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Axib ibase Tim ime Series Database Axib ibase Tim ime Series - - PowerPoint PPT Presentation

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Axib ibase Tim ime Series Database Axib ibase Tim ime Series Database Axibase Time-Series Database (ATSD) is a clustered non-relational database for the storage of various information coming out of the IT infrastructure. ATSD is specifically

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SLIDE 1

Axib ibase Tim ime Series Database

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SLIDE 2

Axib ibase Tim ime Series Database

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Axibase Time-Series Database (ATSD) is a clustered non-relational database for the storage of various information coming out of the IT infrastructure. ATSD is specifically designed to store and analyze large amounts of statistical data collected at high frequency.

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SLIDE 3

Database His istory ry

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  • 1970 – IBM introduced relational algebra for data processing.
  • Cambrian explosion of relational database management systems:
  • 2000 – first large-scale applications emerge, such as Google Search.
  • 2004 – Google Big Table – first non-relational database using distributed file system.
  • Currently we are experiencing Cambrian explosion of non-relational (a.k.a. NoSQL) databases:
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SLIDE 4

Key Dif ifferences Between SQL and NoSQL

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SQL NoSQL High-level Programming Language SQL Transactions Query Optimizer Non-key indexes

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SLIDE 5

Key Dif ifferences Between SQL and NoSQL

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SQL NoSQL Scalability TB PB Maximum Cluster Size 48 (Oracle RAC) 1000+ Distributed Read Time Depends on table size and indexes Linear Write Time Depends on table size and indexes Linear Table Schema (column names, data types) Predetermined Raw bytes. Schema determined by application

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SLIDE 6

How Proven Is NoSQL Technology

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NoSQL is the leading technology behind big data applications.

  • Google – search, gmail, AppEngine
  • Yahoo/Microsoft – search
  • Amazon – e-commerce, search, cloud computing (AWS DynamoDB)
  • IBM Big Insights, Microsoft Azure HD Insight
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SLIDE 7

Big ig Data Adoption

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HBase behind Facebook Messages:

  • 6+ billion messages per day
  • 75+ billion R/W operations per day
  • Peak throughput: 1.5 million R/W operations per second
  • 2+ petabytes of data (6+ PB including replicas) with data growth of over 8 TB per day
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SLIDE 8

Big ig Data Adoption

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IBM BigInsights behind Vestas:

  • A wind energy company in Denmark is reducing the time to analyze petabytes of data from

several weeks to 15 minutes to improve the accuracy of wind turbine placement.

  • Stores 2.8 PB of company historical data together with over 178 external parameters:

temperature, barometric pressure, humidity, precipitation, wind direction, wind velocity etc.

  • Stores precise data on weather over the past 11 years.
  • Collects data from over 35,000 meteorological stations.
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SLIDE 9

Big ig Data Adoption

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HBase behind Explorys:

  • Explorys uses HBase to enable search and analysis of patient populations, treatment protocols,

and clinical outcomes.

  • Stores over 275 billion clinical, financial and operational data elements.
  • 48 million unique patient files.
  • Collecting data from over 340 hospitals and 300,000 healthcare providers.
  • Pull data from 22 integrated major healthcare systems.
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Axib ibase Tim ime Series Database

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Scalability & Speed

  • Collects billions of samples per day. Retains detailed data forever.

Features

  • Combines database, rule engine, and visualization in one product.

Analytical Rule Engine

  • Applies aggregate functions and filters on streaming data.

Integration

  • Accepts data from any source based on industry-standard protocols.

Visualization

  • Built-in portals with smart widgets.
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SLIDE 11

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Big ig Data for IT IT Monitoring

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  • Retain detailed data forever.
  • Collect statistics at high-frequency, for example every 15 seconds.
  • Consolidate performance statistics from all systems into one database: facilities, network,

storage, servers, applications, databases, transactions, service providers, user activity etc.

  • Monitor infrastructure based on abnormal deviations instead of manual thresholds.
  • Apply statistical formulas to predict outages.
  • Take advantage of schema-less database to collect data from any source.
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SLIDE 13

Big ig Data for Developers

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  • Support for annotation-style instrumentation.
  • Alternative to byte-code instrumentation and

file logging.

  • Collect detailed performance and usage

statistics for reporting and analytics, without writing custom monitors.

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SLIDE 14

Big ig Data for Operations

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  • Gather and analyze statistical data generated by the various systems and sensors.
  • Analytics that can support decision control systems.
  • Allows for better real‐time operations decision‐support.
  • Generate accurate forecasts of upcoming issues:
  • Delays
  • Scheduled maintenance based on product usage and sensor data instead of warranty

periods

  • Improved customer service times and standards.
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SLIDE 15

ATSD Archit itecture

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  • ATSD architecture combines database,

analytics and reporting tools into one complete product.

