Warning

This document is for an old release of Galaxy. You can alternatively view this page in the latest release if it exists or view the top of the latest release's documentation.

Source code for galaxy.job_metrics.collectl.processes

""" Modules will run collectl in playback mode and collect various process
statistics for a given pid's process and process ancestors.
"""
import collections
import csv
import logging
import tempfile

from galaxy import util
from ..collectl import stats

log = logging.getLogger(__name__)

# Collectl process information cheat sheet:
#
# Record process information for current user.
# %  collectl -sZ -f./__instrument_collectl  -i 10:10 --procfilt U$USER
#
# TSV Replay of processing information in plottable mode...
#
# % collectl -sZ -P --sep=9 -p __instrument_collectl-jlaptop13-20140322-120919.raw.gz
#
# Has following columns:
#   Date   Time    PID     User    PR      PPID    THRD    S       VmSize  VmLck   VmRSS   VmData  VmStk   VmExe   VmLib   CPU       SysT    UsrT    PCT     AccumT  RKB     WKB     RKBC    WKBC    RSYS    WSYS    CNCL    MajF    MinF    Command
#

# Process data dumped one row per process per interval.
# http://collectl.sourceforge.net/Data-detail.html
PROCESS_COLUMNS = [
    "#Date",  # Date of interval - e.g. 20140322
    "Time",  # Time of interval - 12:18:58
    "PID",  # Process pid.
    "User",  # Process user.
    "PR",  # Priority of process.
    "PPID",  # Parent PID of process.
    "THRD",  # Thread???
    "S",  # Process state - S - Sleeping, D - Uninterruptable Sleep, R - Running, Z - Zombie or T - Stopped/Traced
    # Memory options - http://ewx.livejournal.com/579283.html
    "VmSize",
    "VmLck",
    "VmRSS",
    "VmData",
    "VmStk",
    "VmExe",
    "VmLib",
    "CPU",  # CPU number of process
    "SysT",  # Amount of system time consumed during interval
    "UsrT",  # Amount user time consumed during interval
    "PCT",  # Percentage of current interval consumed by task
    "AccumT",  # Total accumulated System and User time since the process began execution
    # kilobytes read/written - requires I/O level monitoring to be enabled in kernel.
    "RKB",  # kilobytes read by process - requires I/O monitoring in kernel
    "WKB",
    "RKBC",
    "WKBC",
    "RSYS",  # Number of read system calls
    "WSYS",  # Number of write system calls
    "CNCL",
    "MajF",  # Number of major page faults
    "MinF",  # Number of minor page faults
    "Command",  # Command executed
]

# Types of statistics this module can summarize
STATISTIC_TYPES = ["max", "min", "sum", "count", "avg"]

COLUMN_INDICES = {col: i for i, col in enumerate(PROCESS_COLUMNS)}
PID_INDEX = COLUMN_INDICES["PID"]
PARENT_PID_INDEX = COLUMN_INDICES["PPID"]

DEFAULT_STATISTICS = [
    ("max", "VmSize"),
    ("avg", "VmSize"),
    ("max", "VmRSS"),
    ("avg", "VmRSS"),
    ("sum", "SysT"),
    ("sum", "UsrT"),
    ("max", "PCT"),
    ("avg", "PCT"),
    ("max", "AccumT"),
    ("sum", "RSYS"),
    ("sum", "WSYS"),
]


def parse_process_statistics(statistics):
    """ Turn string or list of strings into list of tuples in format ( stat,
    resource ) where stat is a value from STATISTIC_TYPES and resource is a
    value from PROCESS_COLUMNS.
    """
    if statistics is None:
        statistics = DEFAULT_STATISTICS

    statistics = util.listify(statistics)
    statistics = [_tuplize_statistic(_) for _ in statistics]
    # Check for validity...
    for statistic in statistics:
        if statistic[0] not in STATISTIC_TYPES:
            raise Exception("Unknown statistic type encountered %s" % statistic[0])
        if statistic[1] not in PROCESS_COLUMNS:
            raise Exception("Unknown process column encountered %s" % statistic[1])
    return statistics


