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Procpath is a process tree analysis workbench

Project description

https://badge.fury.io/py/Procpath.svg

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 Procpath

For installing Procpath into a dedicated virtual environment pipx [12] can be used.

pipx install Procpath

Quickstart

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 query '$..children[?(@.stat.pid == 2610)]' \
  'SELECT SUM(stat_rss) / 1024.0 * 4 total FROM record'

Get total RSS in MiB of a docker-compose stack.

L=$(docker ps -f status=running -f name='^project_name' -q | xargs -I{} -- \
  docker inspect -f '{{.State.Pid}}' {} | tr '\n' ,)
procpath query "$..children[?(@.stat.pid in [$L])]" \
  'SELECT SUM(stat_rss) / 1024.0 * 4 total 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:

  1. Built-in named queries (currently CPU and RSS): --query-name rss and --query-name cpu.
  2. 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.
  3. 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 10

If opened in a browser alone this SVG has some interactivity. SVG is produced by Pygal [13].

Procpath RSS SVG

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
Procpath RSS vs CPU SVG

Ad-hoc

A GUI-driven ad-hoc visualisation can be done in Sqliteviz [11].

Ad-hoc visualisation in the online version of Sqliteviz is straightforward.

  1. Drop an SQLite database file into Sqliteviz
  2. Create new query
  3. Enter the SQL query (see examples in the section below) and run it
  4. Switch to Chart tab
  5. Click + Trace, select Line chart
  6. Choose X = ts
  7. Choose Y to the expression to plot, for instance, rss
  8. Switch to Transforms, click + Transform, add Split and choose stat_pid

It should look something like this.

Sqliteviz screenshot

SQL query

This section lists SQL queries to back the most basic temporal process analysis tasks. Similar queries with filters are used by procpath plot.

  1. 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 record
    
  2. CPU 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 record_id
        ) tick_diff,
        ts - LAG(ts) OVER (
          PARTITION BY stat_pid
          ORDER BY record_id
        ) 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
    

    Note

    1. Window function support was first added to SQLite with release version 3.25.0 (2018-09-15)
    2. The above only accounts for user and system time

Suggested SQLite database explorers are SQLiteStudio [14] and Sqliteman [15]. The latter may be available in your OS’ repositories. The former may need manual replacement of libsqlite3 it is shipped with, to a newer one with window function support (e.g. this Debian Stretch backport 3.27 [16] depends on libc6 >= 2.14).

Delegation to other tools

Procpath itself is only concerned with procfs [4], but there is a wide range of Linux tools, language-specific or not, from profilers to system call tracers which can provide the key to the problem at hand. These tools typically accept a PID or list of PIDs, and hence benefit from the process tree query capability Procpath provides. It’s a convenience to avoid unnecessary scripting and/or terminal multiplexers in case of many process tries of interest (e.g. Celery nodes).

Procpath has watch command which is analogous to procps watch. In this example watch delegates two process trees to smemstat [17] and py-spy [18].

procpath watch --interval 601 \
  -e TS='date +%s' \
  -e S1='systemctl show --property MainPID redis-server | cut -d "=" -f 2' \
  -e C1='docker inspect -f "{{.State.Pid}}" app_gunicorn_1' \
  -q L1='$..children[?(@.stat.pid == $S1)]..pid' \
  -c 'smemstat -q -o redis-memdiff-$TS.json -p $L1 30 20' \
  -c 'timeout --foreground --signal SIGINT 600 \
      py-spy record --subprocesses --output app-flamegraph-$TS.svg --pid $C1'

Notes:

  1. Typical watch pattern is:
    1. take the root PID from you process supervisor (systemd, Docker, etc)
    2. query all PID of its descendant processes
    3. pass the PID list to the analysis tool of choice
  2. The command environment is re-evaluated each --interval seconds
  3. A process is restarted each --interval seconds only if it has stopped
  4. A process’s stdout output is forwarded as INFO, and stderr as WARNING logging records
  5. If the analysis tool of choice needs to work continuously and doesn’t have a means to terminate itself, it’s suggested to wrap in into timeout --foreground --signal SIGINT INTERVAL ...
  6. watch expects to be interrupted by SIGINT (Ctrl+C), where it sends SIGINT (by default) to all its descendant processes
  7. watch can run fixed number of repetitions specified by --repeat

Sharing and ops

Procpath commands are typically multi-line shell commands, and when it comes to sharing them as such, it can become unwieldy. The spectrum here can go from having a couple of queries to diagnose your workstation you’d like to share with your colleagues, to distributing a part of a commercial product’s, say delivered on premises of the customers as systemd services, troubleshooting operations procedure.