  • Data locality makes analytics run faster.
  • Application server layer is simplified to

provide core shared services

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SLIDE 16

ATSD Components

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  • Pluggable driver provides support for

different storage engines

  • Compute, persistence and data

collection layers scaled independently

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SLIDE 17

Fault Tole lerance

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  • ATSD is a distributed system,

with high fault tolerance.

  • Each data sample is

automatically replicated 3 times for recovery.

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SLIDE 18

ATSD Scala labil ility

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  • ATSD is a distributed, non-relational database with high throughput, fault tolerance and reading

speed.

  • ATSD can collect billions of metrics per day and store petabytes of data.
  • ATSD supports millisecond resolution and sampling intervals of up to several measurements per
  • second. The data is stored without losing accuracy.
  • Additional nodes can be added at runtime to handle increasing volumes. ATSD automatically

distributes the table across active nodes.

  • New nodes can be added in remote data centers to minimize network traffic.
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SLIDE 19

Supported Data Types

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  • Two types of data ingestion: push and pull.
  • ATSD supports numeric values, log messages and properties (collection of key-values).
  • ATSD uses collectors for retrieving structured and unstructured data from remote sources.
  • Support for standard protocols: Telnet, ICMP, CSV/TSV, FILE, JMX, HTTP, and JSON.
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Data Coll llection

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  • Collection is agentless; data is pushed by external systems into ATSD.
  • New metrics are auto-registered. No need to update schema or restart any server components.
  • Existing monitoring tools can be instrumented to stream data into ATSD.
  • Each data sample can be tagged (key = value) at source for subsequent querying, aggregations,

and roll-ups.

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SLIDE 21

Data Storage

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  • Built-in data compression provides 70%-80% disk space savings over raw data.
  • No data needs to be deleted. Seek time is almost linear regardless of the dataset size.
  • Data storage is sparse and efficient. ATSD stores only what is collected instead of long rows with

NULLs or zeros, as is the case in relational model.

  • VMware VMFS-attached disks are sufficient for small to medium clusters.
  • Direct attached disks with JBOD are recommended for larger clusters.
  • JBOD alternatives to minimize node recovery time are available from leading storage vendors,

such as NetApp E-Series.

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SLIDE 22

Built-in In Instruments

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Unlike conventional data warehouses, ATSD comes with a set of built-in tools for data analysis:

  • Analytical Rule Engine
  • Forecasting
  • Visualization
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SLIDE 23

Analyt ytic ical Rule le Engine

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  • Evaluates incoming data in memory based on statistical rules.
  • Statistical rules are applied to the incoming data stream before data is

stored on disk.

  • As data is ingested by ATSD server, a subset of samples that match rule

queries are routed to the rule engine for processing.

  • Rule Engine supports both time- and count- based data windows.
  • Rule expressions and filters can reference not just numeric values but also

tags such as system type, location, priority to ensure that alerts are raised

  • nly for critical issues.
  • Multiple metrics and entities can be correlated within the same rule.
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SLIDE 24

Analyt ytic ical Rule le Engine – Rule Examples

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Type Window Example Description

threshold none value > 75 Raise an alert if last metric value exceeds threshold range none value > 50 AND value <= 75 Raise an alert if value is outside of specified range statistical-count count(10) avg(value) > 75 Raise an alert if average value of the last 10 samples exceeds threshold statistical-time time('15 min') avg(value) > 75 Raise an alert if average value for the last 15 minutes exceeds threshold statistical-deviation time('15 min') avg(value) / avg(value(time: '1 hour')) > 1.25 Raise an alert if 15-minute average exceeds 1-hour average by more than 25% statistical-ungrouped time('15 min') avg(value) > 75 Raise an alert if 15-minute average values for all entities in the group exceeds threshold metric correlation time('15 min') avg(value) > 75 AND avg(value(metric: 'loadavg.1m')) > 0.5 Raise an alert if average values for two separate metrics for the last 15 minutes exceed predefined thresholds entity correlation time('15 min') avg(value) > 75 AND avg(value(entity: 'host2')) > 75 Raise an alert if average values for two entities for the last 15 minutes exceed thresholds threshold override time('15 min') avg(value) >= entity.groupTag('cpu _avg').min() Raise an alert if 15-minute average value exceeds minimum threshold specified for groups to which the entity belongs cpu forecast deviation time('5 min') abs(forecast_deviation(wavg())) > 2 Raise an alert if 5-minute average deviates from forecast by more than two standard deviations cpu forecast diff time('10 min') abs(wavg() - forecast()) > 25 Raise alert if absolute forecast deviates from average by more than specified value disk threshold time('15 min') new_maximum() && threshold_linear_time(99) < 120 Raise alert if last value is the highest observed and linear threshold is expected to violate the 99% threshold in less than 120 minutes

