Friday, 26 March 2010

Usage Statistics parsing and querying with redis and python

This is an update of my previous dabblings with chomping through log files. To summarise where I am now:

I have a distributable workflow, loosely coordinated using Redis and Supervisord - redis is used in two fashions: firstly using its lists as queues, buffering the communication between the workers, and secondly as a store, counting and associating the usage with the items and the metadata entities (people, subjects, etc) of those items.

I have written a very small python logger, that pushes loglines directly onto a redis list, providing me with live updating abilities, as well as manual log file parsing. This is currently switched on for testing in the live repository.

Current code base is here: - it has a good number of things hardcoded to the perculiarities of my log files and repository. However, as part of the PIRUS 2 project, I am turning this into an easily reusable codebase, adding in the ability to push out OpenURLs to PIRUS statistics gatherers.


Loglines -- lpush'd to 'q:loglines'

workers - '' - pulls lines from this queue and parses them up, separating them into 4 categories:

  1. Any hit by a recognised Bot or spider

  2. Any view or download made by a real person on an item in the repository

  3. Any 404, etc

  4. And anything else

and the lines are moved onto 4 (5) queues respectively, q:bothits, q:objectviews (and q:count simultaneously), q:fof, and q:other. I am using prefixes as a convention when working with Redis keys - "q:" will almost always be a queue of some sort. These four queues are consumed by loggers, who commit the logs to disc, segregated into their categories.

The q:count queue is consumed by a further worker called - This does a number of jobs, and is the part that actually does the analysis.

For each repository item logged event, it finds the ID of the item and also whether this was a download of an item's files. With my repository, both these facts are deducible from the URL itself.

Given the ID, it checks redis to see if this item has had its metadata analysed before. If it hasn't, it grabs the metadata for the item from the repositories index (hosted by an instance of Apache Solr) and starts to add connections between metadata entity and ID to the redis index:

eg say item "pid:1" has the simple metadata of author_name='Ben' and subjects='foo, bar'

create unique IDs from the text by hashing the text and prefix it with the type of the field they came from:


  • name => "n:"

  • institution => "i:"

  • faculty => "f:"

  • subjects => "s:"

  • keyphrases => "k:"

  • content type => "type:"

  • collection => "col:"

  • thesis type => "tt:"


>>> from hashlib import md5

>>> md5("Ben").hexdigest()


So, the hashkey of the 'name' 'Ben' is 'n:092f2ba9f39fbc2876e64d12cd662f72'

Now to make the connections in Redis:

  • Add ID to the set 'objectitems' - to keep track of all the IDs (SADD objectitems {ID})

  • Set 'n:092f2....' to 'Ben' (so we can keep a reverse mapping)

  • Add 'n:092f2...' to 'names' set (to make it clearer. KEYS n:* should return an equivalent set)

  • Add 'n:092f2...' to 'e:{id}' eg "e:pid:1" - (e -> prefix for collections of entities. e:{id} is a set of all entities that occur in id)

  • Add 'e:pid:1' to 'e:n:092f2....' (gathers a list of item ids in which this entity 'Ben' occurs in)

Repeat for any entity you wish to track.

To make this more truth-manageable, you should include the id of record with the text when you generate the hashkey. That way, 'Ben' appearing in one record will have a different key than 'Ben' occuring in another. The assertion that these two entities are the same can easily take place in a different set, (I'm using b: as the prefix for these bundles of asserted equivalence)

Once you have made these assertions, you can set about counting :)

Conventions for tracking hits:

d[v|d|o]:{id} - set of the dates on which {id} was viewed (v), downloaded from (d) or any other page action (o)

eg dv:pid:1 -> set of dates on which pid:1 had page views.

YYYY-MM-DD:{id}:[v|d|o] - set of IP clients that accessed a particular item on a given day - v,d,o as above

eg 2010-02-03:pid:1:d - set of IP clients that downloaded a file from pid:1 on 2010-02-03

t:views:{hashkey}, t:dls:{hashkey}, t:other:{hashkey}

Grand totals of views, downloads or other accesses on a given entity or id. Good for quick lookups.

