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Best practices for organizing your Redis data structure in the code

So I'm moving from PostgreSQL to Redis as main database in my project to get the best performance. Now my code contains a lot of HSET, HMSET, HGET, SADD, SREM, SINTER, SORT and other calls to Redis spread everywhere throughout the code. We don't have any ORM with Redis nor any data model description. While I have the structure of my data in my mind, it's not a big problem for me to maintain this code. But once another dev needs to work with it, or even me after just a year, it will become a huge pain to get it all together and be sure you don't miss anything.
Thus my question. What are the best practices for organizing the data-storage code without ORMs and proper data models? I see a lot of articles on how to call Redis here and there to store data in it, but no recommendations whatsoever on how to do it without making your code a mess.

Ended up using rom: https://github.com/josiahcarlson/rom
It allowed me to describe models clearly and use active record to work with them. Takes care of indexes and stuff under the hood, quite simple to use and nice

Using Excel to work with large amounts of output data: is an Excel-database interaction the right solution for the problem?

I have a situation where various analysis programs output large amounts of data, but I may only need to manipulate or access certain parts of the data in a particular Excel workbook.
The numbers might often change as well as newer analyses are run, and I'd like these changes to be reflected in Excel in as automated a manner as possible. Another important consideration is that I'm using Python to process some of the data too, so putting the data somewhere where it's easy for Python and Excel to access would be very beneficial.
I know only a little about databases, but I'm wondering if using one would be a good solution for what my needs - Excel has database interaction capability as far as I'm aware, as does Python. The devil is in the details of course, so I need some help figuring out what system I'd actually set up.
From what I've currently read (in the last hour), here's what I've come up with so far simple plan:
1) Set up an SQLite managed database. Why SQLite? Well, I don't need a database that can manage large volumes of concurrent accesses, but I do need something that is simple to set up, easy to maintain and good enough for use by 3-4 people at most. I can also use the SQLite Administrator to help design the database files.
2 a) Use ODBC/ADO.NET (I have yet to figure out the difference between the two) to help Excel access the database. This is going to be the trickiest part, I think.
2 b) Python already has the built in sqlite3 module, so no worries with the interface there. I can use it to set up the output data into an SQLite managed database as well!
Putting down some concrete questions:
1) Is a server-less database a good solution for managing my data given my access requirements? If not, I'd appreciate alternative suggestions. Suggested reading? Things worth looking at?
2) Excel-SQLite interaction: I could do with some help flushing out the details there...ODBC or ADO.NET? Pointers to some good tutorials? etc.
3) Last, but not least, and definitely of concern: will it be easy enough to teach a non-programmer how to setup spreadsheets using queries to the database (assuming they're willing to put in some time with familiarization, but not very much)?
I think that about covers it for now, thank you for your time!

Although you could certainly use a database to do what you're asking, I'm not sure you really want to add that complexity. I don't see much benefit of adding a database to your mix. ...if you were pulling data from a database as well, then it'd make more sense to add some tables for this & use it.
From what I currently understand of your requirements, since you're using python anyway, you could do your preprocessing in python, then just dump out the processed/augmented values into other csv files for Excel to import. For a more automated solution, you could even write the results directly to the spreadsheets from Python using something like xlwt.

MySql bulk import without writing a file to disk

I have a django site running with a mysql database backend. I accept fairly large uploads from one of the admin users to bulk import some data. The data comes in a format that is slightly different than the form it needs to be in the database so I need to do a little parsing.
I'd like to be able to pivot this data into csv and write it into a cStringIO object then simply use mysql's bulk import command to load that file. I'd prefer to skip writing the file to the disk first, but I can't seem to find a way around it. I've done basically this exact thing with postgresql in the past, but unfortunately this project is on mysql.
The short: Can I take an in memory file like object and somehow use mysql bulk import operation

There is an excellent tutorial called Generator Tricks for Systems Programmers that addresses processing large log files, which is similar, but not identical, to your situation. As long as you can perform the needed transform with access to only the current (and possibly previous) data in the stream, this may work for you.
I have mentioned this gem in a number of answers because I think that it introduces a different way of thinking that can be quite valuable. There is a companion piece, A Curious Course on Coroutines and Concurrency, that can seriously twist your head around.

If by "bulk import" you mean LOAD DATA [LOCAL] INFILE then, no, there's no way around first writing the data to some file, damn it all. You (and I) would really like to write the table directly from an array.
But some OSs, like Linux, allow a RAM-resident filesystem that eases some of the hurt. I'm not enough of a sysadmin to know how to set up one of these guys; I had to get my ISP's tech support to do it for me. I found an article that might have useful info.
HTH