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SLIDE 25

Analyt ytic ical Rule le Engine

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Analyt ytic ical Rule le Engine

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SLIDE 27

Forecasting

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  • Customers have a growing need to predict problems before they occur. The accuracy of

predictions and the percentage of false positives/negatives highly depends on the frequency of data collection, the retention interval, and algorithms.

  • The use of built-in autoregressive time-series extrapolation algorithms (Holt-Winters, ARIMA,

etc.) in ATSD allows predicting of system failures at early stages.

  • The forecasting process is resource intensive and is most effective in a clustered system with

data locality such as ATSD.

  • Dynamic predictions eliminate the need to set manual thresholds.
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SLIDE 28

Forecasting Example

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SLIDE 29

Forecasting Example

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Forecast Settings

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  • ATSD selects the most accurate

forecasting algorithm for each time-series separately based on a ranking system.

  • The winning algorithm is used to

compute forecast for the next day, week or month.

  • Pre-computed forecasts can be

used in rule engine.

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SLIDE 31

Forecast Settings

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SLIDE 32

Vis isualization

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  • ATSD can be integrated with Axibase Enterprise Reporting using the ATSD adapter
  • ATSD comes with a wide variety of widgets for creating interactive portals directly in ATSD.
  • ATSD widgets are designed from the ground-up to handle large data sets and calculations on the

client.

  • ATSD visualization is supported on mobile devices and Smart TVs.
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SLIDE 33

Vis isualization

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SLIDE 34

Search

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  • Implemented in ATSD is log file search system to detect problems in distributed systems for the

purposes of security, audit and change control.

Notifications

  • Supports standard notification mechanisms: email, console, web service, and notification in the

environment.

  • For example, Axibase LED lighting system - the "Data Cube", which changes colors depending on

the status of IT services.

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SLIDE 35

ATSD Benefits

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  • Enables customers to extract value from data that already exists in their operational and IT

infrastructures.

  • Delivers preemptive monitoring through identification of abnormal behaviors in production

systems.

  • Eliminates most manually-defined rules from the customer’s monitoring catalog.
  • Serves as a centralized repository for historical data.
  • Directly supported by AER for Dashboards, Reports, Capacity Planning
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SLIDE 36

System Requirements

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  • Operating Systems:
  • Red Hat Enterprise Linux 5.6+
  • Ubuntu 12.04+
  • Suse Linux Enterprise Server 10+
  • Storage:
  • Direct attached disks (JBOD)
  • NetApp E2660 http://www.netapp.com/us/solutions/big-data/hadoop.aspx
  • Computing Hardware:

Environment < 1K Metrics/sec < 5K Metrics/sec > 5K Metric/sec ATSD Nodes 1 1 > 5 Processors 2 vCPU, 2+ GHz 4 vCPU, 2+ GHz 4 vCPU, 2+ GHz Memory 4 GB (2GB for JVM) 16 GB (8GB for JVM) 16 GB (8GB for JVM)

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SLIDE 37

Use Cases

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  • ITM long-term history extension
  • nmon reporting for AIX, Linux and Solaris
  • Minimize exceptions in monitoring catalog
  • Collect environmental data from SCADA
  • Predictive Maintenance – based on sensors
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SLIDE 38

IT ITM His istory ry Ext xtension

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  • ITM can be instrumented to write streaming data into CSV files.
  • CSV can be instantly uploaded into ATSD using inotify utility and wget.
  • Example: private history streaming in ITM
  • KHD_CSV_OUTPUT_ACTIVATE = Y
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IT ITM His istory ry Ext xtension

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  • Warehouse Proxy Agent is setup to save history data to CSV file
  • n the local machine.
  • ATSD ingests the CSV files for analytics and long-term storage.
  • ATSD converts the data using built in parsers.
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nmon Reporting

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  • Consolidate trusted statistics from UNIX systems in one database
  • Analyze nmon data with forecasting algorithms
  • Leverage AER reporting features and dashboards
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nmon Reporting