Let's walk through an example: consider that a client of IP visits the record page for this 'pid:1' on 2010-01-01:

ID = pid:1

Add the User Agent string ("mozilla... etc") to the 'ua:{IP}' set, to keep track of the fingerprints of the visitors.

Try to add the IP address to the set - in this case "2010-01-01:pid:1:v"

If the IP isn't already in this set (the client hasn't accessed this page already today) then:

  • make sure that "2010-01-01" is a part of the 'dv:pid:1' set

  • go through all the entities that are part of pid:1 (n:092... etc) and increment their totals by one.

    • INCR t:views:n:092...

    • INCR t:views:pid:1

Now, what about querying?

Say we wish to look up the activity on a given entity, say for 'Ben'?

First, find the hashkey(s) that exist that are equivalent - either directly using the simple md5sum hash, or by checking which bundles are for this entity.

You can get the grand totals by simply querying "t:views:key", "t:dls..." for each key and summing them together.

You can get more refined answers by getting the set of IDs that this entity is associated with, and querying that to gather all the daily IP sets for them, and summing the answer. This gives me a nice way to generate data suitable for a daily activity sparkline, like:

I have added another set of keys to the store, of the form 'geocode:{IP}' that record country code to IP address, which gives me a nice way to plot out graphs like the following also using the google chart API:

Python logging to Redis

This functionality is mainly in one file in the github repo:

As you can see, most of that file is taken up with a demonstration of how to invoke it! The file that holds the logging configuration which this demo uses is in logging.conf.example.

NB The usage analysis code and UI is very much a WIP

but, I just wanted to post quickly on the rough overview on how it is set up and working.

Thursday, 25 March 2010

Curating content from one repository to put into another

First you need a little code that I've written:

sudo easy_install recordsilo oaipmhscraper

(This should install all the dependencies for the following)

To harvest some OAI-PMH records from say... :

First, take a look at the Identify page for the OAI-PMH endpoint:

The example identifier indicates that the record identifiers start with: "" - we'll need this in a bit. Maybe not need, but it'll make the local storage more... elegant?

Go to a nice clean directory, with enough storage to handle whatever you want to harvest.

Start a python commandline:

>>> from oaipmhscraper import OAIPMHScraper

---> NB OAIPMHScraper(storage_dir, base_oai_url, identifier_uri_prefix)

>>> oaipmh = OAIPMHScraper("myrepo",

Let's have a look at what could be found out about the OAI-PMH endpoint then:

>>> oaipmh.state

{'lastidentified': '2010-03-25T15:57:15.670552', 'identify': {'deletedRecord': 'persistent', 'compression': [], 'granularity': 'YYYY-MM-DD', 'baseURL': '', 'adminEmails': [''], 'descriptions': ['........'], 'protocolVersion': '2.0', 'repositoryName': 'e-Prints Soton', 'earliestDatestamp': '0001-01-01 00:00:00'}}

>>> oaipmh.getMetadataPrefixes()

{'oai_dc': ('', ''), 'uketd_dc': ('', '')}

Let's grab all the oai_dc from all the objects:

>>> oaipmh.getRecords('oai_dc')

Go make a cup of coffee or tea.... you'll get lots of stuff like:

INFO:OAIPMH Harvester:New object: found with datestamp 2004-04-27T00:00:00 - storing.
2010-03-25 16:01:11,807 - OAIPMH Harvester - INFO - New object: found with datestamp 2005-04-22T00:00:00 - storing.
INFO:OAIPMH Harvester:New object: found with datestamp 2005-04-22T00:00:00 - storing.
2010-03-25 16:01:11,813 - OAIPMH Harvester - INFO - New object: found with datestamp 2004-04-07T00:00:00 - storing.
INFO:OAIPMH Harvester:New object: found with datestamp 2004-04-07T00:00:00 - storing.
2010-03-25 16:01:11,819 - OAIPMH Harvester - INFO - New object: found with datestamp 2004-04-07T00:00:00 - storing.
INFO:OAIPMH Harvester:New object: found with datestamp 2004-04-07T00:00:00 - storing.
2010-03-25 16:01:11,824 - OAIPMH Harvester - INFO - New object: found with datestamp 2004-04-14T00:00:00 - storing.


