9. Fast, Scalable Time Series Querying - pmseries

pmseries is a fast, scalable time series querying which displays information about performance metrics.

The major sections in this chapter are as follows:

Section 9.1, “Introduction to pmseries”, provides an introduction to the concepts and working of pmseries.

Section 9.2, “Timeseries Queries”, explains how query expressions are formed using the pmseries query language.

Section 9.3, “Metadata Qualifiers and Metadata Operators”, explains various metadata properties.

Section 9.4, “Time Specification”, specifies a specific time window of interest.

Section 9.5, “Expressions”, explains the various arithmetic operators, functions, function references as well as their compatibility supported by pmseries.

Section 9.6, “Timeseries Options”, explains the various timeseries options requested to pmseries using command line.

Section 9.7, “PCP Environment”, describes environment variables used to parameterize the file and directory names used by PCP.

Section 9.8, “PCP Grafana Plugin”, explains the PCP Redis data source and lays out the path to the PCP Grafana Plugin.

9.1. Introduction to pmseries

pmseries displays various types of information about performance metrics available through the scalable timeseries facilities of the Performance Co-Pilot (PCP) using the Redis distributed data store.

By default pmseries communicates with a local redis-server(1), however the -h and -p options can be used to specify an alternate Redis instance. If this instance is a node of a Redis cluster, all other instances in the cluster will be discovered and used automatically.

pmseries runs in several different modes - either querying timeseries identifiers, metadata or values (already stored in Redis), or manually loading timeseries into Redis. The latter mode is seldom used, however, since pmproxy(1) will automatically perform this function for local pmlogger(1) instances, when running in its default time series mode.

Without command line options specifying otherwise, pmseries will issue a timeseries query to find matching timeseries and values. All timeseries are identified using a unique SHA-1 hash which is always displayed in a 40-hexdigit human readable form. These hashes are formed using the metadata associated with every metric.

Importantly, this includes all metric metadata (labels, names, descriptors). Metric labels in particular are (as far as possible) unique for every machine - on Linux for example the labels associated with every metric include the unique /etc/machine-id , the hostname, domainname, and other automatically generated machine labels, as well as any administrator-defined labels from /etc/pcp/labels . These labels can be reported with pminfo(1) and the pmcd.labels metric.

See pmLookupLabels(3), pmLookupInDom(3), pmLookupName(3) and pmLookupDesc(3) for detailed information about metric labels and other metric metadata used in each timeseries identifier hash calculation.

The timeseries identifiers provide a higher level (and machine independent) identifier than the traditional PCP performance metric identifiers (pmID), instance domain identifiers (pmInDom) and metric names. See PCPIntro(1) for more details about these traditional identifiers. However, pmseries uses timeseries identifiers in much the same way that pminfo(1) uses the lower level indom, metric identifiers and metric names.

The default mode of pmseries operation (i.e. with no command line options) depends on the arguments it is presented. If all non-option arguments appear to be timeseries identifiers (in 40 hex digit form) pmseries will report metadata for these timeseries - refer to the -a option for details. Otherwise, the parameters will be treated as a timeseries query.

9.2. Timeseries Queries

Query expressions are formed using the pmseries query language described below, but can be as simple as a metric name.

The following is an example of querying timeseries from all hosts that match a metric name pattern (globbed):

$ pmseries kernel.all.cpu*

To identify timeseries expression operands, the query language uses the general syntax:

[metric.name] '{metadata qualifiers}' '[time specification]'

The metric.name component restricts the timeseries query to any matching PCP metric name (the list of metric names for a PCP archive or live host is reported by pminfo(1) with no arguments beyond – host or – archive). The pmseries syntax extends on that of pminfo and allows for glob(7) based pattern matching within the metric name. The above describes operands available as the leaves of pmseries expressions, which may include functions, arithmetic operators and other features. See the EXPRESSIONS section below for further details.

9.3. Metadata Qualifiers and Metadata Operators

Metadata qualifiers are enclosed by “curly” braces ( {} ), and further restrict the query results to timeseries operands with various metadata properties. These qualifiers are based on metric or instance names, and metric label values, and take the general form metadata.name OPERATOR value , such as:

instance.name == "cpu0"
metric.name != "kernel.all.pswitch"

When using label names, the metadata qualifier is optional and can be dropped, such as:

label.hostname == "www.acme.com"
hostname == "www.acme.com"

For metric and instance names only the string operators apply, but for metric label values all operators are available. The set of available operators is:

9.3.1. Boolean operators

All string (label, metrics and instances) and numeric (label) values can be tested for equality (“==”) or inequality (“!=”).

