仅在 required/specific 时间间隔时将收集的 RAW 数据插入 Influxdb
Insert Collectd RAW Data into Influxdb only when required/specific time interval
我们在尝试将数据插入 influxdb 以从 grafana 中获取时遇到问题。截至目前,正在进行一项测试,我们直接从一台主机的 collectd 插入到 influxdb 数据库,问题是这变得非常庞大,这只是来自一台主机的信息,我们的 PROD 中有数百台主机环境是我们的主要目标。
我们正在尝试做的是在需要调查中断时仅插入数据,因为我们计划从 collectd 现在正在创建的 RAW 文件中执行此操作。我们遇到了一个解决方案,但对于 NMON,它确实满足了我们的需求,但对于 NMON 文件,但老实说,我对 Golang 一无所知,这是他们使用的语言,或者至少我是这样认为的。
我在这个社区发现有一个 PARSER 插件是 telegraf 的一部分,但我找不到的是如何使用它从 collectd 提供的 RAW 文件中获取信息并将其插入到涌入数据库。
如果有人能就此事给我建议,我将不胜感激。
提前致谢。
输出样本:
################################################################################
# Collectl: V3.6.5-2 HiRes: 1 Options: -D
# Host: localhost DaemonOpts: -f /var/log/collectl -r00:00,7,60 -m -F60 -s+YZ -i60
# Booted: 1548722801.34 [20190128-18:46:41]
# Distro: Red Hat Enterprise Linux Server release 6.9 (Santiago) Platform: VMware Virtual Platform
# Date: 20190130-230000 Secs: 1548910800 TZ: -0600
# SubSys: bcdfijmnstYZ Options: Interval: 60:60 NumCPUs: 10 NumBud: 3 Flags: ix
# Filters: NfsFilt: EnvFilt: TcpFilt: ituc
# HZ: 100 Arch: x86_64-linux-thread-multi PageSize: 4096
# Cpu: GenuineIntel Speed(MHz): 2992.968 Cores: 1 Siblings: 1 Nodes: 1
# Kernel: 2.6.32-696.20.1.el6.x86_64 Memory: 198339428 kB Swap: 17825788 kB
# NumDisks: 22 DiskNames: sda sdb sdc sde sdd dm-0 dm-1 dm-2 dm-3 dm-4 dm-5 dm-6 dm-7 dm-8 dm-9 dm-10 dm-11 dm-12 dm-13 dm-14 dm-15 dm-16
# NumNets: 3 NetNames: lo:?? eth0:10000 eth1:10000
# NumSlabs: 201 Version: 2.1
# SCSI: CD:1:00:00:00 DA:2:00:00:00 DA:2:00:01:00 DA:2:00:02:00 DA:2:00:03:00 DA:2:00:04:00
################################################################################
>>> 1548910800.001 <<<
buddy Node 0, zone DMA 1 1 1 3 3 2 1 1 0 0 2
buddy Node 0, zone DMA32 14 18 15 17 14 7 7 11 8 4 106
buddy Node 0, zone Normal 403 386 5583 3572 13604 10598 7080 3627 2055 2 27
cpu 22087348 256 7084173 153333789 15626 4857 2989099 0 0
cpu0 2343703 23 756440 15044112 1503 394 389489 0 0
cpu1 1424427 40 403584 16804532 1978 2 13682 0 0
cpu2 1400317 30 399191 16835804 1824 1 12608 0 0
cpu3 2373492 20 777245 15024216 1405 441 357515 0 0
cpu4 2305879 21 754504 15129573 1544 395 334811 0 0
cpu5 2406073 20 826459 14953667 1377 1854 337925 0 0
cpu6 2524873 19 813584 14794741 1330 456 388679 0 0
cpu7 2377199 26 774277 14997185 1586 440 369269 0 0
cpu8 2431979 19 818481 14856251 1454 501 415840 0 0
cpu9 2499402 33 760403 14893703 1620 370 369277 0 0
intr 5904658682
ctxt 4292174746
processes 1824586
procs_running 3