My advice is to hop to a different terminal window and start to poke around with the content you are getting. The underlying store is a take on the CDL's Pairtree microspec (pairtree being a minimalist specification for how to structure the access to object-orientated items on a hierarchical filesystem) This model on top of pairtree I've called a Silo (in the RecordSilo library I've written) and constitutes a basic object model, where each object has a persistent JSON state (r/w-able) and can store any file or file in a subdirectory. It has crude object-level versioning, rather than file-versioning, so you can clone one version, delete/alter/add to it to create a second, curated version for reuse elsewhere without affecting the original.

What makes pairtree attractive is that the files themselves are not altered in form, so normal posix tools can be used on the files without unwrapping, depacking, etc.

Let's have a look around at what's been harvested so far into the "myrepo" silo:

>>> from recordsilo import Silo
>>> s = Silo("myrepo")
>>> s.state
{'storage_dir': 'myrepo', 'identifier_uri_prefix': '', 'uri_base': '', 'base_oai_url': ''}'}

>>> len(s) # NB this can be a time-consuming operation
>>> len(s)

Now let's look at a record: I'm sure I saw '6102' whizz past as it was harvesting...

>>> obj = s.get_item("")

>>> obj
{'files': {'1': ['oai_dc']}, 'subdir': {'1': []}, 'versions': ['1'], 'date': '2004-06-24T00:00:00', 'currentversion': '1', 'metadata_files': {'1': ['oai_dc']}, 'item_id': '', 'version_dates': {'1': '2004-06-24T00:00:00'}, 'metadata': {'identifier': '', 'firstSeen': '2004-06-24T00:00:00', 'setSpec': ['7374617475733D707562', '7375626A656374733D51:5148:5148333031', '7375626A656374733D47:4743', '74797065733D61727469636C65', '67726F75703D756F732D686B']}}

>>> obj.files
>>> obj.versions
>>> obj.clone_version("1","workingcopy")
>>> obj.versions
['1', 'workingcopy']
>>> obj.currentversion
>>> obj.set_version_cursor("1")
>>> obj.set_version_cursor("workingcopy")
>>> obj.files
>>> with obj.get_stream("oai_dc") as oai_dc_xml:
... print
<metadata xmlns="" xmlns:xsi="">
<oai_dc:dc xmlns:xsi="" xmlns:oai_dc="" xmlns:dc="" xsi:schemaLocation="">
<dc:title>Population biology of Hirondellea sp. nov. (Amphipoda: Gammaridea: Lysianassoidea) from the Atacama Trench (south-east Pacific Ocean)</dc:title>
<dc:creator>Perrone, F.M.</dc:creator>
<dc:creator>Dell'Anno, A.</dc:creator>
<dc:creator>Danovaro, R.</dc:creator>
<dc:creator>Groce, N.D.</dc:creator>
<dc:creator>Thurston, M.H.</dc:creator>
<dc:subject>QH301 Biology</dc:subject>
<dc:subject>GC Oceanography</dc:subject>

You can add bytestreams as strings:

>>> obj.put_stream("foo.txt", "Some random text!")

or as file-like objects:

>>> with open("README", "r") as readmefile:
... obj.put_stream("README", readmefile)
>>> obj.files
['oai_dc', 'foo.txt', 'README']
>>> obj.set_version_cursor("1")
>>> obj.files

This isn't the easiest way to browse or poke around the files. It would be nice to see these through a web UI of some kind:

Grab the basic UI code from

(You'll need to install and Mako: sudo easy_install mako

Then edit the file to point to the location of the Silo - if the directory structure looks like the following:

--- Silo directory stuff...

