SCM Dashboard
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The document discusses an SCM Dashboard that provides metrics and reports on code velocity at the product, project, and branch level. It aggregates data from sources like Perforce and displays it using a portal interface. The dashboard is built using a Hadoop and MongoDB architecture to handle large amounts of data from multiple servers in a distributed, scalable manner. MapReduce jobs in Python are used to extract metrics from the raw Perforce data and upload it to MongoDB collections for querying and display in the dashboard portal.
![Python MapReduce
• MapReduce programs are much easier to develop in a scripting
language using the Streaming API tool. Hadoop MapReduce provides
automatic parallelization and distribution, fault-tolerance, and status
and monitoring tools.
• Hadoop Streaming interacts with programs that use the Unix
streaming paradigm. Inputs come in through STDIN and outputs go to
STDOUT. The data has to be text based and each line is considered a
record. The overall data flow in Hadoop streaming is like a pipe where
data streams in through the mapper and the sorted output streams out
through the reducer. In pseudo-code using Unix’s command line
notation, it comes up as the following:
cat [input_file] | [mapper] | sort | [reducer] > [output_file]](https://image.slidesharecdn.com/prakashprismpresentation-ppt-110622131347-phpapp02/85/SCM-Dashboard-16-320.jpg)
![def dump_to_reducer(srvr, chng, depotfiles): def main():
if srvr and depotfiles and chng: depot2count = {}
for filename in depotfiles: final_changes = {}
print "%s|%st%s" % (srvr, filename, str(chng)) for line in sys.stdin:
try:
def main(): p4srvr_depotpath, date_chng = line.split('t',1)
chng, depot_files, l = 0, set(), os.linesep except:
p4srvr = site_perforce_servers(site.perforce_servers) continue
for line in sys.stdin: if (not p4srvr_depotpath) and (not date_chng):
line = line.rstrip(l) print >> sys.stderr, line
if line and line.count('/')==80: continue
srvr = match_begin_line(line, p4srvr) dt, change = date_chng.split('.')
if srvr: change = change.rstrip(l)
chng, depot_files = 0, set() depot_hash = depot2count.setdefault
continue (p4srvr_depotpath,{})
if line and line.count('%')==80: depot_hash.setdefault(dt,0)
srvr = match_end_line(line, p4srvr) chng_set = depot2count[p4srvr_depotpath][dt]
if srvr: depot2count[p4srvr_depotpath][dt] = int(change)
dump_to_reducer(srvr, chng, depot_files) for (p4srvr_depotpath, dt) in depot2count.items():
continue for (dt, chngset) in dt.items():
if line and line[0:7]=='Change ': print json.dumps
chng = dtgrep(line) ({'p4srvr_depotpath':p4srvr_depotpath, 'date': dt,
continue 'changes': chngset})
if line and line[0:6]=='... //':
flgrep(line, depot_files)
Python Python
Mapper script Reducer script](https://image.slidesharecdn.com/prakashprismpresentation-ppt-110622131347-phpapp02/85/SCM-Dashboard-18-320.jpg)
![mdb = mongo_utils.Vcs_Stats(collection_name="depot_churn")
mdb.collection.create_index([('p4srvr_depotpath', pymongo.ASCENDING), ('date',
pymongo.ASCENDING)])
for line in datafile.readlines():
data = json.loads(line)
p4srvr_depotpath = "%s" % data['p4srvr_depotpath']
dstr = data['date']
yy, mm, dd, hh, MM, ss = dstr[0:4], dstr[4:6], dstr[6:8], dstr[8:10], dstr[10:12], dstr
[12:14]
changes = data['changes']
new_data = []
mongo_data = {'p4srvr_depotpath':p4srvr_depotpath,
'date‘:datetime.datetime(yy,mm,dd,hh,MM,ss),
'changes':changes, '_id':"%s/%s:%s"%
(p4srvr_depotpath,dstr,changes)}
mdb.collection.insert(mongo_data)
mdb.collection.ensure_index([('p4srvr_depotpath', pymongo.ASCENDING), ('date',
pymongo.ASCENDING)])
mongodb
upload script](https://image.slidesharecdn.com/prakashprismpresentation-ppt-110622131347-phpapp02/85/SCM-Dashboard-19-320.jpg)
SCM Dashboard
- 1. SCM Dashboard Monitoring Code Velocity at the Product / Project / Branch level Prakash Ranade
- 2. AGENDA • What is SCM Dashboard? • Why is SCM Dashboard needed? • Where is it used? • How does it look? • Challenges in building SCM Dashboard • Goals in designing SCM Dashboard • Technology in building SCM Dashboard • Conclusion
- 3. What is SCM Dashboard? • A framework for organizing, automating, and analyzing software configuration methodologies, metrics, processes, and systems that drive product release performance. • The Dashboard gathers, organizes, and stores information from various internal data sources and displays metrics that are the result of simple or complex calculations with minimal processing time. • Decision support system that provides historical data and current trends in its portlet region, showing metrics/reports side- by-side on the same web page.