procs_blocked 0
int 0: 844 0 0 0 0 0 0 0 0 0 IO-APIC-edge timer
int 1: 10 1146 0 0 0 0 0 0 0 0 IO-APIC-edge i8042
int 8: 1 0 0 0 0 0 0 0 0 0 IO-APIC-edge rtc0
int 9: 0 0 0 0 0 0 0 0 0 0 IO-APIC-fasteoi acpi
int 12: 110 0 0 1080 0 0 0 0 0 0 IO-APIC-edge i8042
int 14: 0 0 0 0 0 0 0 0 0 0 IO-APIC-edge ata_piix
int 15: 95 0 0 0 0 639862 0 0 0 0 IO-APIC-edge ata_piix
int 16: 4 0 0 0 0 0 0 0 0 2 IO-APIC-fasteoi vmwgfx
int 24: 0 0 0 0 0 0 0 0 0 0 PCI-MSI-edge pciehp
int 25: 0 0 0 0 0 0 0 0 0 0 PCI-MSI-edge pciehp
int 26: 0 0 0 0 0 0 0 0 0 0 PCI-MSI-edge pciehp
int 27: 0 0 0 0 0 0 0 0 0 0 PCI-MSI-edge pciehp
int 28: 0 0 0 0 0 0 0 0 0 0 PCI-MSI-edge pciehp
int 29: 0 0 0 0 0 0 0 0 0 0 PCI-MSI-edge pciehp
int 30: 0 0 0 0 0 0 0 0 0 0 PCI-MSI-edge pciehp
int 31: 0 0 0 0 0 0 0 0 0 0 PCI-MSI-edge pciehp
int 32: 0 0 0 0 0 0 0 0 0 0 PCI-MSI-edge pciehp
int 33: 0 0 0 0 0 0 0 0 0 0 PCI-MSI-edge pciehp
int 34: 0 0 0 0 0 0 0 0 0 0 PCI-MSI-edge pciehp
int 35: 0 0 0 0 0 0 0 0 0 0 PCI-MSI-edge pciehp
int 36: 0 0 0 0 0 0 0 0 0 0 PCI-MSI-edge pciehp
int 37: 0 0 0 0 0 0 0 0 0 0 PCI-MSI-edge pciehp
int 38: 0 0 0 0 0 0 0 0 0 0 PCI-MSI-edge pciehp
int 39: 0 0 0 0 0 0 0 0 0 0 PCI-MSI-edge pciehp
int 40: 0 0 0 0 0 0 0 0 0 0 PCI-MSI-edge pciehp
disk 253 3 dm-3 817 0 6530 1019 1 0 8 6 0 187 1025
disk 253 4 dm-4 261 0 3298 154 78648 0 629184 94613 0 12780 94788
disk 253 5 dm-5 122 0 970 30 1 0 8 0 0 30 30
disk 253 6 dm-6 128 0 1018 49 1 0 8 6 0 55 55
disk 253 7 dm-7 122 0 970 43 1 0 8 0 0 43 43
disk 253 8 dm-8 14705 0 173314 18512 30827989 0 246623912 309099632 0 580567 309838167
disk 253 9 dm-9 257 0 2050 167 1 0 8 0 0 80 167
disk 253 10 dm-10 142 0 1130 58 112 0 896 81 0 107 139
disk 253 11 dm-11 26758 0 1183658 26638 9156 0 73248 27014 0 15012 53652
disk 253 12 dm-12 133 0 1058 59 6 0 48 2 0 49 61
disk 253 13 dm-13 133 0 1058 56 6 0 48 2 0 46 58
disk 253 14 dm-14 5088 0 615178 3932 107319 0 858552 1285013 0 3111 1288946
disk 253 15 dm-15 15240 0 410298 10749 1538387 0 12307096 7551698 0 121712 7567479
disk 253 16 dm-16 544 0 20778 753 989864 0 7918912 2153755 0 479168 2155269
load 0.21 0.16 0.10 1/2757 24089
tcp-Ip: Forwarding DefaultTTL InReceives InHdrErrors InAddrErrors ForwDatagrams InUnknownProtos InDiscards InDelivers OutRequests OutDiscards OutNoRoutes ReasmTimeout ReasmReqds ReasmOKs ReasmFails FragOKs FragFails FragCreates
tcp-Ip: 2 64 2345808100 0 65987 0 0 0 2345023989 2542097859 0 88 0 1374787 657103 0 658849 0 1378045