Using Python to generate JSON Data

I'm working on a little project, the aim being to generate a report from a database for a server. The database is SQLite and contains tables like 'connections', 'downloads', etc.
The report I produce will ultimately contain a number of graphs displaying things like 'connections per day', 'top downloads this month', etc.
I plan to use flot for the graphs because the graphs it makes look very nice:
This is my current plan for how my reports will work:
Static .HTML file which is the report. This will contain headings, embedded flot graphs, etc.
JSON Data file. These will be generated by my report generation python script, they will basically contain a JSON variable for each graph representing the dataset that the graph should map. ([100,2009-2-2],[192,2009-2-3]...)
Report generation python script, this will load the SQLite database, run a list of set SQL queries and spit out the JSON Data files.
Does this sound like a sensible set up? I can't help but feel it could be improved but I don't see how. I want the reports to be static. The server they run on cannot take heavy loads so a dynamically generated report is out of the question and also unnecessary for this application.
My concerns are:
I feel that the Python script is largely pointless, all of the processing performed is done by SQLite, my script is basically going to be used to store SQL queries and package up the output. With a bit more work SQLite could probably do this for me.
It seems I'm solving a problem that must have been solved many times before 'take sql queries, spit out pretty graphs in a daily report' must have been done hundreds of times. I'm just having trouble tracking down any broad implementations.

It sounds sensible to me.
You need some programming language to talk to SQLite. You could do it in C, but if you can write the necessary glue code easily in Python, why not? You'll almost certainly save more time writing it than you'll lose from not having the most efficient possible program.
There are definitely programs to analyse logs for you - I've heard of Piwik, for instance. That's for dynamic reports, but no doubt there are projects to do static reports too. But they may not fit the data and output you're after. Writing it yourself means you know precisely what you're getting, so if it's not too much work, carry on.

I feel that the Python script is largely pointless, all of the processing performed is done by SQLite, my script is basically going to be used to store SQL queries and package up the output. With a bit more work SQLite could probably do this for me.
Maybe so, but even then, Python is a great glue language. Also, if you need to do some processing SQLite isn't good at, Python is already there.
It seems I'm solving a problem that must have been solved many times before 'take sql queries, spit out pretty graphs in a daily report' must have been done hundreds of times. I'm just having trouble tracking down any broad implementations.
I think you're leaning towards the general class of HTTP-served reporting. One thing out there that overlaps your problem set is Django, which provides a Python interface between database (SQLite is supported) and web server, along with a templating system for your outputs.
If you just want one or two pieces of a solution, then I recommend looking at SQLAlchemy for interfacing with the database, Jinja2 for templating, and/or Werkzeug for HTTP server interface.

Best DataMining Database

I am an occasional Python programer who only have worked so far with MYSQL or SQLITE databases. I am the computer person for everything in a small company and I have been started a new project where I think it is about time to try new databases.
Sales department makes a CSV dump every week and I need to make a small scripting application that allow people form other departments mixing the information, mostly linking the records. I have all this solved, my problem is the speed, I am using just plain text files for all this and unsurprisingly it is very slow.
I thought about using mysql, but then I need installing mysql in every desktop, sqlite is easier, but it is very slow. I do not need a full relational database, just some way of play with big amounts of data in a decent time.
Update: I think I was not being very detailed about my database usage thus explaining my problem badly. I am working reading all the data ~900 Megas or more from a csv into a Python dictionary then working with it. My problem is storing and mostly reading the data quickly.
Many thanks!

Quick Summary
You need enough memory(RAM) to solve your problem efficiently. I think you should upgrade memory?? When reading the excellent High Scalability Blog you will notice that for big sites to solve there problem efficiently they store the complete problem set in memory.
You do need a central database solution. I don't think hand doing this with python dictionary's only will get the job done.
How to solve "your problem" depends on your "query's". What I would try to do first is put your data in elastic-search(see below) and query the database(see how it performs). I think this is the easiest way to tackle your problem. But as you can read below there are a lot of ways to tackle your problem.
We know:
You used python as your program language.
Your database is ~900MB (I think that's pretty large, but absolute manageable).
You have loaded all the data in a python dictionary. Here I am assume the problem lays. Python tries to store the dictionary(also python dictionary's aren't the most memory friendly) in your memory, but you don't have enough memory(How much memory do you have????). When that happens you are going to have a lot of Virtual Memory. When you attempt to read the dictionary you are constantly swapping data from you disc into memory. This swapping causes "Trashing". I am assuming that your computer does not have enough Ram. If true then I would first upgrade your memory with at least 2 Gigabytes extra RAM. When your problem set is able to fit in memory solving the problem is going to be a lot faster. I opened my computer architecture book where it(The memory hierarchy) says that main memory access time is about 40-80ns while disc memory access time is 5 ms. That is a BIG difference.
Missing information
Do you have a central server. You should use/have a server.
What kind of architecture does your server have? Linux/Unix/Windows/Mac OSX? In my opinion your server should have linux/Unix/Mac OSX architecture.
How much memory does your server have?
Could you specify your data set(CSV) a little better.
What kind of data mining are you doing? Do you need full-text-search capabilities? I am not assuming you are doing any complicated (SQL) query's. Performing that task with only python dictionary's will be a complicated problem. Could you formalize the query's that you would like to perform? For example:
"get all users who work for departement x"
"get all sales from user x"
Database needed
I am the computer person for
everything in a small company and I
have been started a new project where
I think it is about time to try new
databases.
You are sure right that you need a database to solve your problem. Doing that yourself only using python dictionary's is difficult. Especially when your problem set can't fit in memory.
MySQL
I thought about using mysql, but then
I need installing mysql in every
desktop, sqlite is easier, but it is
very slow. I do not need a full
relational database, just some way of
play with big amounts of data in a
decent time.
A centralized(Client-server architecture) database is exactly what you need to solve your problem. Let all the users access the database from 1 PC which you manage. You can use MySQL to solve your problem.
Tokyo Tyrant
You could also use Tokyo Tyrant to store all your data. Tokyo Tyrant is pretty fast and it does not have to be stored in RAM. It handles getting data a more efficient(instead of using python dictionary's). However if your problem can completely fit in Memory I think you should have look at Redis(below).
Redis:
You could for example use Redis(quick start in 5 minutes)(Redis is extremely fast) to store all sales in memory. Redis is extremely powerful and can do this kind of queries insanely fast. The only problem with Redis is that it has to fit completely in RAM, but I believe he is working on that(nightly build already supports it). Also like I already said previously solving your problem set completely from memory is how big sites solve there problem in a timely manner.
Document stores
This article tries to evaluate kv-stores with document stores like couchdb/riak/mongodb. These stores are better capable of searching(a little slower then KV stores), but aren't good at full-text-search.
Full-text-search
If you want to do full-text-search queries you could like at:
elasticsearch(videos): When I saw the video demonstration of elasticsearch it looked pretty cool. You could try put(post simple json) your data in elasticsearch and see how fast it is. I am following elastissearch on github and the author is commiting a lot of new code to it.
solr(tutorial): A lot of big companies are using solr(github, digg) to power there search. They got a big boost going from MySQL full-text search to solr.