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ssh –fN –L localhost:14032:$server:8081 username@$server echo "look for file: $nmonfile" ( echo "nmon p:$parserID e:$entityID f:$filename z:`date +%Z`" && tail -n +0 -f $nmonfile ) | telnet 127.0.0.1 14032 & sleep 2 telnetpid=$(ps -ef | grep "telnet $server $port" | grep -v "grep" | sort -n | head -n 1 | awk '{print $2}') tailpid=$(ps -ef | grep "tail -n +0 -f $nmonfile" | grep -v "grep" | sort -n | head -n 1 | awk '{print $2}') while :; do if [ -d "/proc/$nmonpid" -a "$nmonpid" != "" ]; then if [ -d "/proc/$telnetpid" -a "$telnetpid" != "" -a -d "/proc/$tailpid" -a "$tailpid" != "" ]; then continue #echo "tail($tailpid), telnet($telnetpid) and nmon($nmonpid) working" else #echo "tail/telnet not working, start new tail/telnet" kill $tailpid >>/dev/null 2>&1 kill $telnetpid >>/dev/null 2>&1 ( echo "nmon p:$parserID e:$entityID f:$filename z:`date +%Z`" && tail -n 0 -f $nmonfile ) | telnet $server $port & sleep 2 telnetpid=$(ps -ef | grep "telnet $server $port" | grep -v "grep" | sort -n | head -n 1 | awk '{print $2}') tailpid=$(ps -ef | grep "tail -n 0 -f $nmonfile" | grep -v "grep" | sort -n | head -n 1 | awk '{print $2}') fi sleep 2 else kill $tailpid >>/dev/null 2>&1 kill $telnetpid >>/dev/null 2>&1 break; fi done /usr/bin/topas_nmon -ftdTWALM -s 60 -c 1440 -o /opt/NMON/nmon_log ./resend.sh -d /opt/NMON/nmon_log & h – help s – remote atsd server p – remote atsd port i - parser_id d - logs_directory

nmon Sender Scrpit

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SLIDE 43

nmon Parser Configuration

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SLIDE 44

nmon Format - header

44 Prepared by Axibase AAA,progname,topas_nmon AAA,command,/usr/bin/topas_nmon -ftdTWALM -s 1200 -c 72 -o /opt/NMON/day/ - youtput_dir=/opt/NMON/day/ -ystart_time=20:00:01,Oct14,2014 AAA,version,TOPAS-NMON AAA,build,AIX AAA,disks_per_line,150 AAA,host,canaria AAA,user,root AAA,AIX,6.1.7.16 AAA,TL,07 AAA,runname,canaria AAA,time,20:00:02 AAA,date,14-OCT-2014 AAA,interval,1200 AAA,snapshots,72 AAA,hardware,Architecture PowerPC Implementation POWER7_in_P7_mode 64 bit AAA,cpus,512,256 AAA,kernel, HW-type=CHRP=Common H/W Reference Platform Bus=PCI LPAR=Dynamic Multi- Processor 64 bit AAA,SerialNumber,84D8AB6 AAA,LPARNumberName,6,canaria AAA,MachineType,IBM,9119-FHB AAA,NodeName,canaria AAA,timestampsize,0

  • Upload new lines to ATSD using

inotify utility

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SLIDE 45

nmon Format – system commands

45 Prepared by Axibase BBBB,0000,name,size(GB),disc attach type BBBB,0001,hdisk10,343.42,Hitachi-HDS BBBC,000,hdisk10: BBBC,001,LV NAME LPs PPs DISTRIBUTION MOUNT POINT BBBC,002,PDIoriglogALv 18 18 00..18..00..00..00 /oracle/PDI/origlogA BBBC,003,PDIoriglogBLv 18 18 00..18..00..00..00 /oracle/PDI/origlogB BBBC,004,PDImirrlogALv 18 18 00..18..00..00..00 /oracle/PDI/mirrlogA BBBC,005,PDImirrlogBLv 18 18 00..18..00..00..00 /oracle/PDI/mirrlogB BBBC,006,PDIsapdata3Lv 46 46 00..00..00..00..46 /oracle/PDI/sapdata3 BBBC,007,PDIsapdata2Lv 111 111 00..00..00..00..111 /oracle/PDI/sapdata2 BBBC,008,PDIsapmntLv 80 80 00..80..00..00..00 /sapmnt/PDI BBBC,009,PDIusrsap 136 136 00..96..00..00..40 /usr/sap/PDI BBBC,010,PDIusrsaptransL 80 80 00..80..00..00..00 /usr/sap/trans/PDI BBBC,011,PDIoracleLv 112 112 00..56..00..00..56 /oracle/PDI BBBC,012,PDIorabinLv 64 64 00..64..00..00..00 /oracle/PDI/102_64 BBBC,013,PDIoraarchLv 1982 1982 537..89..536..536..284 /oracle/PDI/oraarch ….. BBBC,210,LV NAME LPs PPs DISTRIBUTION MOUNT POINT BBBC,211,PDIsapdata5Lv 670 670 256..95..210..00..109 /oracle/PDI/sapdata5 BBBC,212,PDIsapdata3Lv 1329 1329 144..305..189..400..291 /oracle/PDI/sapdata3 BBBB,0047,hdisk47,255.87,Hitachi-HDS BBBC,213,hdisk47: BBBC,214,LV NAME LPs PPs DISTRIBUTION MOUNT POINT BBBC,215,PDIsapdata2Lv 776 776 00..400..376..00..00 /oracle/PDI/sapdata2 BBBB,0048,hdisk48,255.87,Hitachi-HDS BBBC,216,hdisk48: BBBC,217,LV NAME LPs PPs DISTRIBUTION MOUNT POINT BBBC,218,PDIsapdata5Lv 1999 1999 400..400..399..400..400 /oracle/PDI/sapdata5

