~launchpad-pqm/launchpad/devel

#!/usr/bin/python -S

# Copyright 2010-2011 Canonical Ltd.  This software is licensed under the

# GNU Affero General Public License version 3 (see the file LICENSE).

"""Generate the database statistics report."""

__metaclass__ = type

from datetime import datetime

from operator import attrgetter

from textwrap import (

    dedent,

    fill,

    )

import _pythonpath

from canonical.database.sqlbase import (

    connect,

    sqlvalues,

    )

from lp.services.scripts import db_options

from lp.scripts.helpers import LPOptionParser

from lp.services.database.namedrow import named_fetchall

class Table:

    pass

def get_where_clause(options, fuzz='0 seconds'):

    "Generate a WHERE clause referencing the date_created column."

    # We have two of the from timestamp, the until timestamp and an

    # interval. The interval is in a format unsuitable for processing in

    # Python. If the interval is set, it represents the period before

    # the until timestamp or the period after the from timestamp,

    # depending on which of these is set. From this information,

    # generate the SQL representation of the from timestamp and the

    # until timestamp.

    if options.from_ts:

        from_sql = ("CAST(%s AS timestamp without time zone)"

            % sqlvalues(options.from_ts))

    elif options.interval and options.until_ts:

        from_sql = (

            "CAST(%s AS timestamp without time zone) - CAST(%s AS interval)"

            % sqlvalues(options.until_ts, options.interval))

    elif options.interval:

        from_sql = (

            "(CURRENT_TIMESTAMP AT TIME ZONE 'UTC') - CAST(%s AS interval)"

            % sqlvalues(options.interval))

    else:

        from_sql = "CAST('1970-01-01' AS timestamp without time zone)"

    if options.until_ts:

        until_sql = (

            "CAST(%s AS timestamp without time zone)"

            % sqlvalues(options.until_ts))

    elif options.interval and options.from_ts:

        until_sql = (

            "CAST(%s AS timestamp without time zone) + CAST(%s AS interval)"

            % sqlvalues(options.from_ts, options.interval))

    else:

        until_sql = "CURRENT_TIMESTAMP AT TIME ZONE 'UTC'"

    fuzz_sql = "CAST(%s AS interval)" % sqlvalues(fuzz)

    clause = "date_created BETWEEN (%s - %s) AND (%s + %s)" % (

        from_sql, fuzz_sql, until_sql, fuzz_sql)

    return clause

def get_table_stats(cur, options):

    params = {'where': get_where_clause(options)}

    tablestats_query = dedent("""\

        SELECT

            Earliest.date_created AS date_start,

            Latest.date_created AS date_end,

            Latest.schemaname,

            Latest.relname,

            Latest.seq_scan - Earliest.seq_scan AS seq_scan,

            Latest.seq_tup_read - Earliest.seq_tup_read AS seq_tup_read,

            Latest.idx_scan - Earliest.idx_scan AS idx_scan,

            Latest.idx_tup_fetch - Earliest.idx_tup_fetch AS idx_tup_fetch,

            Latest.n_tup_ins - Earliest.n_tup_ins AS n_tup_ins,

            Latest.n_tup_upd - Earliest.n_tup_upd AS n_tup_upd,

            Latest.n_tup_del - Earliest.n_tup_del AS n_tup_del,

            Latest.n_tup_hot_upd - Earliest.n_tup_hot_upd AS n_tup_hot_upd,

            Latest.n_live_tup,

            Latest.n_dead_tup,

            Latest.last_vacuum,

            Latest.last_autovacuum,

            Latest.last_analyze,

            Latest.last_autoanalyze

        FROM

            DatabaseTableStats AS Earliest,

            DatabaseTableStats AS Latest

        WHERE

            Earliest.date_created = (

                SELECT min(date_created) FROM DatabaseTableStats

                WHERE %(where)s)

            AND Latest.date_created = (

                SELECT max(date_created) FROM DatabaseTableStats

                WHERE %(where)s)

            AND Earliest.schemaname = Latest.schemaname

            AND Earliest.relname = Latest.relname

        """ % params)

    cur.execute(tablestats_query)

    # description[0] is the column name, per PEP-0249

    fields = [description[0] for description in cur.description]

    tables = set()

    for row in cur.fetchall():

        table = Table()

        for index in range(len(fields)):

            setattr(table, fields[index], row[index])

        table.total_tup_read = table.seq_tup_read + table.idx_tup_fetch

        table.total_tup_written = (

            table.n_tup_ins + table.n_tup_upd + table.n_tup_del)

        tables.add(table)

    return tables

def get_cpu_stats(cur, options):