9.3.2. String operators

Strings can be subject to pattern matching in the form of glob matching (“~~”), regular expression matching (“=~”), and regular expression non-matching (“!~”). The “:” operator is equivalent to “~~” - i.e., regular expression matching.

9.3.3. Relational operators (numeric label values only)

Numeric label values can be subject to the less than (“<”), greater than (“>”), less than or equal (“<=”), greater than or equal (“>=”), equal (“==”) and not equal (“!=”) operators.

9.3.4. Logical operators

Multiple metadata qualifiers can be combined with the logical operators for AND (”&&”) and OR (“||”) as in many programming languages. The comma (“,”) character is equivalent to logical AND (”&&”).

9.4. Time Specification

The final (optional) component of a query allows the user to specify a specific time window of interest. Any time specification will result in values being returned for all matching timeseries only for the time window specified.

The specification is “square” bracket ( [] ) enclosed, and consists of one or more comma-separated components. Each component specifies some aspect related to time, taking the general form: keyword : value , such as:


9.4.1. Sample count

The number of samples to return, specified via either the samples or (equivalent) count keyword. The value provided must be a positive integer. If no end time is explicitly set (see “Time window” later) then the most recent samples will be returned.

9.4.2. Sample interval

An interval between successive samples can be requested using the interval or (equivalent) delta keyword. The value provided should be either a numeric or string value that will be parsed by pmParseInterval(3), such as 5 (seconds) or 2min (minutes).

9.4.3. Time window

Start and end times, and alignments, affecting the returned values. The keywords match the parameters to the pmParseTimeWindow(3) function which will be used to parse them, and are: start or (equivalent) begin , finish or (equivalent) end , align and offset.

9.4.4. Time zones

The resulting timestamps can be returned having been evaluated for a specific timezone, using the timezone or hostzone keywords. The value associated with timezone will be interpreted by pmNewZone(3). A true or false value should be associated with hostzone , and when set to true this has the same effect as described by pmNewContextZone(3).

9.5. Expressions

As described above, operands are the leaves of a query expression tree.

[metric.name] '{metadata qualifiers}' '[time specification]'

Note in most of the query expression examples below, the metadata qualifiers have been omitted for brevity. In all cases, multiple time series may qualify, particularly for the hostname label.

In the simple case, a query expression consists of a single operand and may just be a metric name. In the more general case, a query expression is either an operand or the argument to a function, or two operands in a binary arithmetic or logical expression. Most functions take a single argument (an expression), though some require additional arguments, e.g. rescale.

operand | expr operator expr | func(expr[, arg])

This grammar shows expressions may be nested, e.g. using the addition ( + ) operator as an example,

func1(expr) + func2(expr)
expr + func(expr)
func(expr) + expr
expr + expr

Rules governing compatibility of operands in an expression generally depend on the function and/or operators and are described below individually. An important rule is that if any time windows are specified, then all operands must cover the same number of samples, though the time windows may differ individually. If no time windows or sample counts are given, then pmseries will return a series identifier (SID) instead of a series of timestamps and values. This SID may be used in subsequent /series/values?series= SID REST API calls, along with a specific time window.

9.5.1. Arithmetic Operators

pmseries support addition, subtraction, division and multiplication on each value in the time series of a binary pair of operands. No unary or ternary operators are supported (yet). In all cases, the instance domain and the number of samples of time series operands must be the same. The metadata (units and dimensions) must also be compatible. Depending on the function, the result will usually have the same instance domain and (unless noted otherwise), the same units as the operands. The metadata dimensions (space, time, count) of the result may differ (see below).

Expression operands may have different qualifiers, e.g. you can perform binary arithmetic on metrics qualified by different labels (such as hostname), or metric names. For example, to add the two most recent samples of the process context switch (pswitch) counter metric for hosts node88 and node89, and then perform rate conversion:

$ pmseries 'rate(kernel.all.pswitch{hostname:node88}[count:2] +
   [Tue Nov 10 14:39:48.771868000 2020] 71.257509 8e0a59304eb99237b89593a3e839b5bb8b9a9924

Note the resulting time series of values has one less sample than the expression operand passed to the rate function.

