Procpath is a process tree analysis workbench
Project description
Procpath
Procpath is a process tree analysis command-line workbench. Its goal is to provide natural interface to the tree structure of processes running on a Linux system for inspection and later analysis.
Installation
pip install --user ProcpathFor installing Procpath into a dedicated virtual environment pipx [12] can be used.
pipx install ProcpathQuickstart
Get comma-separated PIDs of the process subtree (including the parent process pid=2610).
procpath query -d , '$..children[?(@.stat.pid == 2610)]..pid'Get JSON document of said process subtree.
procpath query -i 2 '$..children[?(@.stat.pid == 2610)]'Get total RSS in MiB of said process subtree (this is an example that query produces JSON that can be further processed outside of procpath, and below is a much easier way to calculate aggregates).
procpath query '$..children[?(@.stat.pid == 2610)]' \
| jq '[.. | .stat? | objects | .rss] | add / 1024 * 4'Get total RSS in MiB of said process subtree the easy way.
procpath record -d out.sqlite -r 1 '$..children[?(@.stat.pid == 2610)]'
sqlite3 out.sqlite 'SELECT SUM(stat_rss) / 1024.0 * 4 FROM record'Record process trees of two Docker containers once a second, re-evaluating the containers’ root process PIDs once per 30 recordings. Then visualise RSS of each process (which is also just an example, that output SQLite database can be visualised in different ways, including exporting CSV, sqlite3 -csv ..., and doing it the old way, to using proper UI described in Visualisation section below).
procpath record \
-e C1='docker inspect -f "{{.State.Pid}}" project_db_1' \
-e C2='docker inspect -f "{{.State.Pid}}" project_app_1' \
-i 1 -v 30 -d out.sqlite '$..children[?(@.stat.pid in [$C1, $C2])]'
# press Ctrl + C
sqlite3 out.sqlite \
"SELECT stat_pid, group_concat(stat_rss / 1024.0 * 4) \
FROM record \
GROUP BY stat_pid" \
| sed -z 's/\n/\n\n\n/g' | sed 's/|/\n/' | sed 's/,/\n/g' > special_fmt
gnuplot -p -e \
"plot for [i=0:*] 'special_fmt' index i with lines title columnheader(1)"Visualisation
This section describes two methods of visualisation of SQLite databases produced by procpath record.
Built-in
Procpath comes with built-in SVG visualisation for temporal process analysis tasks. The data for visualisation can be fetch from the SQLite database in 3 ways:
Built-in named queries (currently CPU and RSS): --query-name rss and --query-name cpu.
Custom value SELECT expression for any numeric column, e.g. --custom-value-expr "stat_majflt / 1000.0" with scaling, or --custom-value-expr IFNULL(io_rchar - LAG(io_rchar) OVER (PARTITION BY stat_pid ORDER BY record_id), 0) converting cumulative series to series of deltas.
Custom SQL file with whatever calculation you can think of. The result-set must have 3 columns: ts, pid, value. The built-in queries can used as an example, see procpath.procret module.
Plotting features include the following (see the listing of procpath plot --help below).
filtering by time range and PIDs
post-processing using Ramer-Douglas-Peucker algorithm and moving average
comparison plot with two Y axes
logarithmic scale plot
Pygal plot styles and value formatters, and custom plot title
This example plots all processes’ RSS from the recorded database, using Ramer-Douglas-Peucker algorithm to remove redundant points from the SVG with ε=0.5, and with moving average window of 10.
procpath plot -d out.sqlite -f rss.svg -q rss -e 0.5 -w 10If opened in a browser alone this SVG has some interactivity. SVG is produced by Pygal [13].
This example plots RSS vs CPU for PIDs 10543 and 22570 between 2020-07-26 21:30:00 and 2020-07-26 22:30:00 UTC from the recorded database, with moving average window of 4, on logarithmic scale and using Pygal’s LightColorizedStyle and forced integer value formatter.
procpath plot -d out.sqlite -q rss -q cpu --formatter integer -l -w 4 \
-p 10543,22570 --after 2020-07-26T21:30:00 --before 2020-07-26T22:30:00 \
--style LightColorizedStyle
Ad-hoc
A GUI-driven ad-hoc visualisation can be done in Plotly Falcon [11]. Instead of official raw Electron build, you can use this script to build AppImage [10].
Ad-hoc visualisation in Falcon is straightforward.
Choose the SQLite database file
Enter the query (see examples in the section below) and run it
Switch to CHART tab
Click + TRACE, select Line chart
Choose X = ts
Choose Y to the the expression to plot, for instance, rss
Switch to Transforms, + Transform to add Split and choose stat_pid
It should look like this.
SQL query
This section lists SQL queries to back the most basic temporal process analysis tasks. Same queries with filters are used by procpath plot.
RSS in MiB per process.