    # This query calculates the averate cpu utilization from the

    # samples. It assumes samples are taken at regular intervals over

    # the period.

    # Note that we have to use SUM()/COUNT() instead of AVG() as

    # database users not connected when the sample was taken are not

    # recorded - we want the average utilization over the time period,

    # not the subset of the time period the user was actually connected.

    params = {'where': get_where_clause(options)}

    query = dedent("""\

        SELECT (

            CAST(SUM(cpu) AS float) / (

                SELECT COUNT(DISTINCT date_created) FROM DatabaseCpuStats

                WHERE %(where)s

            )) AS avg_cpu, username

        FROM DatabaseCpuStats

        WHERE %(where)s

        GROUP BY username

        """ % params)

    cur.execute(query)

    cpu_stats = set(cur.fetchall())

    # Fold edge into lpnet, as they are now running the same code.

    # This is a temporary hack until we drop edge entirely. See

    # Bug #667883 for details.

    lpnet_avg_cpu = 0.0

    edge_avg_cpu = 0.0

    for stats_tuple in list(cpu_stats):

        avg_cpu, username = stats_tuple

        if username == 'lpnet':

            lpnet_avg_cpu = avg_cpu

            cpu_stats.discard(stats_tuple)

        elif username == 'edge':

            edge_avg_cpu = avg_cpu

            cpu_stats.discard(stats_tuple)

    cpu_stats.add((lpnet_avg_cpu + edge_avg_cpu, 'lpnet'))

    return cpu_stats

def get_bloat_stats(cur, options, kind):

    # Return information on bloated tables and indexes, as of the end of

    # the requested time period.

    params = {

        # We only collect these statistics daily, so add some fuzz

        # to ensure bloat information ends up on the daily reports;

        # we cannot guarantee the disk utilization statistics occur

        # exactly 24 hours apart. Our most recent snapshot could be 1

        # day ago, give or take a few hours.

        'where': get_where_clause(options, fuzz='1 day 6 hours'),

        'bloat': options.bloat,

        'min_bloat': options.min_bloat,

        'kind': kind,

        }

    query = dedent("""

        SELECT * FROM (

            SELECT DISTINCT

                namespace,

                name,

                sub_namespace,

                sub_name,

                count(*) OVER t AS num_samples,

                last_value(table_len) OVER t AS table_len,

                pg_size_pretty(last_value(table_len) OVER t) AS table_size,

                last_value(dead_tuple_len + free_space) OVER t AS bloat_len,

                pg_size_pretty(last_value(dead_tuple_len + free_space) OVER t)

                    AS bloat_size,

                first_value(dead_tuple_percent + free_percent) OVER t

                    AS start_bloat_percent,

                last_value(dead_tuple_percent + free_percent) OVER t

                    AS end_bloat_percent,

                (last_value(dead_tuple_percent + free_percent) OVER t

                    - first_value(dead_tuple_percent + free_percent) OVER t

                    ) AS delta_bloat_percent,

                (last_value(table_len) OVER t

                    - first_value(table_len) OVER t) AS delta_bloat_len,

                pg_size_pretty(

                    last_value(table_len) OVER t

                    - first_value(table_len) OVER t) AS delta_bloat_size

            FROM DatabaseDiskUtilization

            WHERE

                %(where)s

                AND kind = %%(kind)s

            WINDOW t AS (

                PARTITION BY sort ORDER BY date_created

                ROWS BETWEEN UNBOUNDED PRECEDING AND UNBOUNDED FOLLOWING)

            ) AS whatever

        WHERE

            table_len >= %(min_bloat)s

            AND end_bloat_percent >= %(bloat)s

        ORDER BY bloat_len DESC

        """ % params)

    cur.execute(query, params)

    bloat_stats = named_fetchall(cur)

    return list(bloat_stats)

def main():

    parser = LPOptionParser()

    db_options(parser)

    parser.add_option(

        "-f", "--from", dest="from_ts", type=datetime,

        default=None, metavar="TIMESTAMP",

        help="Use statistics collected since TIMESTAMP.")

    parser.add_option(

        "-u", "--until", dest="until_ts", type=datetime,

        default=None, metavar="TIMESTAMP",

        help="Use statistics collected up until TIMESTAMP.")

    parser.add_option(

        "-i", "--interval", dest="interval", type=str,

        default=None, metavar="INTERVAL",

        help=(

            "Use statistics collected over the last INTERVAL period. "

            "INTERVAL is a string parsable by PostgreSQL "

            "such as '5 minutes'."))

    parser.add_option(

        "-n", "--limit", dest="limit", type=int,

        default=15, metavar="NUM",

        help="Display the top NUM items in each category.")