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SLIDE 46

nmon Format – snapshot

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ZZZZ,T0009,23:02:43,14-OCT-2014 CPU01,T0009,58.9,27.9,6.6,6.6 CPU02,T0009,0.4,0.6,0.0,99.0 CPU03,T0009,0.1,0.3,0.0,99.6 CPU04,T0009,0.0,0.3,0.0,99.7 .... SCPU255,T0009,0.00,0.00,0.00,0.01 SCPU256,T0009,0.00,0.00,0.00,0.01 CPU_ALL,T0009,16.0,1.7,0.7,81.6,,256 PCPU_ALL,T0009,10.22,1.12,0.1,6.42,64.00 SCPU_ALL,T0009,10.22,1.12,0.1,6.42 LPAR,T0009,17.817,64,256,124,64.00,128,0.00,13.92,14.37,1,0,15.98,1.75,0.09,10.03,15.98,1.75,0.09,10.03,0,0 POOLS,T0009,124,124.00,124.00,0.00,0.00,0.00,0.00,0,64.00 MEM,T0009,43.1,98.5,451646.0,129618.0,1048576.0,131584.0 MEMNEW,T0009,40.4,4.3,12.1,43.1,15.0,41.6 MEMUSE,T0009,4.3,3.0,40.0,960,1088,4.3,40.0, 259828592.0 PAGE,T0009,19186.2,17033.4,673.6,0.0,0.0,0.0,0.0,0.0 MEMPAGES4KB,T0009,229089856,106620616,11661852,0,11661852,110641309,960,1088,0,0,110641309 MEMPAGES64KB,T0009,2459100,562547,0,0,0,1896553,60,68,0,0,1896553,0,0,1843706 LARGEPAGE,T0009,0,0,0,0,16.0 PROC,T0009,20.79,0.52,24224,113015,9105,6041,5,6,41235,0,0,0,10 FILE,T0009,0,486,0,89481716,29209504,0,0,0 NET,T0009,0.0,1569.1,4748.1,0.0,21043.1,4752.3 NETPACKET,T0009,0.6,10604.5,4517.1,0.6,10604.5,4517.1 NETSIZE,T0009,56.8,151.5,1076.4,60.0,2921.5,1076.5 NETERROR,T0009,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0 IOADAPT,T0009,20985.2,834.6,797.9,21476.7,882.3,814.1,21053.4,861.9,807.3,21326.5,911.4,812.4,744.0,138.7,36.1,0.1,138.7,33.0 JFSFILE,T0009,88.2,9.5,59.9,44.7,1.3,0.3,15.6,0.3,36.4,73.7,68.1,89.5,59.0,88.9,88.9,88.9,88.9,0.7,46.8,88.6,93.1,93.0,93.5,93.8 JFSINODE,T0009,13.8,0.0,12.4,4.4,0.1,0.0,0.8,0.0,11.1,0.3,18.0,3.2,2.9,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0 DISKBUSY,T0009,5.1,6.4,3.9,4.2,4.9,4.4,4.1,4.1,4.6,3.1,3.9,3.6,2.8,2.2,14.0,22.6,8.5,0.0,5.5,5.6,6.7,10.5,0.5,11.1,0.0,24.9,7.8,5.0 DISKREAD,T0009,1136.4,447.6,382.6,305.1,322.3,352.8,321.1,326.3,355.4,97.8,95.5,87.6,152.3,92.2,979.4,2052.8,10432.8,0.0,438.8,559.2,4 DISKWRITE,T0009,2701.0,6.9,14.3,2.8,10.6,3.0,3.8,3.0,34.0,0.4,0.2,0.6,23.2,278.8,5.2,4.8,0.4,0.0,20.5,38.4,0.8,138.7,0.0,138.7,0.0 DISKXFER,T0009,124.5,56.8,44.8,38.5,40.8,44.2,40.4,41.2,45.6,12.4,12.1,11.3,24.5,15.0,122.6,185.4,166.0,0.0,55.2,70.9,56.7,33.2,3.0,33 DISKRXFER,T0009,74.8,56.0,43.7,38.3,40.4,44.1,40.2,40.9,43.4,12.4,12.1,11.2,19.1,11.0,122.3,185.0,166.0,0.0,53.3,68.5,56.6,0.2,3.0,0.0 DISKBSIZE,T0009,30.8,8.0,8.9,8.0,8.2,8.0,8.0,8.0,8.5,7.9,7.9,7.8,7.2,24.8,8.0,11.1,62.9,0.0,8.3,8.4,8.0,4.8,244.9,4.2,0.5,60.2,8.0,8

