Meilleure façon d'utiliser Apache Hive pour sessionize mes données de journal ? -Débordement de pile

Is there a better way to use Apache Hive to sessionize my log data ? I'm not sure that I'm doing so, below, in the optimal way:

The log data is stored in sequence files; a single log entry is a JSON string; eg:

{"source": {"api_key": "app_key_1", "user_id": "user0"}, "events": [{"timestamp": 1330988326, "event_type": "high_score", "event_params": {"score": "1123", "level": "9"}}, {"timestamp": 1330987183, "event_type": "some_event_0", "event_params": {"some_param_00": "val", "some_param_01": 100}}, {"timestamp": 1330987775, "event_type": "some_event_1", "event_params": {"some_param_11": 100, "some_param_10": "val"}}]}

Formatted, this looks like:

{'source': {'api_key': 'app_key_1', 'user_id': 'user0'},
 'events': [{'event_params': {'level': '9', 'score': '1123'},
             'event_type': 'high_score',
             'timestamp': 1330988326},
            {'event_params': {'some_param_00': 'val', 'some_param_01': 100},
             'event_type': 'some_event_0',
             'timestamp': 1330987183},
            {'event_params': {'some_param_10': 'val', 'some_param_11': 100},
             'event_type': 'some_event_1',
             'timestamp': 1330987775}]
}

'source' contains some info ( user_id and api_key ) about the source of the events contained in 'events'; 'events' contains a list of events generated by the source; each event has 'event_params', 'event_type', and 'timestamp' ( timestamp is a Unix timestamp in GMT ). Note that timestamps within a single log entry, and across log entries may be out of order.

Note that I'm constrained such that I cannot change the log format, cannot initially log the data into separate files that are partitioned ( though I could use Hive to do this after the data is logged ), etc.

In the end, I'd like a table of sessions, where a session is associated with an app ( api_k ) and user, and has a start time and session length ( or end time ); sessions are split where, for a given app and user, a gap of 30 or more minutes occurs between events.

My solution does the following ( Hive script and python transform script are below; doesn't seem like it would be useful to show the SerDe source, but let me know if it would be ):

[1] load the data into log_entry_tmp, in a denormalized format

[2] explode the data into log_entry, so that, eg, the above single entry would now have multiple entries:

{"source_api_key":"app_key_1","source_user_id":"user0","event_type":"high_score","event_params":{"score":"1123","level":"9"},"event_timestamp":1330988326}
{"source_api_key":"app_key_1","source_user_id":"user0","event_type":"some_event_0","event_params":{"some_param_00":"val","some_param_01":"100"},"event_timestamp":1330987183}
{"source_api_key":"app_key_1","source_user_id":"user0","event_type":"some_event_1","event_params":{"some_param_11":"100","some_param_10":"val"},"event_timestamp":1330987775}

[3] transform and write data into session_info_0, where each entry contains events' app_id, user_id, and timestamp

[4] tranform and write data into session_info_1, where entries are ordered by app_id, user_id, event_timestamp ; and each entry contains a session_id ; the python tranform script finds the splits, and groups the data into sessions

[5] transform and write final session data to session_info_2 ; the sessions' app + user, start time, and length in seconds

---

[Hive script]

drop table if exists app_info;
create external table app_info ( app_id int, app_name string, api_k string )
location '${WORK}/hive_tables/app_info';

add jar ../build/our-serdes.jar;

-- [1] load the data into log_entry_tmp, in a denormalized format

drop table if exists log_entry_tmp;
create external table log_entry_tmp
row format serde 'com.company.TestLogSerde'
location '${WORK}/hive_tables/test_logs';

drop table if exists log_entry;
create table log_entry (
    entry struct<source_api_key:string,
                 source_user_id:string,
                 event_type:string,
                 event_params:map<string,string>,
                 event_timestamp:bigint>);