tcp-Icmp: InMsgs InErrors InDestUnreachs InTimeExcds InParmProbs InSrcQuenchs InRedirects InEchos InEchoReps InTimestamps InTimestampReps InAddrMasks InAddrMaskReps OutMsgs OutErrors OutDestUnreachs OutTimeExcds OutParmProbs OutSrcQuenchs OutRedirects OutEchos OutEchoReps OutTimestamps OutTimestampReps OutAddrMasks OutAddrMaskReps
tcp-Icmp: 9644 24 1453 0 0 0 0 4223 3966 2 0 0 0 10125 0 1934 0 0 0 0 3966 4223 0 2 0 0
tcp-Tcp: RtoAlgorithm RtoMin RtoMax MaxConn ActiveOpens PassiveOpens AttemptFails EstabResets CurrEstab InSegs OutSegs RetransSegs InErrs OutRsts
tcp-Tcp: 1 200 120000 -1 189518 203747 116 1705 1538 2337716464 2532808789 6750 1 1108
tcp-Udp: InDatagrams NoPorts InErrors OutDatagrams RcvbufErrors SndbufErrors
tcp-Udp: 7155937 2287 12361 9272237 1 0
fs-ds 279615 266755 45 0 0 0
fs-is 121177 118
fs-fnr 9728 0 6815744
nfsc-net 0 0 0 0
nfsc-rpc 499842 1085 499886
nfsc-proc2 18 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
nfsc-proc3 22 0 105381 0 2333 383140 6 1704 0 0 0 0 0 0 0 0 0 0 44 7201 20 10 0
Telegraf's docs
显示如何读取 collectd 文件的示例,
您使用文件输入并使用 data_format = "collectd"
来使用 collectd 解析器。
旁注,InfluxDB can accept CollectD directly. (this may require this file 我的安装中似乎没有)
有关速率限制,请查看 collectd 的 interval, or if you prefer using telegraf, it also has such an option。
我不确定 influxdb 对此的选择,但他们的 downsampling and retention 功能可能对您感兴趣。
我们在尝试将数据插入 influxdb 以从 grafana 中获取时遇到问题。截至目前,正在进行一项测试,我们直接从一台主机的 collectd 插入到 influxdb 数据库,问题是这变得非常庞大,这只是来自一台主机的信息,我们的 PROD 中有数百台主机环境是我们的主要目标。
我们正在尝试做的是在需要调查中断时仅插入数据,因为我们计划从 collectd 现在正在创建的 RAW 文件中执行此操作。我们遇到了一个解决方案,但对于 NMON,它确实满足了我们的需求,但对于 NMON 文件,但老实说,我对 Golang 一无所知,这是他们使用的语言,或者至少我是这样认为的。
我在这个社区发现有一个 PARSER 插件是 telegraf 的一部分,但我找不到的是如何使用它从 collectd 提供的 RAW 文件中获取信息并将其插入到涌入数据库。
如果有人能就此事给我建议,我将不胜感激。
提前致谢。
输出样本:
################################################################################
# Collectl: V3.6.5-2 HiRes: 1 Options: -D
# Host: localhost DaemonOpts: -f /var/log/collectl -r00:00,7,60 -m -F60 -s+YZ -i60
# Booted: 1548722801.34 [20190128-18:46:41]
# Distro: Red Hat Enterprise Linux Server release 6.9 (Santiago) Platform: VMware Virtual Platform
# Date: 20190130-230000 Secs: 1548910800 TZ: -0600
# SubSys: bcdfijmnstYZ Options: Interval: 60:60 NumCPUs: 10 NumBud: 3 Flags: ix
# Filters: NfsFilt: EnvFilt: TcpFilt: ituc
# HZ: 100 Arch: x86_64-linux-thread-multi PageSize: 4096
# Cpu: GenuineIntel Speed(MHz): 2992.968 Cores: 1 Siblings: 1 Nodes: 1
# Kernel: 2.6.32-696.20.1.el6.x86_64 Memory: 198339428 kB Swap: 17825788 kB
# NumDisks: 22 DiskNames: sda sdb sdc sde sdd dm-0 dm-1 dm-2 dm-3 dm-4 dm-5 dm-6 dm-7 dm-8 dm-9 dm-10 dm-11 dm-12 dm-13 dm-14 dm-15 dm-16
# NumNets: 3 NetNames: lo:?? eth0:10000 eth1:10000
# NumSlabs: 201 Version: 2.1
# SCSI: CD:1:00:00:00 DA:2:00:00:00 DA:2:00:01:00 DA:2:00:02:00 DA:2:00:03:00 DA:2:00:04:00
################################################################################