Other rules for arithmetic expressions:

  1. If both operands have the semantics of a counter, then only addition and subtraction are allowed.

  2. If the left operand is a counter and the right operand is not, then only multiplication or division are allowed

  3. If the left operand is not a counter and the right operand is a counter, then only multiplication is allowed.

  4. Addition and subtraction - the dimensions of the result are the same as the dimensions of the operands.

  5. Multiplication - the dimensions of the result are the sum of the dimensions of the operands.

  6. Division - the dimensions of the result are the difference of the dimensions of the operands.

9.5.2. Functions

Expression functions operate on vectors of time series values, and may be nested with other functions or expressions as described above. When an operand has multiple instances, there will generally be one result for each series of instances. For example, the result for

$ pmseries 'min(kernel.all.load[count:100])'

will be the smallest value of the 100 most recent samples, treating each of the three load average instances as a separate time series. As an example, for the two most recent samples for each of the three instances of the load average metric:

$ pmseries 'kernel.all.load[count:2]'
    [Tue Nov 10 11:52:30.833379000 2020] 1.100000e+00 a7c96e5e2e0431a12279756d11590fa9fed8f306
    [Tue Nov 10 11:52:30.833379000 2020] 9.900000e-01 ee9b506935fd0976a893dc27242926f49326b9a1
    [Tue Nov 10 11:52:30.833379000 2020] 1.070000e+00 d5e1c360d13064c461169091997e1e8be7488133
    [Tue Nov 10 11:52:20.827134000 2020] 1.120000e+00 a7c96e5e2e0431a12279756d11590fa9fed8f306
    [Tue Nov 10 11:52:20.827134000 2020] 9.900000e-01 ee9b506935fd0976a893dc27242926f49326b9a1
    [Tue Nov 10 11:52:20.827134000 2020] 1.070000e+00 d5e1c360d13064c461169091997e1e8be7488133

Using the min function :

$ pmseries 'min(kernel.all.load[count:2])'
    [Tue Nov 10 11:52:30.833379000 2020] 1.100000e+00 a7c96e5e2e0431a12279756d11590fa9fed8f306
    [Tue Nov 10 11:52:30.833379000 2020] 9.900000e-01 ee9b506935fd0976a893dc27242926f49326b9a1
    [Tue Nov 10 11:52:30.833379000 2020] 1.070000e+00 d5e1c360d13064c461169091997e1e8be7488133

For singular metrics (with no instance domain), a single value will result, e.g. for the five most recent samples of the context switching metric:

$ pmseries 'kernel.all.pswitch[count:5]'
    [Tue Nov 10 12:44:59.380666000 2020] 460774294
    [Tue Nov 10 12:44:49.382070000 2020] 460747232
    [Tue Nov 10 12:44:39.378545000 2020] 460722370
    [Tue Nov 10 12:44:29.379029000 2020] 460697388
    [Tue Nov 10 12:44:19.379096000 2020] 460657412

$ pmseries 'min(kernel.all.pswitch[count:5])'
    [Tue Nov 10 12:44:19.379096000 2020] 460657412 d7832c4fba33bcc980b1a1b614e0508043288480

Future versions of pmseries may provide functions that perform aggregation, interpolation, filtering or transforms in other ways, e.g. across instances instead of time.

9.5.3. Function Reference

  • max (expr) : The maximum value in the time series for each instance of expr.

  • min (expr) : The minimum value in the time series for each instance of expr.

  • rate (expr) : The rate with respect to time of each sample. The given expr must have counter semantics and the result will have instant semantics (the time dimension reduced by one). In addition, the result will have one less sample than the operand - this is because the first sample cannot be rate converted (two samples are required).

  • rescale (expr , scale) rescale the values in the time series for each instance of expr to scale (units). Note that expr should have instant or discrete semantics (not counter - rate conversion should be done first if needed). The time, space and count dimensions between expr and scale must be compatible. Example: rate convert the read throughput counter for each disk instance and then rescale to mbytes per second. Note the native units of disk.dev.read_bytes is a counter of kbytes read from each device instance since boot.

    $ pmseries 'rescale(rate(disk.dev.read_bytes[count:4]), "mbytes/s")'
  • abs (expr) : The absolute value of each value in the time series for each instance of expr . This has no effect if the type of expr is unsigned.