Playbook

To make writing and sharing of command bundles easy, Procpath comes with another convenience layer – playbooks. Procpath playbooks are a Python configparser representation of its command-line interface, with a few bits of custom semantics. It looks like:

[stack]
environment:
  L=docker ps -f status=running -f name='^project_name' -q | xargs -I{} -- \
    docker inspect -f '{{.State.Pid}}' {} | tr '\n' ,
query: $..children[?(@.stat.pid in [$L])]
procfile_list: stat

# this section inherits some options, and overrides one of them
[stack:status:query]
extends: stack
sql_query: SELECT SUM(status_vmrss) total FROM record
procfile_list: stat,status

[stack:stat:query]
extends: stack
sql_query: SELECT SUM(stat_rss) * 4 total FROM record

Here’s how playbooks are read and interpreted:

  1. A CLI minus-separated argument is written as an underscore-separated option.
  2. The option value delimiter is :. A comment is prefixed with #.
  3. A multi-value option is written one per line. The long line can be broken up by placing a backslash before the newline.
  4. A section name can be compound. Segments are delimited by :. If the section represents a command, its last segment must be the command’s name.
  5. A section inherits from other sections via extends option.
  6. Single-value option search stops, going from the command section up, on the first match.
  7. A multi-value option is joined across the section’s and its parent sections’ values.

A playbook can be saved as a .procpath file and run like:

procpath play -f example.procpath '*:query'

For the playbook CLI, see the listing of procpath play --help below.

Advanced usage

  • Setting and/or overriding options via CLI

    [python:record]
    environment:
      PIDS=docker ps -f status=running -f name='^project_name' -q | xargs -I{} -- \
           docker inspect -f '{{.State.Pid}}' {} | tr '\n' ,
    query: $..children[?(@.stat.pid in [$PIDS] and 'python' in @.stat.comm)]
    interval: 10
    recnum: 30
    
    [python:plot]
    query_name:
      cpu
      rss
    

    database_file is required for both record and plot. It can be set via CLI like the following. Hence this will record the database and make CPU vs RSS plot out of it:

    procpath play -f demo.procpath -o 'database_file=db.sqlite' '*'
    
  • Running playbook with escalated privileges

    [python:watch]
    environment:
      DT=date +"%Y%m%dT%H%M%S"
      STACK=docker ps -f status=running -f name='^project_name' -q | xargs -I{} -- \
            docker inspect -f '{{.State.Pid}}' {} | tr '\n' ,
    query:
      PIDS=$..children[?(@.stat.pid in [$STACK] and 'python' in @.stat.comm)]..pid
    interval: 10
    repeat: 30
    command:
      procpath record -i 1 -d db_$DT.sqlite \
        '$..children[?(@.stat.pid in [$PIDS])]'
      echo $PIDS | tr ',' '\n' | xargs -P0 -I{} -- \
        py-spy record --idle --pid {} -o py_{}_$DT.svg
    

    py-spy typically requires escalated privileges to access the target Python process’ memory. xargs -P0 can be used to spawn py-spy per PID, because py-spy doesn’t support multiple targets natively. A playbook running py-spy with sudo can be run like the following:

    sudo env "PATH=$PATH" procpath play -f demo.procpath python:watch
    

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.

  1. Represent procfs [4] process tree as a tree structure.
  2. Expose this structure to queries in a compact tree query language.
  3. 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.

  1. 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.
  2. JSON Pointer [7]. No recursive descent supported.
  3. JMESPath (AWS boto dependency). No recursive descent supported [8].
  4. 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

query

$ procpath query --help
usage: procpath query [-h] [-f PROCFILE-LIST] [-d DELIMITER] [-i INDENT]
                      [-e ENVIRONMENT]
                      [query] [sql_query]

Execute given JSONPath and/or SQL query against process tree producing JSON or
separator-delimited values.

positional arguments:
  query                 JSONPath expression, for example this query returns PIDs
                        for process subtree including the given root's:
                        $..children[?(@.stat.pid == 2610)]..pid
  sql_query             SQL query to further filter and/or aggregate collected
                        process nodes. Note that if JSONPath query is present it
                        must return full nodes, e.g. $..children[?(@.stat.pid ==
                        2610)]. For example this query returns total RSS of the
                        processes: SELECT SUM(stat_rss) / 1024.0 * 4 total FROM
                        record

optional arguments:
  -h, --help            show this help message and exit

named arguments:
  -f PROCFILE-LIST, --procfile-list PROCFILE-LIST
                        Procfs files to read per PID. Comma-separated list. 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
  -e ENVIRONMENT, --environment ENVIRONMENT
                        Commands to evaluate in the shell and template the
                        queries, like VAR=date. Multiple occurrence is possible.

record

$ procpath record --help
usage: procpath record [-h] [-f PROCFILE-LIST] [-e ENVIRONMENT] -d DATABASE-FILE
                       [-i INTERVAL] [-r RECNUM] [-v REEVALNUM]
                       [query]

Record the nodes of process tree matching given JSONPath query into a SQLite
database in given intervals.