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SLIDE 47

47 Prepared by Axibase

"max capacity": "1.0", "max logical": "16", "max memory mb": "6144", "max virtual": "4", "min capacity": "0.1", "min logical": "1", "min memory mb": "1024", "min virtual": "1", "nodename": "itm-aix", "nov11": "2014", "online memory": "3072", "pool cpu": "16", "pool id": "0", "progname": "topas_nmon", "runname": "itm-aix", "serialnumber": "102CA4V", "smt threads": "4", "snapshots": "10000", "time": "04:26:57", "timestampsize": "0", "tl": "03", "user": "root" "version": "TOPAS-NMON", "virtual cpu": "2", "weight": "128",

Lis ist of f nmon Configuration Properties

"aix": "7.1.3.16", "build": "AIX", "capped": "0", "command": "/usr/bin...,Nov11,2014 ", "cpu in sys": "16", "cpus": "16,8", "date": "11-NOV-2014", "disks_per_line": "150", "entitled capacity": "0.5", "hardware": "Architecture Po...64 bit", "host": "itm-aix", "ibm": "8286-42A", "interval": "60", "kernel": "HW-type=CHRP=Common...64 bit", "logical cpu": "8", "lparname": "ITM-AIX", "lparno": "16", "lparnumbername": "16,ITM-AIX", "machinetype": "IBM,8286-42A",

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SLIDE 48

48 Prepared by Axibase

Derived Metrics

pcpu_total.busy = pcpu_total.sys + pcpu_total.user + pcpu_total.wait scpu_total.busy = scpu_total.sys + scpu_total.user + scpu_total.wait pcpu_total.entitled_capacity_used% = pcpu_total.busy / pcpu_total.entitled_capacity * 100 pcpu.total = pcpu.sys + pcpu.user + pcpu.wait -- computed by tag (by processor id) scpu.total = scpu.sys + scpu.user + scpu.wait -- computed by tag (by processor id) nmon.memory_mb.memused% = (1 - nmon.memory_mb.memfree/nmon.memory_mb.memtotal) * 100 nmon.memory_mb.memused = nmon.memory_mb.memtotal - nmon.memory_mb.memfree nmon.memory_mb.swapused% = (1 - nmon.memory_mb.swapfree/nmon.memory_mb.swaptotal) * 100 nmon.memory_mb.swapused = nmon.memory_mb.swaptotal - nmon.memory_mb.swapfree nmon.cpu_total.busy% = 100 - nmon.cpu_total.idle% nmon.logical_partition.entitled_used% = nmon.logical_partition.physicalcpu / nmon.logical_partition.entitled * 100 nmon.logical_partition.physicalcpu_used% = nmon.logical_partition.physicalcpu / nmon.logical_partition.virtualcpus * 100 nmon.memory.real_used_% = 100 - nmon.memory.real_free_% nmon.memory.virtual_used_% = 100 - nmon.memory.virtual_free_% nmon.memory.real_used(mb) = nmon.memory.real_total(mb) - nmon.memory.real_free(mb) nmon.memory.virtual_used(mb) = nmon.memory.virtual_total(mb) - nmon.memory.virtual_free(mb)

 ATSD computes derived metrics to simplify downstream rule development and visualization tasks  The derived metrics are stored similar to original metrics and are also available in rule expressions, forecasts and widgets:

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SLIDE 49

49 Prepared by Axibase

CPU Metrics CPU Metrics CPU Metrics Disk and i/o Metrics nmon.asynchronous_i/o.aiocpu nmon.asynchronous_i/o.aioprocs nmon.asynchronous_i/o.aiorunning nmon.cpu.idle% nmon.cpu.sys% nmon.cpu.user% nmon.cpu.wait% nmon.cpu_total.busy nmon.cpu_total.idle% nmon.cpu_total.sys% nmon.cpu_total.user% nmon.cpu_total.wait% nmon.logical_partition.capped nmon.logical_partition.ec_idle% nmon.logical_partition.ec_sys% nmon.logical_partition.ec_user% nmon.logical_partition.ec_wait% nmon.logical_partition.entitled nmon.logical_partition.folded nmon.logical_partition.logicalcpus nmon.logical_partition.physicalcpu nmon.logical_partition.pool_id nmon.logical_partition.poolcpus nmon.logical_partition.poolidle nmon.logical_partition.sharedcpu nmon.logical_partition.usedallcpu% nmon.logical_partition.usedpoolcpu% nmon.logical_partition.virtualcpus nmon.logical_partition.vp_idle% nmon.logical_partition.vp_sys% nmon.logical_partition.vp_user% nmon.logical_partition.vp_wait% nmon.logical_partition.weight nmon.pcpu.idle nmon.pcpu.sys nmon.pcpu.user nmon.pcpu.wait nmon.pcpu_total.entitled_capacity nmon.pcpu_total.idle nmon.pcpu_total.sys nmon.pcpu_total.user nmon.pcpu_total.wait nmon.processes.asleep_bufio nmon.processes.asleep_diocio nmon.processes.asleep_rawio nmon.processes.blocked nmon.processes.exec nmon.processes.fork nmon.processes.msg nmon.processes.pswitch nmon.processes.read nmon.processes.runnable nmon.processes.sem nmon.processes.swap-in nmon.processes.syscall nmon.processes.write nmon.scpu.idle nmon.scpu.sys nmon.scpu.user nmon.scpu.wait nmon.scpu_total.idle nmon.scpu_total.sys nmon.scpu_total.user nmon.scpu_total.wait nmon.multiple_cpu_pools.entitled nmon.multiple_cpu_pools.entitled_pool_capacity nmon.multiple_cpu_pools.max_pool_capacity nmon.multiple_cpu_pools.pool_busy_time nmon.multiple_cpu_pools.pool_id nmon.multiple_cpu_pools.pool_max_time nmon.multiple_cpu_pools.shcpu_busy_time nmon.multiple_cpu_pools.shcpu_tot_time nmon.multiple_cpu_pools.shcpus_in_sys nmon.disk_%busy nmon.disk_adapter.kb/s nmon.disk_adapter.tps nmon.disk_block_size nmon.disk_io_average_reads_per_second nmon.disk_io_average_writes_per_second nmon.disk_io_reads_per_second nmon.disk_io_writes_per_second nmon.disk_read_kb/s nmon.disk_read_service_time_msec/xfer nmon.disk_service_time_msec/xfer nmon.disk_transfers_per_second nmon.disk_wait_queue_time_msec/xfer nmon.disk_write_kb/s nmon.disk_write_service_time_msec/xfer nmon.file_i/o.dirblk nmon.file_i/o.iget nmon.file_i/o.namei nmon.file_i/o.readch nmon.file_i/o.ttycanch nmon.file_i/o.ttyoutch nmon.file_i/o.ttyrawch nmon.file_i/o.writech nmon.jfs_filespace_%used nmon.jfs_inode_%used nmon.large_page_use.freepages nmon.large_page_use.highwater nmon.large_page_use.pages nmon.large_page_use.sizemb nmon.large_page_use.usedpages nmon.transfers_from_disk_(reads)_per_second