  • floor (expr) : Rounded down to the nearest integer value of the time series for each instance of expr.

  • round (expr) : Rounded up or down to the nearest integer for each value in the time series for each instance of expr.

  • log (expr) : Logarithm of the values in the time series for each instance of expr.

  • sqrt (expr) : Square root of the values in the time series for each instance of expr.

9.5.4. Compatibility

All operands in an expression must have the same number of samples, but not necessarily the same time window. e.g. you could subtract some metric time series from today from that of yesterday by giving different time windows and different metrics or qualifiers, ensuring the same number of samples are given as the operands.

Operands in an expression must either all have a time window, or none. If no operands have a time window, then instead of a series of time stamps and values, the result will be a time series identifier (SID) that may be passed to the /series/values?series= SID REST API function, along with a time window. For further details, see PMWEBAPI(3).

If the semantics of both operands in an arithmetic expression are not counter (i.e. PM_SEM_INSTANT or PM_SEM_DISCRETE) then the result will have semantics PM_SEM_INSTANT unless both operands are PM_SEM_DISCRETE in which case the result is also PM_SEM_DISCRETE.

9.6. Timeseries Options

9.6.1. Timeseries Metadata

Using command line options, pmseries can be requested to provide metadata (metric names, instance names, labels, descriptors) associated with either individual timeseries or a group of timeseries, for example:

$ pmseries -a dcb2a032a308b5717bf605ba8f8737e9c6e1ed19

    PMID: 60.0.21
    Data Type: 64-bit unsigned int  InDom: PM_INDOM_NULL 0xffffffff
    Semantics: counter  Units: millisec
    Source: f5ca7481da8c038325d15612bb1c6473ce1ef16f
    Metric: kernel.all.cpu.nice
    labels {"agent":"linux","domainname":"localdomain",\

The complete set of pmseries metadata reporting options are:



-a , –all

Convenience option to report all metadata for the given timeseries, equivalent to -dilms.

-d , –desc

Metric descriptions detailing the PMID, data type, data semantics, units, scale
and associated instance domain. This option has a direct pminfo(1) equivalent.

-g pattern , –glob = pattern

Provide a glob(7) pattern to restrict the report provided by the -i , -l , -m and -S.

-i , –instances

Metric descriptions detailing the PMID, data type, data semantics, units, scale and associated instance domain.

-I , –fullindom

Print the InDom in verbose mode. This option has a direct pminfo(1) equivalent.

-l , –labels

Print label sets associated with metrics and instances. Labels are optional
metric metadata described in detail in pmLookupLabels(3). This option has a
direct pminfo(1) equivalent.

-m , –metrics

Print metric names.

-M , –fullpmid

Print the PMID in verbose mode. This option has a direct pminfo(1) equivalent.

-n , –names

Print comma-separated label names only (not values) for the labels associated with metrics and instances.

-s , –series

Print timeseries identifiers associated with metrics, instances and sources.
These unique identifiers are calculated from intrinsic (non-optional)
labels and other metric metadata associated with each PMAPI context
(sources), metrics and instances. Archive, local context or pmcd(1)
connections for the same host all produce the same source identifier.
This option has a direct pminfo(1) equivalent. See also pmLookupLabels(3)
and the -l/–labels option.

References : pminfo(1) , glob(7) , pmLookupLabels(3) , pmcd(1)

9.6.2. Timeseries Sources

A source is a unique identifier (represented externally as a 40-byte hexadecimal SHA-1 hash) that represents both the live host and/or archives from which each timeseries originated. The context for a source identifier (obtained with -s ) can be reported with:

-S , –sources : Print names for timeseries sources. These names are either hostnames or fully qualified archive paths.

It is important to note that live and archived sources can and will generate the same SHA-1 source identifier hash, provided that the context labels remain the same for that host (labels are stored in PCP archives and can also be fetched live from pmcd(1) ).

9.6.3. Timeseries Loading

Timeseries metadata and data are loaded either automatically by a local pmproxy(1), or manually using a specially crafted pmseries query and the -L/ –load option:

$ pmseries --load "{source.path: \"$PCP_LOG_DIR/pmlogger/acme\"}"
pmseries: [Info] processed 2275 archive records from [...]

This query must specify a source archive path, but can also restrict the import to specific timeseries (using metric names, labels, etc) and to a specific time window using the time specification component of the query language.