You probably do need a full relational DBMS, if not right now, very soon. If you start now while your problems and data are simple and straightforward then when they become complex and difficult you will have plenty of experience with at least one DBMS to help you. You probably don't need MySQL on all desktops, you might install it on a server for example and feed data out over your network, but you perhaps need to provide more information about your requirements, toolset and equipment to get better suggestions.
And, while the other DBMSes have their strengths and weaknesses too, there's nothing wrong with MySQL for large and complex databases. I don't know enough about SQLite to comment knowledgeably about it.
EDIT: #Eric from your comments to my answer and the other answers I form even more strongly the view that it is time you moved to a database. I'm not surprised that trying to do database operations on a 900MB Python dictionary is slow. I think you have to first convince yourself, then your management, that you have reached the limits of what your current toolset can cope with, and that future developments are threatened unless you rethink matters.
If your network really can't support a server-based database than (a) you really need to make your network robust, reliable and performant enough for such a purpose, but (b) if that is not an option, or not an early option, you should be thinking along the lines of a central database server passing out digests/extracts/reports to other users, rather than simultaneous, full RDBMS working in a client-server configuration.
The problems you are currently experiencing are problems of not having the right tools for the job. They are only going to get worse. I wish I could suggest a magic way in which this is not the case, but I can't and I don't think anyone else will.

Have you done any bench marking to confirm that it is the text files that are slowing you down? If you haven't, there's a good chance that tweaking some other part of the code will speed things up so that it's fast enough.

It sounds like each department has their own feudal database, and this implies a lot of unnecessary redundancy and inefficiency.
Instead of transferring hundreds of megabytes to everyone across your network, why not keep your data in MySQL and have the departments upload their data to the database, where it can be normalized and accessible by everyone?
As your organization grows, having completely different departmental databases that are unaware of each other, and contain potentially redundant or conflicting data, is going to become very painful.

Does the machine this process runs on have sufficient memory and bandwidth to handle this efficiently? Putting MySQL on a slow machine and recoding the tool to use MySQL rather than text files could potentially be far more costly than simply adding memory or upgrading the machine.

Here is a performance benchmark of different database suits ->
Database Speed Comparison
I'm not sure how objective the above comparison is though, seeing as it's hosted on sqlite.org. Sqlite only seems to be a bit slower when dropping tables, otherwise you shouldn't have any problems using it. Both sqlite and mysql seem to have their own strengths and weaknesses, in some tests the one is faster then the other, in other tests, the reverse is true.
If you've been experiencing lower then expected performance, perhaps it is not sqlite that is the causing this, have you done any profiling or otherwise to make sure nothing else is causing your program to misbehave?
EDIT: Updated with a link to a slightly more recent speed comparison.

It has been a couple of months since I posted this question and I wanted to let you all know how I solved this problem. I am using Berkeley DB with the module bsddb instead loading all the data in a Python dictionary. I am not fully happy, but my users are.
My next step is trying to get a shared server with redis, but unless users starts complaining about speed, I doubt I will get it.
Many thanks everybody who helped here, and I hope this question and answers are useful to somebody else.

If you have that problem with a CSV file, maybe you can just pickle the dictionary and generate a pickle "binary" file with pickle.HIGHEST_PROTOCOL option. It can be faster to read and you get a smaller file. You can load the CSV file once and then generate the pickled file, allowing faster load in next accesses.
Anyway, with 900 Mb of information, you're going to deal with some time loading it in memory. Another approach is not loading it on one step on memory, but load only the information when needed, maybe making different files by date, or any other category (company, type, etc..)

Take a look at mongodb.