You need to change data_dir to "../myrepo" and then you can start the server by running 'python'

Point a browser at http://localhost:8080/ and wait a while - that start page loads *every* object in the Silo.

And let's revisit our altered record, at http://localhost:8080/
So, from this point, I can curate the records as I wish, add files to each item - perhaps licences, PREMIS files, etc - and then push them onto another repository, such as Fedora.

Thursday, 11 February 2010

My swiss army toolkit for distributed/multiprocessing systems

My first confession - I avoid 'threading' and shared memory. Avoid it like the plague, not because I cannot do it but because it can be a complete pain to build and maintain relative to the alternatives.

I am very much pro multiprocessing versus multithreading - obviously, there are times when threading is by far the best choice, but I've found multiprocessing for the most part it to be quicker, easier and far easier to log, manage and debug than multithreading.

So, what do I mean by a 'multiprocessing' system? (just to be clear)

A multiprocessing system consists of many concurrently running processes running on one or more machines, and contains some means to distribute messages and persist data between these processes.

This does not mean that the individual processes cannot multithread themselves, it is just that each process handles a small, well-defined aspect of the system (paralleling the unix commandline tool idiom).

Tools for multiprocess management:

  • Redis - data structure server, providing atomic operations on integers, lists, sets, and sorted lists.
  • RabbitMQ - messaging server, based on the AMQP spec. IMO Much cleaner, easier to manage, more flexible and more reliable than all the JMS systems I've used.
  • Supervisor - a battle-tested, process manager that can be operated via XML-RPC or HTTP. Enables live control and status of your processes.
Redis has become my swiss army knife of data munging - a store that persists data and which has some very useful atomic operations, such as integer incrementing, list manipulations and very fast set operations. I've also used it for some quick-n-dirty process orchestrations (which is how I've used it in the example that ends this post.)

I've also used it for usage statistic parsing and characterisation of miscellaneous XML files too!

RabbitMQ - a dependable, fast message server which I am primarily using as a buffer for asynchronous operations and task distribution. More boilerplate to use than, say Redis, but by far more suited for that sort of thing.

Supervisord - I've been told that the ruby project 'god' is similar to this - I really have found it very useful, especially on those systems I run remotely. An HTML page to control processes and view logs and stats? what's not to like!

Now for a little illustration of a simple multiprocessing solution - in fact, this blog post far, far outweighs the code written and perhaps even overeggs the simple nature of the problem. I typically wouldn't use supervisor for a simple task like the following, but it seems a suitable example to show how to work it.

The ability to asynchronously deliver messages, updates and tasks between your processes is a real boon - it enables quick solutions to normally vexing or time-consuming problems. For example, let's look at a trivial problem of how to harvest the content from a repository with an OAI-PMH service:

A possible solution needs:
  • a process to communicate with the OAI-PMH service to gain the list of identifiers for the items in the repository (with the ability to update itself at a later time). Including the ability to find the serialised form of the full metadata for the item, if it cannot be gotten from the OAI-PMH service (eg Eprints3 XML isn't often included in the OAI-PMH service, but can be retrieved from the Export function.),
  • a process that simply downloads files to a point on the disc,
  • and a service that allows process one to queue jobs for process 2 to download - in this case Redis.
I told you it would be trivial :)