- 4. Why is SCM Dashboard needed? You are not able to manage what you can not measure. • The Dashboard is an easy way to enhance visibility on the product releases, such as showing how you do compared to previous performances, goals and benchmarks. What gets watched, will get done. • Ability to make more informed decisions based on multiple reports. Not only for the executives, but for all levels of engineering. • Release Manager, Director • Development, QA Manager, • Developer, QA
- 5. Who needs metrics? Type of files, lines, Dev change, file churn Bug fixes, # changes, Dev QA depot churn Manager Bug trends, Perforce QA Trends Manager SCM Director Dashboard Team Bug fixes, branch stability reports
- 6. How does it look?
- 7. How does it look?
- 8. Data challenges SB, TB, OB Multiple Build Systems Environments Has gone through multiple transformations. No Complex initial values were recorded. Some fields Bugzilla data have multiple values. Above 3 million changes, more than 5000 Large Perforce branches, and an archive consisting of 2 TB Repository data.
- 9. Dashboard Goals Speed Sharing Portal • Max. 5 seconds • Social Engineering • Ability to configure response time for the • Easy to share charts multiple metrics on a requests single page. and reports among • Provides frequent, or team members • Ability to fine tune at least daily, updates settings and filters on • Easy to make project • Bases project status charts and reports. dashboards on incremental data • Ability to drill downs updates and form aggregations.
- 10. Building blocks
- 11. An Architecture based on Hadoop and MongoDB • Hadoop is a open-source software used for breaking a big job into smaller tasks, performing each task and collecting the results. • MapReduce is a programming model for data processing, working by breaking the processing into two phases, a map phase and a reduce phase. • Hadoop streaming is a utility that comes with the distribution, allowing you to create and run MapReduce jobs in Python. • The HDFS is a filesystem that stores large files across multiple machines and achieves reliability by replicating the data across multiple hosts. • MongoDB is a document based database system. Each document can be thought of as a large hash object. There are keys(columns) with values which can be anything such as hashes, arrays, numbers, serialized objects, etc.
- 12. Perforce Branch: Our Perforce branch exists on multiple perforce servers. Our branch specification looks like this. • server1:1666 //depot/<component>/<old-branch>/… //depot/<component>/<new-branch>/… • server2:1666 //depot/<component2>/<old-branch>/… //depot/<component2>/<new-branch>/… //depot/<component3>/<old-branch>/… //depot/<component3>/<new-branch>/… • server3:1666 //depot/<component4>/<old-branch>/… //depot/<component4>/<new-branch>/…
- 13. Branch policies • Branch Manager identifies and lists new feature/bugs, improvements in Bugzilla and Perforce BMPS, and then sets the check-in policies on the branch and change specification forms. Change 1359870 by pranade@pranade-prism1 on 2011/04/27 17:31:36 Implement Prism View... QA Notes: Testing Done: Perforce Create, Update, delete view Bug Number: 703648, 703649 Approved by: daf Reviewed by: gaddamk, akalaveshi Review URL: https://reviewboard.eng.vmware.com/r/227466/ #You may set automerge requests to YES|NO|MANUAL below, #with at most one being set to YES. Merge to: MAIN: YES Merge to: Release: NO Affected files ... ... //depot/component-1/branch-1/views.py#12 edit ... //depot/component-1/branch-1/templates/vcs/perforce.html#15 edit ... //depot/component-1/branch-1/tests.py#1 add ... //depot/component-1/branch-1/utils.py#14 delete Differences ...
- 14. Perforce Data collection • “p4 describe” displays the details of the changeset, as follows: The changelist number The changelist creator name and workspace name The date when the changelist created The changelist’s description The submitted file lists and the code diffs • We have a Perforce data dumper script which connect to perforce servers and dumps the “p4 describe” output of the submitted changelist. • The Perforce data dumper script dumps output in 64 MB file chunks, which are then copied to HDFS.