-- [2] explode the data into log_entry

insert overwrite table log_entry
select explode (trans0_list) t
from log_entry_tmp;

drop table if exists session_info_0;
create table session_info_0 (
    app_id string,
    user_id string,
    event_timestamp bigint
);

-- [3] transform and write data into session_info_0, where each entry contains events' app_id, user_id, and timestamp

insert overwrite table session_info_0
select ai.app_id, le.entry.source_user_id, le.entry.event_timestamp
from log_entry le
join app_info ai on (le.entry.source_api_key = ai.api_k);

add file ./TestLogTrans.py;

drop table if exists session_info_1;
create table session_info_1 (
    session_id string,
    app_id string,
    user_id string,
    event_timestamp bigint,
    session_start_datetime string,
    session_start_timestamp bigint,
    gap_secs int
);

-- [4] tranform and write data into session_info_1, where entries are ordered by app_id, user_id, event_timestamp ; and each entry contains a session_id ; the python tranform script finds the splits, and groups the data into sessions

insert overwrite table session_info_1
select
    transform (t.app_id, t.user_id, t.event_timestamp)
        using './TestLogTrans.py'
        as (session_id, app_id, user_id, event_timestamp, session_start_datetime, session_start_timestamp, gap_secs)
from
    (select app_id as app_id, user_id as user_id, event_timestamp as event_timestamp from session_info_0 order by app_id, user_id, event_timestamp ) t;

drop table if exists session_info_2;
create table session_info_2 (
    session_id string,
    app_id string,
    user_id string,
    session_start_datetime string,
    session_start_timestamp bigint,
    len_secs int
);

-- [5] transform and write final session data to session_info_2 ; the sessions' app + user, start time, and length in seconds

insert overwrite table session_info_2
select session_id, app_id, user_id, session_start_datetime, session_start_timestamp, sum(gap_secs)
from session_info_1
group by session_id, app_id, user_id, session_start_datetime, session_start_timestamp;

---

[TestLogTrans.py]

#!/usr/bin/python

import sys, time

def buildDateTime(ts):

    return time.strftime('%Y-%m-%d %H:%M:%S', time.gmtime(ts))

curGroup = None
prevGroup = None
curSessionStartTimestamp = None
curSessionStartDatetime = None
prevTimestamp = None

for line in sys.stdin.readlines():

    fields = line.split('\t')
    if len(fields) != 3:
        raise Exception('fields = %s', fields)

    app_id = fields[0]
    user_id = fields[1]
    event_timestamp = int(fields[2].strip())

    curGroup = '%s-%s' % (app_id, user_id)
    curTimestamp = event_timestamp

    if prevGroup == None:
        prevGroup = curGroup
        curSessionStartTimestamp = curTimestamp
        curSessionStartDatetime = buildDateTime(curSessionStartTimestamp)
        prevTimestamp = curTimestamp

    isNewGroup = (curGroup != prevGroup)

    gapSecs = 0 if isNewGroup else (curTimestamp - prevTimestamp)

    isSessionSplit = (gapSecs >= 1800)

    if isNewGroup or isSessionSplit:
        curSessionStartTimestamp = curTimestamp
        curSessionStartDatetime = buildDateTime(curSessionStartTimestamp)

    session_id = '%s-%s-%d' % (app_id, user_id, curSessionStartTimestamp)

    print '%s\t%s\t%s\t%d\t%s\t%d\t%d' % (session_id, app_id, user_id, curTimestamp, curSessionStartDatetime, curSessionStartTimestamp, gapSecs)

    prevGroup = curGroup
    prevTimestamp = curTimestamp

I think you could easily drop step 3, and put that query that you use there in as a subquery to your from clause in step 4. Physicalising that transform, doesn't appear to give you anything.

Otherwise I think for what you're trying to achieve here, this seems a reasonable approach.

Potentially step 2 you could achieve using a custom mapper, passing the output into step 4 as a custome reducer (with step 3 built in as a subquery). That will reduce you mapreduce jobs by 1, and therefore could give you a significant saving in time.