Installing Redis: (See for fuller instructions)
  • sudo apt-get install build-essential python-dev python-setuptools [make sure you can build and use easy_install - here shown for debian/ubuntu/etc]
  • sudo easy_install supervisor
  • mkdir oaipmh_directory # A directory to contain all the bits you need
  • cd oaipmh_directory
Create a supervisor configuration for the task at hand and save it as supervisord.conf.
autorestart = false
numprocs = 1
autostart = false
redirect_stderr = True
stopwaitsecs = 10
startsecs = 10
priority = 10
command = python
startretries = 3
stdout_logfile = workerlogs/harvest.log

autorestart = true
numprocs = 1
autostart = false
redirect_stderr = True
stopwaitsecs = 10
startsecs = 10
priority = 999
command = oaipmh_file_downloader q:download_list
startretries = 3
stdout_logfile = workerlogs/download.log

autorestart = true
numprocs = 1
autostart = true
redirect_stderr = True
stopwaitsecs = 10
startsecs = 10
priority = 999
command = path/to/the/redis-server
startretries = 3
stdout_logfile = workerlogs/redis.log

file = /tmp/supervisor.sock

minfds = 1024
minprocs = 200
loglevel = info
logfile = /tmp/supervisord.log
logfile_maxbytes = 50MB
nodaemon = false
pidfile = /tmp/
logfile_backups = 10

serverurl = unix:///tmp/supervisor.sock

supervisor.rpcinterface_factory = supervisor.rpcinterface:make_main_rpcinterface

username = guest
password = mypassword
port =

This has a lot of boilerplate on it, so let's go through it, section by section:

[program:redis] - this controls the redis program. You will need to change the path to the redis server to wherever it was built on your system - eg ~/redis-1.2.1/redis-server

[program:oaipmhgrabber] and [program:downloader] - these set up the processes, look at the 'command' key for the command that is run for them eg downloader has "oaipmh_file_downloader q:download_list" - The OAIPMHScraper package adds in the script, 'q:download_list' is the redis-based list that the download tasks appear on. NB we haven't written yet - don't worry!

NB very important that autorestart=false in [program:oaipmhgrabber] - if it didn't, it would eternally repeat - on and on and on - harvesting!

Supervisor boilerplate: [unix_http_server], [supervisord], [supervisorctl]

RPC interface control [rpcinterface:supervisor]

HTTP interface control - [inet_http_server] - which includes importantly the username and password to log in to the control panel!

Now, create the log directory:

mkdir workerlogs

Let's now write '': PLEASE use a different OAI2 endpoint url!
#!/usr/bin/env python

from oaipmhscraper import Eprints3Harvester

o = Eprints3Harvester("repo", base_oai_url="")

[Note there is a base OAIPMHScraper class, but this simply goes and gets the metadata or Identifiers for a given endpoint and stores whatever XML metadata it gets into a store. The Eprints3 harvester gets the files as well, or tries to.]

You may have to change the template for other eprints repositories - the above template would result in the following for item 774:


YMMV for other repositories of course, so you can rewrite this template accordingly.

Your directory should look like this:

--> supervisord.conf workerlogs/

Let's start the supervisor to make the configuration is correct:

[---] $ supervisord -c supervisord.conf
[---] $

Now open http://localhost:9001/ - it should look like the following:

Click on the 'redis' name to see the logfile that this is generating - you'll want to see lines like:

11 Feb 13:34:32 . 0 clients connected (0 slaves), 2517 bytes in use, 0 shared objects

Let's start the harvest :)

Click on 'start' for the oaipmh grabber process and wait - in the configuration file, we told it to wait for the process to stay up for 10 seconds before reporting that it was running, so it should take about that long for the page to refresh.

Now, let's see what it is putting onto the queue, before we start the download process (see, easy to debug!)

>>> from redis import Redis
>>> r = Redis()
>>> r.keys("*")
>>> r.llen("q:download_list")
>>> r.llen("q:download_list")
>>> r.llen("q:download_list")
>>> # Still accruing things to download as we speak...
>>> r.lrange("q:download_list", 0,0)
[u'{"url": "", "filename": "XML", "pid": "", "silo": "repo"}']

Now, let's switch on the downloader and work on those messages - go back to http://localhost:9001 and start the downloader. Click on the downloader name when the page refreshes to get a 'tail' of it's logfile in the browser.