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

named arguments:
  -f PROCFILE-LIST, --procfile-list PROCFILE-LIST
                        Procfs files to read per PID. Comma-separated list. 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. Multiple occurrence is possible.
  -d DATABASE-FILE, --database-file DATABASE-FILE
                        Path to the recording database file

loop control arguments:
  -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
                        while recordings are taken.

plot

$ procpath plot --help
usage: procpath plot [-h] -d DATABASE-FILE [-f PLOT-FILE] [-q QUERY-NAME]
                     [--custom-query-file CUSTOM-QUERY-FILE]
                     [--custom-value-expr CUSTOM-VALUE-EXPR] [-a AFTER]
                     [-b BEFORE] [-p PID-LIST] [-l] [--style STYLE]
                     [--formatter FORMATTER] [--title TITLE] [-e EPSILON]
                     [-w MOVING-AVERAGE-WINDOW]

Plot previously recorded SQLite database using predefined or custom SQL
expression or query.

optional arguments:
  -h, --help            show this help message and exit

named arguments:
  -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.

query control arguments:
  -q QUERY-NAME, --query-name QUERY-NAME
                        Built-in query name. Available: rss,cpu. Can occur once
                        or twice (including other query-contributing options).
                        In the latter case, the plot has two Y axes.
  --custom-query-file CUSTOM-QUERY-FILE
                        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 (including other query-
                        contributing options). In the latter case, the plot has
                        two Y axes.
  --custom-value-expr CUSTOM-VALUE-EXPR
                        Use custom SELECT expression to plot as the value. Can
                        occur once or twice (including other query-contributing
                        options). In the latter case, the plot has two Y axes.

filter control arguments:
  -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.

plot control arguments:
  -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.

post-processing control arguments:
  -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.

watch

$ procpath watch --help
usage: procpath watch [-h] [-e ENVIRONMENT] [-q QUERY] -c COMMAND -i INTERVAL
                      [-r REPEAT] [-s STOP-SIGNAL] [-f PROCFILE-LIST]

Execute given commands in given intervals. It has similar purpose to procps
watch, but allows JSONPath queries to process tree to choose processes of
interest.

optional arguments:
  -h, --help            show this help message and exit

command control arguments:
  -e ENVIRONMENT, --environment ENVIRONMENT
                        Commands to evaluate in the shell, like C1='docker
                        inspect -f "{{.State.Pid}}" nginx' or D='date +%s'.
                        Multiple occurrence is possible.
  -q QUERY, --query QUERY
                        JSONPath expressions that typically evaluate into a list
                        of PIDs. The environment defined with -e can be used
                        like L1='$..children[?(@.stat.pid == $C1)]..pid'.
                        Multiple occurrence is possible.
  -c COMMAND, --command COMMAND
                        Target command to "watch" in the shell. The environment
                        and query results can be used like 'smemstat -o
                        smemstat-$D.json -p $L1'. Query result lists are joined
                        with comma. Multiple occurrence is possible.

named arguments:
  -i INTERVAL, --interval INTERVAL
                        Interval in second after which to re-evaluate the
                        environment and the queries, and re-run each command if
                        one has finished.
  -r REPEAT, --repeat REPEAT
                        Fixed number to repetitions instead of infinite watch.
  -s STOP-SIGNAL, --stop-signal STOP-SIGNAL
                        Signal to send to the spawned processes on watch stop.
                        By default: SIGINT.
  -f PROCFILE-LIST, --procfile-list PROCFILE-LIST
                        Procfs files to read per PID. Comma-separated list. By
                        default: stat, cmdline. Available: stat, cmdline, io,
                        status.

play

$ procpath play --help
usage: procpath play [-h] -f PLAYBOOK-FILE [-l] [-n] [-o OPTION]
                     target [target ...]

Play one or more sections from given playbook.

positional arguments:
  target                Name or glob-expression of the section from the
                        playbook.

optional arguments:
  -h, --help            show this help message and exit

named arguments:
  -f PLAYBOOK-FILE, --playbook-file PLAYBOOK-FILE
                        Path to the playbook to play.
  -l, --list-sections   List matching sections in the playbook.
  -n, --dry-run         Collect and print target sections.
  -o OPTION, --option OPTION
                        A key-value pair to override the option in the playbook,
                        like database_file=db.sqlite. Multiple occurrence is
                        possible.

[1]https://unix.stackexchange.com/q/67668/124219
[2]https://stackoverflow.com/a/59182595/2072035
[3]https://heptapod.host/saajns/procpath/-/blob/branch/default/procpath/procfile.py
[4](1, 2) https://en.wikipedia.org/wiki/Procfs
[5]https://goessner.net/articles/JsonPath/
[6](1, 2) https://pypi.org/project/JSONPyth/
[7]https://tools.ietf.org/html/rfc6901
[8]https://github.com/jmespath/jmespath.py/issues/110
[9]https://pypi.org/project/jq/
[11]https://github.com/lana-k/sqliteviz
[12]https://pypi.org/project/pipx/
[13]https://pypi.org/project/pygal/
[14]https://github.com/pawelsalawa/sqlitestudio
[15]https://sourceforge.net/projects/sqliteman/
[16]https://packages.debian.org/stretch-backports/libsqlite3-0
[17]https://kernel.ubuntu.com/~cking/smemstat/
[18]https://pypi.org/project/py-spy/

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