>>> 1548910800.001 <<<
buddy Node 0, zone DMA 1 1 1 3 3 2 1 1 0 0 2
buddy Node 0, zone DMA32 14 18 15 17 14 7 7 11 8 4 106
buddy Node 0, zone Normal 403 386 5583 3572 13604 10598 7080 3627 2055 2 27
cpu 22087348 256 7084173 153333789 15626 4857 2989099 0 0
cpu0 2343703 23 756440 15044112 1503 394 389489 0 0
cpu1 1424427 40 403584 16804532 1978 2 13682 0 0
cpu2 1400317 30 399191 16835804 1824 1 12608 0 0
cpu3 2373492 20 777245 15024216 1405 441 357515 0 0
cpu4 2305879 21 754504 15129573 1544 395 334811 0 0
cpu5 2406073 20 826459 14953667 1377 1854 337925 0 0
cpu6 2524873 19 813584 14794741 1330 456 388679 0 0
cpu7 2377199 26 774277 14997185 1586 440 369269 0 0
cpu8 2431979 19 818481 14856251 1454 501 415840 0 0
cpu9 2499402 33 760403 14893703 1620 370 369277 0 0
intr 5904658682
ctxt 4292174746
processes 1824586
procs_running 3
procs_blocked 0
int 0: 844 0 0 0 0 0 0 0 0 0 IO-APIC-edge timer
int 1: 10 1146 0 0 0 0 0 0 0 0 IO-APIC-edge i8042
int 8: 1 0 0 0 0 0 0 0 0 0 IO-APIC-edge rtc0
int 9: 0 0 0 0 0 0 0 0 0 0 IO-APIC-fasteoi acpi
int 12: 110 0 0 1080 0 0 0 0 0 0 IO-APIC-edge i8042
int 14: 0 0 0 0 0 0 0 0 0 0 IO-APIC-edge ata_piix
int 15: 95 0 0 0 0 639862 0 0 0 0 IO-APIC-edge ata_piix
int 16: 4 0 0 0 0 0 0 0 0 2 IO-APIC-fasteoi vmwgfx
int 24: 0 0 0 0 0 0 0 0 0 0 PCI-MSI-edge pciehp
int 25: 0 0 0 0 0 0 0 0 0 0 PCI-MSI-edge pciehp
int 26: 0 0 0 0 0 0 0 0 0 0 PCI-MSI-edge pciehp
int 27: 0 0 0 0 0 0 0 0 0 0 PCI-MSI-edge pciehp
int 28: 0 0 0 0 0 0 0 0 0 0 PCI-MSI-edge pciehp
int 29: 0 0 0 0 0 0 0 0 0 0 PCI-MSI-edge pciehp
int 30: 0 0 0 0 0 0 0 0 0 0 PCI-MSI-edge pciehp
int 31: 0 0 0 0 0 0 0 0 0 0 PCI-MSI-edge pciehp
int 32: 0 0 0 0 0 0 0 0 0 0 PCI-MSI-edge pciehp
int 33: 0 0 0 0 0 0 0 0 0 0 PCI-MSI-edge pciehp
int 34: 0 0 0 0 0 0 0 0 0 0 PCI-MSI-edge pciehp
int 35: 0 0 0 0 0 0 0 0 0 0 PCI-MSI-edge pciehp
int 36: 0 0 0 0 0 0 0 0 0 0 PCI-MSI-edge pciehp
int 37: 0 0 0 0 0 0 0 0 0 0 PCI-MSI-edge pciehp
int 38: 0 0 0 0 0 0 0 0 0 0 PCI-MSI-edge pciehp
int 39: 0 0 0 0 0 0 0 0 0 0 PCI-MSI-edge pciehp
int 40: 0 0 0 0 0 0 0 0 0 0 PCI-MSI-edge pciehp
disk 253 3 dm-3 817 0 6530 1019 1 0 8 6 0 187 1025
