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What's the best strategy for dumping very large python dictionaries to a database?

I'm writing something that essentially refines and reports various strings out of an enormous python dictionary (the source file for the dictionary is XML over a million lines long).
I found mongodb yesterday and was delighted to see that it accepts python dictionaries easy as you please... until it refused mine because the dict object is larger than the BSON size limit of 16MB.
I looked at GridFS for a sec, but that won't accept any python object that doesn't have a .read attribute.
Over time, this program will acquire many of these mega dictionaries; I'd like to dump each into a database so that at some point I can compare values between them.
What's the best way to handle this? I'm awfully new to all of this but that's fine with me :) It seems that a NoSQL approach is best; the structure of these is generally known but can change without notice. Schemas would be nightmarish here.

Have your considered using Pandas? Yes Pandas does not natively accept xmls but if you use ElementTree from xml (standard library) you should be able to read it into a Pandas data frame and do what you need with it including refining strings and adding more data to the data frame as you get it.

So I've decided that this problem is more of a data design problem than a python situation. I'm trying to load a lot of unstructured data into a database when I probably only need 10% of it. I've decided to save the refined xml dictionary as a pickle on a shared filesystem for cool storage and use mongo to store the refined queries I want from the dictionary.
That'll reduce their size from 22MB to 100K.
Thanks for chatting with me about this :)

Extract webscraped python data to SQLite, excel or xml?

I'm kinda new to Python and webscraping but I'm currently at a point where I need to extract data to a database. Can someone tell me the pros and cons by using sqlite, excel or xml?
I've read that sqlite should be the fastest, so I may go for that database structure, but can someone then tell me what IDE you use to handle sqlite data after I've extracted it from python?
Edit: I hope my post makes sense. I'm currently trying to use a web scraper from here: https://github.com/gingeleski/odds-portal-scraper
Thanks in advance.

For the short term, Excel is a good way to examine your data and prototype analysis and visualizations. It gets old using it for very large datasets, or multiple similar datasets. Basically as soon as you start doing the same thing more than twice or writing VB code you should switch to the pandas/matplotlib solution.
It looks like the scraper you are using already puts the results in an SQLITE database, but if you have your data in a list or dictionary, I'd suggest using pandas to do calculations and matplotlib for visualizations, as that will give you a robust, extensible solution over the long term. It is very easy to read and write data between an SQLITE database and pandas.
A good way of viewing the data in the DB is a must. I'm currently using SQLiteStudio.

When you say IDE, I'm assuming you're looking for a way to view the SQLite data? If so, DBeaver is a free, open source SQL client. You could use this to view the data quite easily.

Storing and querying a large amount of data

I have a large amount of data around 50GB worth in a csv which i want to analyse purposes of ML. It is however way to large to fit in Python. I ideally want to use mySQL because querying is easier. Can anyone offer a host of tips for me to look into. This can be anything from:
How to store it in the first place, i realise i probably can't load it in all at once, would i do it iteratively? If so what things can i look into for this? In addition i've heard about indexing, would that really speed up queries on such a massive data set?
Are there better technologies out there to handle this amount of data and still be able to query and do feature engineering quickly. What i eventually feed into my algorithm should be able to be done in Python but i need query and do some feature engineering before i get my data set that is ready to be analysed.
I'd really appreciate any advice this all needs to be done on personal computer! Thanks!!

Can anyone offer a host of tips for me to look into
Gladly!
Look at the CSV file first line to see if there is a header. You'd need to create a table with the same fields (and type of data)
One of the fields might seem unique per line and can be used later to find the line. That's your candidate for PRIMARY KEY. Otherwise add an AUTO-INCREMENT field as PRIMARY KEY
INDEXes are used to later search for data. Whatever fields you feel you will be searching/filtering on later should have some sort of INDEX. You can always add them later.
INDEXes can combine multiple fields if they are often searched together
In order to read in the data, you have 2 ways:
Use LOAD DATA INFILE Load Data Infile Documentation
Write your own script: The best technique is to create a prepared statement for the
INSERT command. Then read your CSV line by line (in a loop), split the fields
into variables and execute the prepared statement with this line's
values
You will benefit from a web page designed to search the data. Depends on who needs to use it.
Hope this gives you some ideas

That's depend on what you have, you can use Apache spark and then use their SQL feature, spark SQL gives you the possibility to write SQL queries in your dataset, but for best performance you need a distributed mode(you can use it in a local machine but the result is limited) and high machine performance. you can use python, scala, java to write your code.

Use Python to load data into Mysql

is it possible to set up tables for Mysql in Python?
Here's my problem, I have bunch of .txt files which I want to load into Mysql database. Instead of creating tables in phpmyadmin manually, is it possible to do the following things all in Python?
Create table, including data type definition.
Load many files one by one. I only know this LOAD DATA LOCAL INFILE command to load one file.
Many thanks

Yes, it is possible, you'll need to read the data from the CSV files using CSV module.
http://docs.python.org/library/csv.html
And the inject the data using Python MySQL binding. Here is a good starter tutorial:
http://zetcode.com/databases/mysqlpythontutorial/
If you already know python it will be easy

It is. Typically what you want to do is use an Object-Retlational Mapping library.
Probably the most widely used in the python ecosystem is SQLAlchemy, but there is a lot of magic going on in it, so if you want to keep a tighter control on your DB schema, or if you are learning about relational DB's and want to follow along what the code does, you might be better off with something lighter like Canonical's storm.
EDIT: Just thought to add. The reason to use ORM's is that they provide a very handy way to manipulate data / interface to the DB. But if all you will ever want to do is to do a script to convert textual data to MySQL tables, than you might get along with something even easier. Check the tutorial linked from the official MySQL website, for example.
HTH!

50 million+ Rows of Data - CSV or MySQL

I have a CSV file which is about 1GB big and contains about 50million rows of data, I am wondering is it better to keep it as a CSV file or store it as some form of a database. I don't know a great deal about MySQL to argue for why I should use it or another database framework over just keeping it as a CSV file. I am basically doing a Breadth-First Search with this dataset, so once I get the initial "seed" set the 50million I use this as the first values in my queue.
Thanks,

I would say that there are a wide variety of benefits to using a database over a CSV for such large structured data so I would suggest that you learn enough to do so. However, based on your description you might want to check out non-server/lighter weight databases. Such as SQLite, or something similar to JavaDB/Derby... or depending on the structure of your data a non-relational (Nosql) database- obviously you will need one with some type of python support though.

If you want to search on something graph-ish (since you mention Breadth-First Search) then a graph database might prove useful.

Are you just going to slurp in everything all at once? If so, then CSV is probably the way to go. It's simple and works.
If you need to do lookups, then something that lets you index the data, like MySQL, would be better.

From your previous questions, it looks like you are doing social-network searches against facebook friend data; so I presume your data is a set of 'A is-friend-of B' statements, and you are looking for a shortest connection between two individuals?
If you have enough memory, I would suggest parsing your csv file into a dictionary of lists. See Can this breadth-first search be made faster?
If you cannot hold all the data at once, a local-storage database like SQLite is probably your next-best alternative.
There are also some python modules which might help:
graph-tool http://projects.skewed.de/graph-tool/
python-graph http://pypi.python.org/pypi/python-graph/1.8.0
networkx http://networkx.lanl.gov/
igraph http://igraph.sourceforge.net/

How about some key-value storages like MongoDB