SELECT datetime(ts, 'unixepoch', 'localtime') ts, stat_pid, stat_rss / 1024.0 / 1024 * (SELECT value FROM meta WHERE key = 'page_size') rss FROM recordCPU usage percent per process.
WITH diff AS ( SELECT ts, stat_pid, stat_utime + stat_stime - LAG(stat_utime + stat_stime) OVER ( PARTITION BY stat_pid ORDER BY ts ) tick_diff, ts - LAG(ts) OVER ( PARTITION BY stat_pid ORDER BY ts ) ts_diff FROM record ) SELECT datetime(ts, 'unixepoch', 'localtime') ts, stat_pid, 100.0 * tick_diff / (SELECT value FROM meta WHERE key = 'clock_ticks') / ts_diff cpu_load FROM diff
Design
This section describes the problem and the solution in general. What preceded Procpath and why it didn’t solve the problem.
Problem statement
On servers and desktops processes have become treelike long ago. For instance, this is a process tree of Chromium browser with few opened tabs:
chromium-browser ...
├─ chromium-browser --type=utility ...
├─ chromium-browser --type=gpu-process ...
│ └─ chromium-browser --type=broker
└─ chromium-browser --type=zygote
└─ chromium-browser --type=zygote
├─ chromium-browser --type=renderer ...
├─ chromium-browser --type=renderer ...
├─ chromium-browser --type=renderer ...
├─ chromium-browser --type=renderer ...
└─ chromium-browser --type=utility ...
On a server environment it can be substituted with a dozen of task queue worker process trees, processes of the connection pool of a database, several web-server process trees or anything-goes in a bunch of Docker containers.
This environment begs some operational questions, point-in-time and temporal. When I have several trees like above, how do I know the (sub)tree’s current resource profile, like total main memory consumption, CPU time and so on? How do I track these profiles in time when, for instance, I suspect a memory leak? How to point other process analysis and introspection tools to these trees?
Existing approaches for outputting a tree’s PIDs include applying bash-fu on pstree output [1] or nested pgrep for shallower cases. procps (providing top and ps) is inadequate for any of above from embracing process hierarchy to collecting temporal metrics. psmisc (providing pstree) is only good for displaying the hierarchy, and doesn’t cover any programmatic interaction. htop is great for interactive inspection of process trees with its filter and search, but for programmatic interaction is also useless. glances has the JSON output feature, but it doesn’t have process-level granularity…
For process metrics collection alone (given you know the PIDs), sysstat (providing pidstat) is likely the only simple solution, which still requires some ad-hoc scripting [2].
Solution
The solution lies in applying the right tool to the job principle.
Represent procfs [4] process tree as a tree structure.
Expose this structure to queries in a compact tree query language.
Flatten and store a query result in a ubiquitous format allowing for easy transmission and transformation.
A major non-functional requirement here is ease of installation, preferably in the form of pure-python package. That’s because an ad-hoc investigation may not allow installing compiler toolchain on the target machine, which discards psutil and discourages XML as the tree representation format, as it would require lxml for XPath.
Representation is relatively simple. Read all /proc/N/stat, build the tree and serialise it as JSON. The ubiquitous form is even simpler. SQLite!
The step in between is much less obvious. Discarding special graph query languages and focusing on ones targeting JSON the list goes like this. But it’s unfortunately, taking into account the Python implementations, is not about choosing the best requirement match, but about choosing the lesser evil.
JSONPath [5] and its Python port. Informal, regex-based (obscure error messages and edge-cases), what-if-XPath-worked-on-JSON prototype. Most popular non-regex Python implementation are a sequence of forks, none of which supports recursive descent. One grammar-based package would work [6], but its filter expressions are just Python eval.
JSON Pointer [7]. No recursive descent supported.
JMESPath (AWS boto dependency). No recursive descent supported [8].
jq and its Python bindings [9]. jq is a programming language in disguise of JSON transformation CLI tool. Even though there’s lengthy documentation, on occasional use jq feels very counter-intuitive and requires lot of googling and trial-and-error.
Pondering and playing with these, item 1 and JSONPyth [6] was the choice. Filter Python expression syntax can be “jsonified” by the AttrDict idiom, and the security concern of eval is justified by the CLI use cases.
Data model
procpath query outputs the pid=1 process node with all its descendants into stdout.
{
"stat": {"pid": 1, "ppid": 0, ...}
"cmdline": "root node",
"other_stat_file": ...,
"children": [
{
"cmdline": "cmdline of some process",
"stat": {"pid": 1, "ppid": 323, ...},
"other_stat_file": ...
},
{
"cmdline": "cmdline of another process with children",
"stat": {"pid": 1, "ppid": 324, ...},
"other_stat_file": ...,
"children": [...]
},
...
]
}When JSONPath query is provided to the command, the output is a list of process nodes. See more examples in the test suite.
When recorded into a SQLite database, schema is inferred from used procfs files. The root node or the node list is flattened and recorded into the record table having the DDL like the following.