disk 253 4 dm-4 261 0 3298 154 78648 0 629184 94613 0 12780 94788
disk 253 5 dm-5 122 0 970 30 1 0 8 0 0 30 30
disk 253 6 dm-6 128 0 1018 49 1 0 8 6 0 55 55
disk 253 7 dm-7 122 0 970 43 1 0 8 0 0 43 43
disk 253 8 dm-8 14705 0 173314 18512 30827989 0 246623912 309099632 0 580567 309838167
disk 253 9 dm-9 257 0 2050 167 1 0 8 0 0 80 167
disk 253 10 dm-10 142 0 1130 58 112 0 896 81 0 107 139
disk 253 11 dm-11 26758 0 1183658 26638 9156 0 73248 27014 0 15012 53652
disk 253 12 dm-12 133 0 1058 59 6 0 48 2 0 49 61
disk 253 13 dm-13 133 0 1058 56 6 0 48 2 0 46 58
disk 253 14 dm-14 5088 0 615178 3932 107319 0 858552 1285013 0 3111 1288946
disk 253 15 dm-15 15240 0 410298 10749 1538387 0 12307096 7551698 0 121712 7567479
disk 253 16 dm-16 544 0 20778 753 989864 0 7918912 2153755 0 479168 2155269
load 0.21 0.16 0.10 1/2757 24089
tcp-Ip: Forwarding DefaultTTL InReceives InHdrErrors InAddrErrors ForwDatagrams InUnknownProtos InDiscards InDelivers OutRequests OutDiscards OutNoRoutes ReasmTimeout ReasmReqds ReasmOKs ReasmFails FragOKs FragFails FragCreates
tcp-Ip: 2 64 2345808100 0 65987 0 0 0 2345023989 2542097859 0 88 0 1374787 657103 0 658849 0 1378045
tcp-Icmp: InMsgs InErrors InDestUnreachs InTimeExcds InParmProbs InSrcQuenchs InRedirects InEchos InEchoReps InTimestamps InTimestampReps InAddrMasks InAddrMaskReps OutMsgs OutErrors OutDestUnreachs OutTimeExcds OutParmProbs OutSrcQuenchs OutRedirects OutEchos OutEchoReps OutTimestamps OutTimestampReps OutAddrMasks OutAddrMaskReps
tcp-Icmp: 9644 24 1453 0 0 0 0 4223 3966 2 0 0 0 10125 0 1934 0 0 0 0 3966 4223 0 2 0 0
tcp-Tcp: RtoAlgorithm RtoMin RtoMax MaxConn ActiveOpens PassiveOpens AttemptFails EstabResets CurrEstab InSegs OutSegs RetransSegs InErrs OutRsts
tcp-Tcp: 1 200 120000 -1 189518 203747 116 1705 1538 2337716464 2532808789 6750 1 1108
tcp-Udp: InDatagrams NoPorts InErrors OutDatagrams RcvbufErrors SndbufErrors
tcp-Udp: 7155937 2287 12361 9272237 1 0
fs-ds 279615 266755 45 0 0 0
fs-is 121177 118
fs-fnr 9728 0 6815744
nfsc-net 0 0 0 0
nfsc-rpc 499842 1085 499886
nfsc-proc2 18 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
nfsc-proc3 22 0 105381 0 2333 383140 6 1704 0 0 0 0 0 0 0 0 0 0 44 7201 20 10 0
Telegraf's docs
显示如何读取 collectd 文件的示例,
您使用文件输入并使用 data_format = "collectd"
来使用 collectd 解析器。
旁注,InfluxDB can accept CollectD directly. (this may require this file 我的安装中似乎没有)
有关速率限制,请查看 collectd 的 interval, or if you prefer using telegraf, it also has such an option。
我不确定 influxdb 对此的选择,但他们的 downsampling and retention 功能可能对您感兴趣。