000]           3(1000000, 10000000]         4
Romney, Mitt   (0, 1]                     77(1, 10]                  3681(10, 100]               31853(100, 1000]             43357(1000, 10000]           26186(10000, 100000]             1(100000, 1000000]           0(1000000, 10000000]         0
dtype: int64

grouped.size().unstack(0)
cand_nmObama, BarackRomney, Mitt
contb_receipt_amt
(0, 1]49377
(1, 10]400703681
(10, 100]37228031853
(100, 1000]15399143357
(1000, 10000]2228426186
(10000, 100000]21
(100000, 1000000]30
(1000000, 10000000]40
bucket_sums=grouped.contb_receipt_amt.sum()#对出资额求和

bucket_sums

cand_nm        contb_receipt_amt  
Obama, Barack  (0, 1]                      318.24(1, 10]                  337267.62(10, 100]              20288981.41(100, 1000]            54798531.46(1000, 10000]          51753705.67(10000, 100000]           59100.00(100000, 1000000]       1490683.08(1000000, 10000000]     7148839.76
Romney, Mitt   (0, 1]                       77.00(1, 10]                   29819.66(10, 100]               1987783.76(100, 1000]            22363381.69(1000, 10000]          63942145.42(10000, 100000]           12700.00(100000, 1000000]              NaN(1000000, 10000000]            NaN
Name: contb_receipt_amt, dtype: float64

bucket_sums=bucket_sums.unstack(0)

bucket_sums
cand_nmObama, BarackRomney, Mitt
contb_receipt_amt
(0, 1]318.2477.00
(1, 10]337267.6229819.66
(10, 100]20288981.411987783.76
(100, 1000]54798531.4622363381.69
(1000, 10000]51753705.6763942145.42
(10000, 100000]59100.0012700.00
(100000, 1000000]1490683.08NaN
(1000000, 10000000]7148839.76NaN
normed_sums=bucket_sums.div(bucket_sums.sum(axis=1),axis=0)#在面元内进行规格化

normed_sums
cand_nmObama, BarackRomney, Mitt
contb_receipt_amt
(0, 1]0.8051820.194818
(1, 10]0.9187670.081233
(10, 100]0.9107690.089231
(100, 1000]0.7101760.289824
(1000, 10000]0.4473260.552674
(10000, 100000]0.8231200.176880
(100000, 1000000]1.000000NaN
(1000000, 10000000]1.000000NaN
normed_sums[:-2].plot(kind='barh',stacked=True)

<matplotlib.axes._subplots.AxesSubplot at 0x1700ecfd988>

在这里插入图片描述

#根据州统计赞助信息
grouped=fec_mrbo.groupby(['cand_nm','contbr_st'])

totals=grouped.contb_receipt_amt.sum().unstack(0).fillna(0)

totals=totals[totals.sum(1)>100000]

totals[:10]
cand_nmObama, BarackRomney, Mitt
contbr_st
AK275353.1586204.24
AL537835.48504882.08
AR324802.28105351.50
AZ1484241.841850484.23
CA23370680.8410908232.46
CO2104551.381477810.82
CT2037216.663377421.85
DC4317865.85999740.50
DE325394.1481404.00
FL7138932.528008067.40
percent=totals.div(totals.sum(1),axis=0)

percent
cand_nmObama, BarackRomney, Mitt
contbr_st
AK0.7615750.238425
AL0.5158020.484198
AR0.7550840.244916
AZ0.4450870.554913
CA0.6817800.318220
CO0.5874760.412524
CT0.3762420.623758
DC0.8119940.188006
DE0.7998910.200109
FL0.4713100.528690
GA0.5874900.412510
HI0.8763340.123666
IA0.7413760.258624
ID0.2004240.799576
IL0.8276490.172351
IN0.6225490.377451
KS0.5751670.424833
KY0.5165020.483498
LA0.3578430.642157
MA0.5912560.408744
MD0.7506310.249369
ME0.9105300.089470
MI0.5227050.477295
MN0.8552630.144737
MO0.4951270.504873
MS0.3745930.625407
MT0.6530630.346937
NC0.6558790.344121
NE0.5874310.412569
NH0.5862260.413774
NJ0.4906870.509313
NM0.8441330.155867
NV0.5403620.459638
NY0.5953930.404607
OH0.4894120.510588
OK0.4135490.586451
OR0.6966950.303305
PA0.6618560.338144
PR0.8897720.110228
RI0.7078510.292149
SC0.6105310.389469
SD0.3459480.654052
TN0.4248300.575170
TX0.5185050.481495
UT0.1229480.877052
VA0.5554020.444598
VT0.9461760.053824
WA0.7656340.234366
WI0.8053430.194657
WV0.5738910.426109
WY0.4408230.559177
XX0.0000001.000000