CREATE TABLE record (
record_id INTEGER PRIMARY KEY AUTOINCREMENT NOT NULL,
ts REAL NOT NULL,
cmdline TEXT,
stat_pid INTEGER,
stat_comm TEXT,
...
)Procpath doesn’t pre-processes procfs data. For instance, rss is expressed in pages, utime in clock ticks and so on. To properly interpret data in record table, there’s also meta table containing the following key-value records.
platform_node |
platform.node() |
platform_platform |
platform.platform() |
page_size |
resource.getpagesize() typically 4096 |
clock_ticks |
os.sysconf('SC_CLK_TCK') typically 100 |
Procpath supports stat, cmdline, io and status procfs files. stat and cmdline are the default ones. Each procfs file field is described in procpath.procfile module [3].
Command-line interface
$ procpath query --help
usage: procpath query [-h] [-f PROCFILE_LIST] [-d DELIMITER] [-i INDENT]
[query]
positional arguments:
query JSONPath expression, for example this query returns
PIDs for process subtree including the given root's:
$..children[?(@.stat.pid == 2610)]..pid
optional arguments:
-h, --help show this help message and exit
-f PROCFILE_LIST, --procfile-list PROCFILE_LIST
PID proc files to read. By default: stat,cmdline.
Available: stat,cmdline,io,status.
-d DELIMITER, --delimiter DELIMITER
Join query result using given delimiter
-i INDENT, --indent INDENT
Format result JSON using given indent number$ procpath record --help
usage: procpath record [-h] [-f PROCFILE_LIST] [-e ENVIRONMENT]
[-i INTERVAL] [-r RECNUM] [-v REEVALNUM] -d
DATABASE_FILE
[query]
positional arguments:
query JSONPath expression, for example this query returns a
node including its subtree for given PID:
$..children[?(@.stat.pid == 2610)]
optional arguments:
-h, --help show this help message and exit
-f PROCFILE_LIST, --procfile-list PROCFILE_LIST
PID proc files to read. By default: stat,cmdline.
Available: stat,cmdline,io,status.
-e ENVIRONMENT, --environment ENVIRONMENT
Commands to evaluate in the shell and template the
query, like VAR=date.
-i INTERVAL, --interval INTERVAL
Interval in second between each recording, 10 by
default.
-r RECNUM, --recnum RECNUM
Number of recordings to take at --interval seconds
apart. If not specified, recordings will be taken
indefinitely.
-v REEVALNUM, --reevalnum REEVALNUM
Number of recordings after which environment must be
re-evaluate. It's useful when you expect it to change
in while recordings are taken.
-d DATABASE_FILE, --database-file DATABASE_FILE
Path to the recording database file$ path plot --help
usage: procpath plot [-h] -d DATABASE_FILE [-f PLOT_FILE]
[-q QUERY_NAME_LIST] [-a AFTER] [-b BEFORE]
[-p PID_LIST] [-e EPSILON] [-w MOVING_AVERAGE_WINDOW]
[-l] [--style STYLE] [--formatter FORMATTER]
[--title TITLE]
[--custom-query-file CUSTOM_QUERY_FILE_LIST]
[--custom-value-expr CUSTOM_VALUE_EXPR_LIST]
optional arguments:
-h, --help show this help message and exit
-d DATABASE_FILE, --database-file DATABASE_FILE
Path to the database file to read from.
-f PLOT_FILE, --plot-file PLOT_FILE
Path to the output SVG file, plot.svg by default.
-q QUERY_NAME_LIST, --query-name QUERY_NAME_LIST
Built-in query name. Available: rss,cpu. Can occur
once or twice. In the latter case, the plot has two Y
axes.
-a AFTER, --after AFTER
Include only points after given UTC date, like
2000-01-01T00:00:00.
-b BEFORE, --before BEFORE
Include only points before given UTC date, like
2000-01-01T00:00:00.
-p PID_LIST, --pid-list PID_LIST
Include only given PIDs. Comma-separated list.
-e EPSILON, --epsilon EPSILON
Reduce points using Ramer-Douglas-Peucker algorithm
and given ε.
-w MOVING_AVERAGE_WINDOW, --moving-average-window MOVING_AVERAGE_WINDOW
Smooth the lines using moving average.
-l, --logarithmic Plot using logarithmic scale.
--style STYLE Plot using given pygal.style, like LightGreenStyle.
--formatter FORMATTER
Force given pygal.formatter, like integer.
--title TITLE Override plot title.
--custom-query-file CUSTOM_QUERY_FILE_LIST
Use custom SQL query in given file. The result-set
must have 3 columns: ts, pid, value. See
procpath.procret. Can occur once or twice. In the
latter case, the plot has two Y axes.
--custom-value-expr CUSTOM_VALUE_EXPR_LIST
Use custom SELECT expression to plot as the value. Can
occur once or twice. In the latter case, the plot has
two Y axes.Release history Release notifications | RSS feed
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