[docs]def generate_process_statistics(collectl_playback_cli, pid, statistics=DEFAULT_STATISTICS): """ Playback collectl file and generate summary statistics. """ with tempfile.NamedTemporaryFile() as tmp_tsv: collectl_playback_cli.run(stdout=tmp_tsv) with open(tmp_tsv.name) as tsv_file: return _read_process_statistics(tsv_file, pid, statistics)
def _read_process_statistics(tsv_file, pid, statistics): process_summarizer = CollectlProcessSummarizer(pid, statistics) current_interval = None for row in csv.reader(tsv_file, dialect="excel-tab"): if current_interval is None: for header, expected_header in zip(row, PROCESS_COLUMNS): if header.lower() != expected_header.lower(): raise Exception("Unknown header value encountered while processing collectl playback - %s" % header) # First row, check contains correct header. current_interval = CollectlProcessInterval() continue if current_interval.row_is_in(row): current_interval.add_row(row) else: process_summarizer.handle_interval(current_interval) current_interval = CollectlProcessInterval() # Do we have unsummarized rows... if current_interval and current_interval.rows: process_summarizer.handle_interval(current_interval) return process_summarizer.get_statistics() class CollectlProcessSummarizer: def __init__(self, pid, statistics): self.pid = pid self.statistics = statistics self.columns_of_interest = {s[1] for s in statistics} self.tree_statistics = collections.defaultdict(stats.StatisticsTracker) self.process_accum_statistics = collections.defaultdict(stats.StatisticsTracker) self.interval_count = 0 def handle_interval(self, interval): self.interval_count += 1 rows = self.__rows_for_process(interval.rows, self.pid) for column_name in self.columns_of_interest: column_index = COLUMN_INDICES[column_name] if column_name == "AccumT": # Should not sum this across pids each interval, sum max at end... for r in rows: pid_seconds = self.__time_to_seconds(r[column_index]) self.process_accum_statistics[r[PID_INDEX]].track(pid_seconds) else: # All other stastics should be summed across whole process tree # at each interval I guess. if column_name in ["SysT", "UsrT", "PCT"]: to_num = float else: to_num = int interval_stat = sum(to_num(r[column_index]) for r in rows) self.tree_statistics[column_name].track(interval_stat) def get_statistics(self): if self.interval_count == 0: return [] computed_statistics = [] for statistic in self.statistics: statistic_type, column = statistic if column == "AccumT": # Only thing that makes sense is sum if statistic_type != "max": log.warning("Only statistic max makes sense for AccumT") continue value = sum(v.max for v in self.process_accum_statistics.values()) else: statistics_tracker = self.tree_statistics[column] value = getattr(statistics_tracker, statistic_type) computed_statistic = (statistic, value) computed_statistics.append(computed_statistic) return computed_statistics def __rows_for_process(self, rows, pid): process_rows = [] pids = self.__all_child_pids(rows, pid) for row in rows: if row[PID_INDEX] in pids: process_rows.append(row) return process_rows def __all_child_pids(self, rows, pid): pids_in_process_tree = {str(self.pid)} added = True while added: added = False for row in rows: pid = row[PID_INDEX] parent_pid = row[PARENT_PID_INDEX] if parent_pid in pids_in_process_tree and pid not in pids_in_process_tree: pids_in_process_tree.add(pid) added = True return pids_in_process_tree def __time_to_seconds(self, minutes_str): parts = minutes_str.split(":") seconds = 0.0 for i, val in enumerate(parts): seconds += float(val) * (60 ** (len(parts) - (i + 1))) return seconds class CollectlProcessInterval: """ Represent all rows in collectl playback file for given time slice with ability to filter out just rows corresponding to the process tree corresponding to a given pid. """ def __init__(self): self.rows = [] def row_is_in(self, row): if not self.rows: # No rows, this row defines interval. return True first_row = self.rows[0] return first_row[0] == row[0] and first_row[1] == row[1] def add_row(self, row): self.rows.append(row) def _tuplize_statistic(statistic): if not isinstance(statistic, tuple): statistic_split = statistic.split("_", 1) statistic = (statistic_split[0].lower(), statistic_split[1]) return statistic __all__ = ('generate_process_statistics', )