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《Oracle数据库使用listagg去重删除重复数据的方法汇总》文章介绍了在Oracle数据库中使用LISTAGG和XMLAGG函数进行字符串聚合并去重的方法,包括去重聚合、使用XML解析和CLO... 目录案例表第一种：使用wm_concat() + distinct去重聚合第二种：使用listagg，
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Redis主从/哨兵机制原理分析

Redis主从/哨兵机制原理分析

《Redis主从/哨兵机制原理分析》本文介绍了Redis的主从复制和哨兵机制,主从复制实现了数据的热备份和负载均衡,而哨兵机制可以监控Redis集群,实现自动故障转移,哨兵机制通过监控、下线、选举和故... 目录一、主从复制1.1 什么是主从复制1.2 主从复制的作用1.3 主从复制原理1.3.1 全量复制
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Redis主从复制的原理分析

Redis主从复制的原理分析

《Redis主从复制的原理分析》Redis主从复制通过将数据镜像到多个从节点,实现高可用性和扩展性,主从复制包括初次全量同步和增量同步两个阶段,为优化复制性能,可以采用AOF持久化、调整复制超时时间、... 目录Redis主从复制的原理主从复制概述配置主从复制数据同步过程复制一致性与延迟故障转移机制监控与维
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Java读取InfluxDB数据库的方法详解

Java读取InfluxDB数据库的方法详解

《Java读取InfluxDB数据库的方法详解》本文介绍基于Java语言,读取InfluxDB数据库的方法,包括读取InfluxDB的所有数据库,以及指定数据库中的measurement、field、... 首先，创建一个Java项目，用于撰写代码。接下来，配置所需要的依赖；这里我们就选择可用于与Infl
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Redis连接失败：客户端IP不在白名单中的问题分析与解决方案

Redis连接失败：客户端IP不在白名单中的问题分析与解决方案

《Redis连接失败：客户端IP不在白名单中的问题分析与解决方案》在现代分布式系统中,Redis作为一种高性能的内存数据库,被广泛应用于缓存、消息队列、会话存储等场景,然而,在实际使用过程中,我们可能... 目录一、问题背景二、错误分析1. 错误信息解读2. 根本原因三、解决方案1. 将客户端IP添加到Re
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详谈redis跟数据库的数据同步问题

详谈redis跟数据库的数据同步问题

《详谈redis跟数据库的数据同步问题》文章讨论了在Redis和数据库数据一致性问题上的解决方案,主要比较了先更新Redis缓存再更新数据库和先更新数据库再更新Redis缓存两种方案,文章指出,删除R... 目录一、Redis 数据库数据一致性的解决方案1.1、更新Redis缓存、删除Redis缓存的区别二
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oracle数据库索引失效的问题及解决

oracle数据库索引失效的问题及解决

《oracle数据库索引失效的问题及解决》本文总结了在Oracle数据库中索引失效的一些常见场景,包括使用isnull、isnotnull、!=、、、函数处理、like前置%查询以及范围索引和等值索引... 目录oracle数据库索引失效问题场景环境索引失效情况及验证结论一结论二结论三结论四结论五总结ora
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C#实现文件读写到SQLite数据库

C#实现文件读写到SQLite数据库

《C#实现文件读写到SQLite数据库》这篇文章主要为大家详细介绍了使用C#将文件读写到SQLite数据库的几种方法,文中的示例代码讲解详细,感兴趣的小伙伴可以参考一下... 目录1. 使用 BLOB 存储文件2. 存储文件路径3. 分块存储文件《文件读写到SQLite数据库China编程的方法》博客中，介绍了文
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Redis主从复制实现原理分析

Redis主从复制实现原理分析

《Redis主从复制实现原理分析》Redis主从复制通过Sync和CommandPropagate阶段实现数据同步,2.8版本后引入Psync指令,根据复制偏移量进行全量或部分同步,优化了数据传输效率... 目录Redis主DodMIK从复制实现原理实现原理Psync: 2.8版本后总结Redis主从复制实
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