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SLIDE 50

50 Prepared by Axibase

Memory Metrics Memory Metrics Memory Metrics Memory Metrics Network Metrics nmon.memory.real_free(mb) nmon.memory.real_free_% nmon.memory.real_total(mb) nmon.memory.virtual_free(mb) nmon.memory.virtual_free_% nmon.memory.virtual_total(mb) nmon.memory_mb.active nmon.memory_mb.bigfree nmon.memory_mb.buffers nmon.memory_mb.cached nmon.memory_mb.highfree nmon.memory_mb.hightotal nmon.memory_mb.inactive nmon.memory_mb.lowfree nmon.memory_mb.lowtotal nmon.memory_mb.memfree nmon.memory_mb.memshared nmon.memory_mb.memtotal nmon.memory_mb.swapcached nmon.memory_mb.swapfree nmon.memory_mb.swaptotal nmon.memory_new.free% nmon.memory_new.fscache% nmon.memory_new.pinned% nmon.memory_new.process% nmon.memory_new.system% nmon.memory_new.user% nmon.memory_use.%maxclient nmon.memory_use.%maxperm nmon.memory_use.%minperm nmon.memory_use.%numclient nmon.memory_use.%numperm nmon.memory_use.lruable_pages nmon.memory_use.maxfree nmon.memory_use.minfree nmon.memorypages.cycles nmon.memorypages.exfills nmon.memorypages.maxfree nmon.memorypages.minfree nmon.memorypages.nonsys_pgs nmon.memorypages.numclient nmon.memorypages.numclsegpin nmon.memorypages.numclseguse nmon.memorypages.numcompress nmon.memorypages.numframes nmon.memorypages.numfrb nmon.memorypages.numiodone nmon.memorypages.numperm nmon.memorypages.numpermio nmon.memorypages.numpgsp_pgs nmon.memorypages.numpout nmon.memorypages.numpsegpin nmon.memorypages.numpseguse nmon.memorypages.numralloc nmon.memorypages.numremote nmon.memorypages.numsios nmon.memorypages.numvpages nmon.memorypages.numwsegpin nmon.memorypages.numwseguse nmon.memorypages.pageins nmon.memorypages.pageouts nmon.memorypages.pfavail nmon.memorypages.pfpinavail nmon.memorypages.pfrsvdblks nmon.memorypages.pgexct nmon.memorypages.pgrclm nmon.memorypages.pgspgins nmon.memorypages.pgspgouts nmon.memorypages.pgsteals nmon.memorypages.scans nmon.memorypages.system_pgs nmon.memorypages.zerofills nmon.paging_and_virtual.allocstall nmon.paging_and_virtual.kswapd_inodesteal nmon.paging_and_virtual.kswapd_steal nmon.paging_and_virtual.nr_dirty nmon.paging_and_virtual.nr_mapped nmon.paging_and_virtual.nr_page_table_pages nmon.paging_and_virtual.nr_slab nmon.paging_and_virtual.nr_unstable nmon.paging_and_virtual.nr_writeback nmon.paging_and_virtual.pageoutrun nmon.paging_and_virtual.pgactivate nmon.paging_and_virtual.pgalloc_dma nmon.paging_and_virtual.pgalloc_high nmon.paging_and_virtual.pgalloc_normal nmon.paging_and_virtual.pgdeactivate nmon.paging_and_virtual.pgfault nmon.paging_and_virtual.pgfree nmon.paging_and_virtual.pginodesteal nmon.paging_and_virtual.pgmajfault nmon.paging_and_virtual.pgpgin nmon.paging_and_virtual.pgpgout nmon.paging_and_virtual.pgrefill_dma nmon.paging_and_virtual.pgrefill_high nmon.paging_and_virtual.pgrefill_normal nmon.paging_and_virtual.pgrotated nmon.paging_and_virtual.pgscan_direct_dma nmon.paging_and_virtual.pgscan_direct_high nmon.paging_and_virtual.pgscan_direct_normal nmon.paging_and_virtual.pgscan_kswapd_dma nmon.paging_and_virtual.pgscan_kswapd_high nmon.paging_and_virtual.pgscan_kswapd_normal nmon.paging_and_virtual.pgsteal_dma nmon.paging_and_virtual.pgsteal_high nmon.paging_and_virtual.pgsteal_normal nmon.paging_and_virtual.pswpin nmon.paging_and_virtual.pswpout nmon.paging_and_virtual.slabs_scanned nmon.paging.cycles nmon.paging.faults nmon.paging.pgin nmon.paging.pgout nmon.paging.pgsin nmon.paging.pgsout nmon.paging.reclaims nmon.paging.scans nmon.network_errors.collisions nmon.network_errors.ierrs nmon.network_errors.oerrs nmon.network_i/o.read-kb/s nmon.network_i/o.write-kb/s nmon.network_packets.read/s nmon.network_packets.reads/s nmon.network_packets.write/s nmon.network_packets.writes/s nmon.network_size.readsize nmon.network_size.writesize

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SLIDE 51

nmon Predefined Portals

51 Prepared by Axibase

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SLIDE 52

52 Prepared by Axibase

Predefined AIX IX Portal

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SLIDE 53

53 Prepared by Axibase

Predefined Linux Portal

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SLIDE 54

54 Prepared by Axibase

Contact Axib ibase

Axibase Contact Details:

  • General - 408.973.7897
  • Fax - 408.725.8885
  • Email - sales@axibase.com

Our headquarters are located in Cupertino, Silicon Valley:

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