As a convenience, if the argument to load is a valid file path as determined by access(2), then a short-hand form can be used:

$ pmseries --load $PCP_LOG_DIR/pmlogger/acme.0

9.6.4. Options

The available command line options, in addition to timeseries metadata and sources options described above, are:



-c config , –config = config

Specify the config file to use.

-h host , –host = host

Connect Redis server at host, rather than the one the localhost.

-L , –load

Load timeseries metadata and data into the Redis cluster.

-p port , –port = port

Connect Redis server at port, rather than the default 6379 .

-q , –query

Perform a timeseries query. This is the default action.

-t , –times

Report time stamps numerically (in milliseconds) instead of the default human readable form.

-v , –values

Report all of the known values for given label name(s).

-V , –version

Display version number and exit.

-Z timezone , –timezone = timezone

Use timezone for the date and time. Timezone is in the format of the
environment variable TZ as described in environ(7).

-? , –help

Display usage message and exit.

9.6.5. Examples

The following sample query shows several fundamental aspects of the pmseries query language:

$ pmseries 'kernel.all.load{hostname:"toium"}[count:2]'

    [Thu Nov 14 05:57:06.082861000 2019] 1.0e-01 b84040ffccd54f839b65140cf139bab51cbbcf62
    [Thu Nov 14 05:57:06.082861000 2019] 6.8e-01 a60b5b3bf25e71071c41934fa4d7d251f765f30c
    [Thu Nov 14 05:57:06.082861000 2019] 6.4e-01 e1974a062375e6e62370ffadf5b0650dad739480
    [Thu Nov 14 05:57:16.091546000 2019] 1.6e-01 b84040ffccd54f839b65140cf139bab51cbbcf62
    [Thu Nov 14 05:57:16.091546000 2019] 6.7e-01 a60b5b3bf25e71071c41934fa4d7d251f765f30c
    [Thu Nov 14 05:57:16.091546000 2019] 6.4e-01 e1974a062375e6e62370ffadf5b0650dad739480

This query returns the two most recent values for all instances of the kernel.all.load metric with a label.hostname matching the regular expression “toium”. This is a set-valued metric (i.e., a metric with an “instance domain” which in this case consists of three instances: 1, 5 and 15 minute averages). The first column returned is a timestamp, then a floating point value, and finally an instance identifier timeseries hash (two values returned for three instances, so six rows are returned). The metadata for these timeseries can then be further examined:

$ pmseries -a eb713a9cf472f775aa59ae90c43cd7f960f7870f

    PMID: 60.2.0
    Data Type: float  InDom: 60.2 0xf000002
    Semantics: instant  Units: none
    Source: 0e89c1192db79326900d82131c31399524f0b3ee
    Metric: kernel.all.load
    inst [1 or "1 minute"] series b84040ffccd54f839b65140cf139bab51cbbcf62
    inst [5 or "5 minute"] series a60b5b3bf25e71071c41934fa4d7d251f765f30c
    inst [15 or "15 minute"] series e1974a062375e6e62370ffadf5b0650dad739480
    inst [1 or "1 minute"] labels {"agent":"linux","hostname":"toium"}
    inst [5 or "5 minute"] labels {"agent":"linux","hostname":"toium"}
    inst [15 or "15 minute"] labels {"agent":"linux","hostname":"toium"}

9.7. PCP Environment

Environment variables with the prefix PCP_ are used to parameterize the file and directory names used by PCP. On each installation, the file /etc/pcp.conf contains the local values for these variables. The $PCP_CONF variable may be used to specify an alternative configuration file, as described in pcp.conf(5).

For environment variables affecting PCP tools, see pmGetOptions(3).

9.8. PCP Grafana Plugin

The PCP Redis Grafana datasource from the PCP Grafana plugin queries the fast, scalable time series capabilities provided by the pmseries functionality. It is intended to query historical data across multiple hosts and supports filtering based on labels. This data source also provides a native interface between Grafana and Performance Co-Pilot (PCP), allowing PCP metric data to be presented in Grafana panels, such as graphs, tables, heatmaps, etc. Under the hood, the data source makes REST API query requests to the PCP pmproxy(1) service, which can be running either locally or on a remote host. The pmproxy daemon can be local or remote and uses the Redis time-series database (local or remote) for persistent storage.

For more information on PCP Grafana Plugin, visit PCP Grafana Plugin Documentation .