You should get something like the following:
INFO:CombineHarvester File downloader:Starting download of XML (from to object
2010-02-11 13:43:51,284 - CombineHarvester File downloader - INFO - Download completed in 0 seconds
INFO:CombineHarvester File downloader:Download completed in 0 seconds
2010-02-11 13:43:51,285 - CombineHarvester File downloader - INFO - Saving to Silo repo
INFO:CombineHarvester File downloader:Saving to Silo repo
2010-02-11 13:43:51,287 - CombineHarvester File downloader - INFO - Starting download of XML (from to object
INFO:CombineHarvester File downloader:Starting download of XML (from to object
So, that will go about and download all the XML (Eprints3 XML) for each item it found in the repository. (I haven't put in much to stop dupe downloads etc. - exercise for the reader ;))

How about we try to download the files for each item too? I just so happens I've included a little Eprints3 XML parser and method for queuing up the files for download 'reprocessRecords' - let's use this to download the files now - save as

#!/usr/bin/env python

from oaipmhscraper import Eprints3Harvester

o = Eprints3Harvester("repo", base_oai_url="")

Add this process to the top of the supervisord.conf file:
autorestart = false
numprocs = 1
autostart = false
redirect_stderr = True
stopwaitsecs = 10
startsecs = 10
priority = 999
command = python
startretries = 3
stdout_logfile = workerlogs/download_files.log

Now, to demonstrate the commandline supervisor controller:
[--] $ supervisorctl
$ supervisorctl
downloader RUNNING pid 20750, uptime 0:15:41
oaipmhgrabber STOPPED Feb 11 01:58 PM
redis RUNNING pid 16291, uptime 0:25:31
supervisor> shutdown
Really shut the remote supervisord process down y/N? y
Shut down
(Press Ctrl+D to leave this terminal)

Now restart the supervisor:
[--] $ supervisord -c supervisord.conf

And refresh http://localhost:9001/

[NB in the following picture, I reran oaipmhgrabber, so you could see what the status of a normally exiting process looks like]

Now, switch on the reprocess record worker and tail -f the downloader if you want to watch it work :)

What's a RecordSilo? (aka How things are stored in the example)

This class is based on CDL's spec for Pairtree object storage - each object contains a JSON manifest and is made up of object-level versions. But, it is easier to understand if you have some kind of GUI to poke around with, so I quickly wrote the following server for that end:

Grab the dropbox code and templates from - unpack it into the same directory as you are in now.

so that:

[--] $ ls dump.rdb repo supervisord.conf templates workerlogs

Edit and change the data_dir to equal your repo directory name - in this case, just "repo"

(Make sure you have mako and installed too! sudo easy_install mako


$ python

Go to http://localhost:8080/ to then see all your objects! This page opens them all, so could take a while :)

(I did this on my work computer and may have not put in some dependencies, etc but it worked for me. Let me know if it doesn't in the comments)

Monday, 18 January 2010

Usage stats and Redis

Redis has been such a massively useful tool to me.

Recently, it has let me cut through access logs munging like a hot knife through butter, all with multiprocessing goodness.

Key things:

Using sets to manage botlists:

>>> from redis import Redis
>>> r = Redis()
>>> for bot in r.smembers("botlist"):
... print bot
>>> total = 0
>>> for bot in r.smembers("botlist"):
... total = total + r.scard(bot)
>>> total

So, I have 3882 different IP addresses that I have built up that I consider bots.

Keeping counts and avoiding race-conditions

By using the Redis INCR command, it's easy to write little workers that run in their own process but which atomically increment counts of hits.

What does the stat system look like?

I am treating each line of the Apache-style log as a message that I am passing through a number of workers.