Is there a better way to use Apache Hive to sessionize my log data ? I'm not sure that I'm doing so, below, in the optimal way:

The log data is stored in sequence files; a single log entry is a JSON string; eg:

{"source": {"api_key": "app_key_1", "user_id": "user0"}, "events": [{"timestamp": 1330988326, "event_type": "high_score", "event_params": {"score": "1123", "level": "9"}}, {"timestamp": 1330987183, "event_type": "some_event_0", "event_params": {"some_param_00": "val", "some_param_01": 100}}, {"timestamp": 1330987775, "event_type": "some_event_1", "event_params": {"some_param_11": 100, "some_param_10": "val"}}]}

Formatted, this looks like:

{'source': {'api_key': 'app_key_1', 'user_id': 'user0'},
 'events': [{'event_params': {'level': '9', 'score': '1123'},
             'event_type': 'high_score',
             'timestamp': 1330988326},
            {'event_params': {'some_param_00': 'val', 'some_param_01': 100},
             'event_type': 'some_event_0',
             'timestamp': 1330987183},
            {'event_params': {'some_param_10': 'val', 'some_param_11': 100},
             'event_type': 'some_event_1',
             'timestamp': 1330987775}]
}

'source' contains some info ( user_id and api_key ) about the source of the events contained in 'events'; 'events' contains a list of events generated by the source; each event has 'event_params', 'event_type', and 'timestamp' ( timestamp is a Unix timestamp in GMT ). Note that timestamps within a single log entry, and across log entries may be out of order.

Note that I'm constrained such that I cannot change the log format, cannot initially log the data into separate files that are partitioned ( though I could use Hive to do this after the data is logged ), etc.

In the end, I'd like a table of sessions, where a session is associated with an app ( api_k ) and user, and has a start time and session length ( or end time ); sessions are split where, for a given app and user, a gap of 30 or more minutes occurs between events.

My solution does the following ( Hive script and python transform script are below; doesn't seem like it would be useful to show the SerDe source, but let me know if it would be ):

[1] load the data into log_entry_tmp, in a denormalized format

[2] explode the data into log_entry, so that, eg, the above single entry would now have multiple entries:

{"source_api_key":"app_key_1","source_user_id":"user0","event_type":"high_score","event_params":{"score":"1123","level":"9"},"event_timestamp":1330988326}
{"source_api_key":"app_key_1","source_user_id":"user0","event_type":"some_event_0","event_params":{"some_param_00":"val","some_param_01":"100"},"event_timestamp":1330987183}
{"source_api_key":"app_key_1","source_user_id":"user0","event_type":"some_event_1","event_params":{"some_param_11":"100","some_param_10":"val"},"event_timestamp":1330987775}

[3] transform and write data into session_info_0, where each entry contains events' app_id, user_id, and timestamp

[4] tranform and write data into session_info_1, where entries are ordered by app_id, user_id, event_timestamp ; and each entry contains a session_id ; the python tranform script finds the splits, and groups the data into sessions

[5] transform and write final session data to session_info_2 ; the sessions' app + user, start time, and length in seconds

---

[Hive script]

drop table if exists app_info;
create external table app_info ( app_id int, app_name string, api_k string )
location '${WORK}/hive_tables/app_info';

add jar ../build/our-serdes.jar;

-- [1] load the data into log_entry_tmp, in a denormalized format

drop table if exists log_entry_tmp;
create external table log_entry_tmp
row format serde 'com.company.TestLogSerde'
location '${WORK}/hive_tables/test_logs';

drop table if exists log_entry;
create table log_entry (
    entry struct<source_api_key:string,
                 source_user_id:string,
                 event_type:string,
                 event_params:map<string,string>,
                 event_timestamp:bigint>);

-- [2] explode the data into log_entry

insert overwrite table log_entry
select explode (trans0_list) t
from log_entry_tmp;

drop table if exists session_info_0;
create table session_info_0 (
    app_id string,
    user_id string,
    event_timestamp bigint
);