- 15. MapReduce • We have a Perforce data dumper script which connect to perforce servers and dumps the “p4 describe” output of the submitted changelist. Each MapReduce script scans all the information from a “p4 describe” output. The following reports can be created by writing different MapReduce scripts: Number of submitted changes per depot path File information like add, edit, integrate, branch, delete File types such as “c”, “py”, “pl”, “java”, etc. Number of lines added, removed, modified Most revised files and least revised files Bug number and bug status Reviewers and test case information Change submitter names and group mapping Depot path and branch spec mapping
- 16. Python MapReduce • MapReduce programs are much easier to develop in a scripting language using the Streaming API tool. Hadoop MapReduce provides automatic parallelization and distribution, fault-tolerance, and status and monitoring tools. • Hadoop Streaming interacts with programs that use the Unix streaming paradigm. Inputs come in through STDIN and outputs go to STDOUT. The data has to be text based and each line is considered a record. The overall data flow in Hadoop streaming is like a pipe where data streams in through the mapper and the sorted output streams out through the reducer. In pseudo-code using Unix’s command line notation, it comes up as the following: cat [input_file] | [mapper] | sort | [reducer] > [output_file]
- 17. Process Combined p4 describe output from all servers in 64MBchunks Hadoop Parallelism And HDFS map Schemaless, Split files Document of p4 Storage describe map System reduce part-‐01 64 MB file size map reduce part-‐02 Changes,Lines, Files,Users, churn map metadata reduce part-‐03 Split files map • changes • p4 server A • lines p4 hadoop • MapReduce mongoDB describe • p4 server B • files • MapReduce • p4 server C • users
- 18. def dump_to_reducer(srvr, chng, depotfiles): def main(): if srvr and depotfiles and chng: depot2count = {} for filename in depotfiles: final_changes = {} print "%s|%st%s" % (srvr, filename, str(chng)) for line in sys.stdin: try: def main(): p4srvr_depotpath, date_chng = line.split('t',1) chng, depot_files, l = 0, set(), os.linesep except: p4srvr = site_perforce_servers(site.perforce_servers) continue for line in sys.stdin: if (not p4srvr_depotpath) and (not date_chng): line = line.rstrip(l) print >> sys.stderr, line if line and line.count('/')==80: continue srvr = match_begin_line(line, p4srvr) dt, change = date_chng.split('.') if srvr: change = change.rstrip(l) chng, depot_files = 0, set() depot_hash = depot2count.setdefault continue (p4srvr_depotpath,{}) if line and line.count('%')==80: depot_hash.setdefault(dt,0) srvr = match_end_line(line, p4srvr) chng_set = depot2count[p4srvr_depotpath][dt] if srvr: depot2count[p4srvr_depotpath][dt] = int(change) dump_to_reducer(srvr, chng, depot_files) for (p4srvr_depotpath, dt) in depot2count.items(): continue for (dt, chngset) in dt.items(): if line and line[0:7]=='Change ': print json.dumps chng = dtgrep(line) ({'p4srvr_depotpath':p4srvr_depotpath, 'date': dt, continue 'changes': chngset}) if line and line[0:6]=='... //': flgrep(line, depot_files) Python Python Mapper script Reducer script
- 19. mdb = mongo_utils.Vcs_Stats(collection_name="depot_churn") mdb.collection.create_index([('p4srvr_depotpath', pymongo.ASCENDING), ('date', pymongo.ASCENDING)]) for line in datafile.readlines(): data = json.loads(line) p4srvr_depotpath = "%s" % data['p4srvr_depotpath'] dstr = data['date'] yy, mm, dd, hh, MM, ss = dstr[0:4], dstr[4:6], dstr[6:8], dstr[8:10], dstr[10:12], dstr [12:14] changes = data['changes'] new_data = [] mongo_data = {'p4srvr_depotpath':p4srvr_depotpath, 'date‘:datetime.datetime(yy,mm,dd,hh,MM,ss), 'changes':changes, '_id':"%s/%s:%s"% (p4srvr_depotpath,dstr,changes)} mdb.collection.insert(mongo_data) mdb.collection.ensure_index([('p4srvr_depotpath', pymongo.ASCENDING), ('date', pymongo.ASCENDING)]) mongodb upload script
- 20. /* 0 */ { "_id": "perforce-server1:1666|//depot/component-1/branch-1/20110204005204:1290141", "date": "Thu, 03 Feb 2011 16:52:04 GMT -08:00", "p4srvr_depotpath": "perforce-server1:1666|//depot/component-1/esx41p01-hp4/", "changes": 1290141, "user": "pranade", "total_dict": { "all": "9", "branch": "9" } } /* 1 */ { "_id": "perforce-server1:1666|//depot/component-2/branch-2/20100407144638:1029666", "date": "Wed, 07 Apr 2010 07:46:38 GMT -07:00", "p4srvr_depotpath": "perforce-server1:1666|//depot/component-2/branch-2/", "changes": 1029666, "user": "akalaveshi", "total_dict": { "edit": "3", "all": "3" } } /* 2 */ { "_id": "perforce-server1:1666|//depot/component-2/branch-2/20100106003808:976075", "date": "Tue, 05 Jan 2010 16:38:08 GMT -08:00", "p4srvr_depotpath": "perforce-server1:1666|//depot/component-2/branch-2/", "changes": 976075, "user": "pranade", "total_dict": { "integrate": "10", "edit": "2", "all": "12" } mongodb data }
- 21. Conclusion • We have designed a framework called SCM Dashboard. • “p4 describe” command contains most of the information. • Hadoop: horizontally scalable computational solution. Streaming makes MapReduce programming easy. • Mongodb: Document model, dynamic queries, comprehensive data models.
- 22. QUESTIONS?