All in the same AMQP exchange: ("stats")

Queue "loglines" - msg's = A single log line in the Apache format. Can be sourced from either local logs or from the live service.

loglines is listened to by a worker, just one at the moment. This worker feeds three queues:

Queue "bothits" - log lines from a request that matches a bot IP

Queue "objectviews" - log lines from a request that was a record page view or item download

Queue "other" - log lines that I am presently not so interested in.

[These three queues are consumed by 3 loggers and these maintain a copy of the logs, pre-separated. These are designed to be temporary parts of the workflow, to be discarded once we know what we want from the logs.]

objectviews is subscribed to by a worker which does the heavy crunching as shown below.

The first worker is '' - this does the broad separation and checking of a logged event. In essence, it uses the Redis SISMEMBER command to see if the IP address is in the blacklists and if not, applies a few regex's to see if it is a record view and/or a download or something else.

Broad Logging

There are three logger workers that feeds for "bothits", "objectviews", and "other" - these workers just sit and listen on the relevant queue for an apache log line and appends it to the logfile it has open. Saves me having to open/close logger objects or pass anything around.

The logfiles are purely as a record of the processing and so I can skip redoing it if I want to do any further analysis, like tracking individuals, etc.

The loggers also INCR a key in Redis for each line they see - u:objectviews, u:bothits, and u:other as appropriate - these give me a rough idea of how the processing is going.

(And you can generate pretty charts from it too:),9760660&chd=t:368744,9760660,1669552&chs=600x200&chl=Views|Bots|Other

(data sourced at a point during the processing - 10million bot hits vs 360k object views/dls)

Counting hits (metadata and time based)

Most of the heavy lifting is in - this is fed from the object views/downloads stream coming from the worker. It does a number of procedural steps for the metadata:

  • Get metadata from ORA's Solr endpoint (as JSON)
    • Specifically, get the 'authors' (names), subjects/keyphrases, institutions, content types, and collections things appear in.
    • These fields correspond to certain keys in Redis. Eg names = 'number:names' = number of unique names, 'n:...' = hits to a given name, etc
  • For each view/dl:
    • INCR 'ids:XXXXX' where XXXXX is 'names', 'subjects', etc. It'll return the new value for this, eg 142
    • SET X:142 to be equal to the text for this new entity, where X is the prefix for the field.
    • SADD this id (eg X:142) to the relevant set for it, like 'names', 'subjects', etc - This is so we can have an accurate idea of the entities in use even after removing/merging them.
    • Reverse lookup: Hash the text for the entity (eg md5("John F. Smith")) and SET r:X:{hash} to be equal to "X:142"
    • SET X:views:142 to be equal to 1 to get the ball rolling (or X:dl:142 for downloads)
  • If the name is not new:
    • Hash the text and lookup r:{hash} to get the id (eg n:132)
    • INCR the item's counter (eg INCR n:views:132)
  • Time-based and other counts:
    • INCR t:{object id} (total hits on that repository object since logs began)
    • INCR t:MMYY (total 'proper' hits for that month)
    • INCR t:MMYY:{object id} (total 'proper' hits for that repo item that month)
    • INCR t:MMYY:{entity id} (Total hits for an entity, say 'n:132' that month)

A lot of pressure is put on Redis by but it seems to be coping fine. A note for anyone else thinking about this: Redis keeps its datastore in RAM - running out of RAM is a Bad Thing(tm).

I know that I could also just use the md5 hashes as ids, rather than using a second id - I'm still developing this section and this outline just states it how it is now!

Also, it's worth noting that if I needed to, I can put remote redis 'shards' on other machines and they can just pull log lines from the main objectview queue to process. (It'll still need to create the id <-> entity name mapping on the main store though or a slave of the main store.)

But why did I do this?

I thought that it would mean I could handle both legacy logs and live data and have a framework I could put against other systems and in a way that would mean I would write less code and for the system to be more reliable.

So far, I still think this is the case. If people are interested, I'll abstract out a class or two (eg the metadata lookup function, etc) and stick it on google code. It's not really a lot of code so far, I think even this outline post is longer....