-- [3] transform and write data into session_info_0, where each entry contains events' app_id, user_id, and timestamp

insert overwrite table session_info_0
select ai.app_id, le.entry.source_user_id, le.entry.event_timestamp
from log_entry le
join app_info ai on (le.entry.source_api_key = ai.api_k);

add file ./TestLogTrans.py;

drop table if exists session_info_1;
create table session_info_1 (
    session_id string,
    app_id string,
    user_id string,
    event_timestamp bigint,
    session_start_datetime string,
    session_start_timestamp bigint,
    gap_secs int
);

-- [4] tranform and write data into session_info_1, where entries are ordered by app_id, user_id, event_timestamp ; and each entry contains a session_id ; the python tranform script finds the splits, and groups the data into sessions

insert overwrite table session_info_1
select
    transform (t.app_id, t.user_id, t.event_timestamp)
        using './TestLogTrans.py'
        as (session_id, app_id, user_id, event_timestamp, session_start_datetime, session_start_timestamp, gap_secs)
from
    (select app_id as app_id, user_id as user_id, event_timestamp as event_timestamp from session_info_0 order by app_id, user_id, event_timestamp ) t;

drop table if exists session_info_2;
create table session_info_2 (
    session_id string,
    app_id string,
    user_id string,
    session_start_datetime string,
    session_start_timestamp bigint,
    len_secs int
);

-- [5] transform and write final session data to session_info_2 ; the sessions' app + user, start time, and length in seconds

insert overwrite table session_info_2
select session_id, app_id, user_id, session_start_datetime, session_start_timestamp, sum(gap_secs)
from session_info_1
group by session_id, app_id, user_id, session_start_datetime, session_start_timestamp;

---

[TestLogTrans.py]

#!/usr/bin/python

import sys, time

def buildDateTime(ts):

    return time.strftime('%Y-%m-%d %H:%M:%S', time.gmtime(ts))

curGroup = None
prevGroup = None
curSessionStartTimestamp = None
curSessionStartDatetime = None
prevTimestamp = None

for line in sys.stdin.readlines():

    fields = line.split('\t')
    if len(fields) != 3:
        raise Exception('fields = %s', fields)

    app_id = fields[0]
    user_id = fields[1]
    event_timestamp = int(fields[2].strip())

    curGroup = '%s-%s' % (app_id, user_id)
    curTimestamp = event_timestamp

    if prevGroup == None:
        prevGroup = curGroup
        curSessionStartTimestamp = curTimestamp
        curSessionStartDatetime = buildDateTime(curSessionStartTimestamp)
        prevTimestamp = curTimestamp

    isNewGroup = (curGroup != prevGroup)

    gapSecs = 0 if isNewGroup else (curTimestamp - prevTimestamp)

    isSessionSplit = (gapSecs >= 1800)

    if isNewGroup or isSessionSplit:
        curSessionStartTimestamp = curTimestamp
        curSessionStartDatetime = buildDateTime(curSessionStartTimestamp)

    session_id = '%s-%s-%d' % (app_id, user_id, curSessionStartTimestamp)

    print '%s\t%s\t%s\t%d\t%s\t%d\t%d' % (session_id, app_id, user_id, curTimestamp, curSessionStartDatetime, curSessionStartTimestamp, gapSecs)

    prevGroup = curGroup
    prevTimestamp = curTimestamp

I think you could easily drop step 3, and put that query that you use there in as a subquery to your from clause in step 4. Physicalising that transform, doesn't appear to give you anything.

Otherwise I think for what you're trying to achieve here, this seems a reasonable approach.

Potentially step 2 you could achieve using a custom mapper, passing the output into step 4 as a custome reducer (with step 3 built in as a subquery). That will reduce you mapreduce jobs by 1, and therefore could give you a significant saving in time.

Source