    parser.add_option(

        "-b", "--bloat", dest="bloat", type=float,

        default=40, metavar="BLOAT",

        help="Display tables and indexes bloated by more than BLOAT%.")

    parser.add_option(

        "--min-bloat", dest="min_bloat", type=int,

        default=10000000, metavar="BLOAT",

        help="Don't report tables bloated less than BLOAT bytes.")

    parser.set_defaults(dbuser="database_stats_report")

    options, args = parser.parse_args()

    if options.from_ts and options.until_ts and options.interval:

        parser.error(

            "Only two of --from, --until and --interval may be specified.")

    con = connect()

    cur = con.cursor()

    tables = list(get_table_stats(cur, options))

    if len(tables) == 0:

        parser.error("No statistics available in that time range.")

    arbitrary_table = tables[0]

    interval = arbitrary_table.date_end - arbitrary_table.date_start

    per_second = float(interval.days * 24 * 60 * 60 + interval.seconds)

    if per_second == 0:

        parser.error("Only one sample in that time range.")

    user_cpu = get_cpu_stats(cur, options)

    print "== Most Active Users =="

    print

    for cpu, username in sorted(user_cpu, reverse=True)[:options.limit]:

        print "%40s || %10.2f%% CPU" % (username, float(cpu) / 10)

    print

    print "== Most Written Tables =="

    print

    tables_sort = [

        'total_tup_written', 'n_tup_upd', 'n_tup_ins', 'n_tup_del', 'relname']

    most_written_tables = sorted(

        tables, key=attrgetter(*tables_sort), reverse=True)

    for table in most_written_tables[:options.limit]:

        print "%40s || %10.2f tuples/sec" % (

            table.relname, table.total_tup_written / per_second)

    print

    print "== Most Read Tables =="

    print

    # These match the pg_user_table_stats view. schemaname is the

    # namespace (normally 'public'), relname is the table (relation)

    # name. total_tup_red is the total number of rows read.

    # idx_tup_fetch is the number of rows looked up using an index.

    tables_sort = ['total_tup_read', 'idx_tup_fetch', 'schemaname', 'relname']

    most_read_tables = sorted(

        tables, key=attrgetter(*tables_sort), reverse=True)

    for table in most_read_tables[:options.limit]:

        print "%40s || %10.2f tuples/sec" % (

            table.relname, table.total_tup_read / per_second)

    table_bloat_stats = get_bloat_stats(cur, options, 'r')

    if not table_bloat_stats:

        print

        print "(There is no bloat information available in this time range.)"

    else:

        print

        print "== Most Bloated Tables =="

        print

        for bloated_table in table_bloat_stats[:options.limit]:

            print "%40s || %2d%% || %s of %s" % (

                bloated_table.name,

                bloated_table.end_bloat_percent,

                bloated_table.bloat_size,

                bloated_table.table_size)

        index_bloat_stats = get_bloat_stats(cur, options, 'i')

        print

        print "== Most Bloated Indexes =="

        print

        for bloated_index in index_bloat_stats[:options.limit]:

            print "%65s || %2d%% || %s of %s" % (

                bloated_index.sub_name,

                bloated_index.end_bloat_percent,

                bloated_index.bloat_size,

                bloated_index.table_size)

        # Order bloat delta report by size of bloat increase.

        # We might want to change this to percentage bloat increase.

        bloating_sort_key = lambda x: x.delta_bloat_len

        table_bloating_stats = sorted(

            table_bloat_stats, key=bloating_sort_key, reverse=True)

        if table_bloating_stats[0].num_samples <= 1:

            print

            print fill(dedent("""\

                (There are not enough samples in this time range to display

                bloat change statistics)

                """))

        else:

            print

            print "== Most Bloating Tables =="

            print

            for bloated_table in table_bloating_stats[:options.limit]:

                # Bloat decreases are uninteresting, and would need to be in

                # a separate table sorted in reverse anyway.

                if bloated_table.delta_bloat_percent > 0:

                    print "%40s || +%4.2f%% || +%s" % (

                        bloated_table.name,

                        bloated_table.delta_bloat_percent,

                        bloated_table.delta_bloat_size)

            index_bloating_stats = sorted(

                index_bloat_stats, key=bloating_sort_key, reverse=True)

            print

            print "== Most Bloating Indexes =="

            print

            for bloated_index in index_bloating_stats[:options.limit]:

                # Bloat decreases are uninteresting, and would need to be in

                # a separate table sorted in reverse anyway.

                if bloated_index.delta_bloat_percent > 0:

                    print "%65s || +%4.2f%% || +%s" % (

                        bloated_index.sub_name,

                        bloated_index.delta_bloat_percent,

                        bloated_index.delta_bloat_size)

if __name__ == '__main__':

    main()