I have a dictionary in Python where the keys are integers and the values sets of integers. Considering the potential size (millions of key-value pairs, where a set can contain from 1 to several hundreds of integers), I would like to store it in a SQL (?) database, rather than serialize it with pickle to store it and load it back in whenever I need it.
From reading around I see two potential ways to do this, both with its downsides:
Serialize the sets and store them as BLOBs: So I would get an SQL with two columns, the first column are the keys of the dictionary as INTEGER PRIMARY KEY, the second column are the BLOBS, containing a set of integers.
Downside: Not able to alter sets anymore without loading the complete BLOB in, and after adding a value to it, serialize it back and insert it back to the database as a BLOB.
Add a unique key for each element of each set: I would get two columns, one with the keys (which are now key_dictionary + index element of set/list), one with one integer value in each row. I'd now be able to add values to a "set" without having to load the whole set into python. I would have to put more work in keeping track of all the keys.
In addition, once the database is complete, I will always need sets as a whole, so idea 1 seems to be faster? If I query for all in primary keys BETWEEN certain values, or LIKE certain values, to obtain my whole set in system 2, will the SQL database (sqlite) still work as a hashtable? Or will it linearly search for all values that fit my BETWEEN or LIKE search?
Overall, what's the best way to tackle this problem? Obviously, if there's a completely different 3rd way that solves my problems naturally, feel free to suggest it! (haven't found any other solution by searching around)
I'm kind of new to Python and especially to databases, so let me know if my question isn't clear. :)
You second answer is nearly what I would recommend. What I would do is have three columns:
Set ID
Key
Value
I would then create a composite primary key on the Set ID and Key which guarantees that the combination is unique:
CREATE TABLE something (
set,
key,
value,
PRIMARY KEY (set, key)
);
You can now add a value straight into a particular set (Or update a key in a set) and select all keys in a set.
This being said, your first strategy would be more optimal for read-heavy workloads as the size of the indexes would be smaller.
will the SQL database (sqlite) still work as a hashtable?
SQL databases tend not to use hashtables. Nor do they usually do a sequential lookup. What they do is usually create an index (Which tends to be some kind of tree, e.g. a B-tree) which allows for range lookups (e.g. where you don't know exactly what keys you're looking for).
Related
Also, when is it appropriate to use one?
An index is used to speed up searching in the database. MySQL has some good documentation on the subject (which is relevant for other SQL servers as well):
http://dev.mysql.com/doc/refman/5.0/en/mysql-indexes.html
An index can be used to efficiently find all rows matching some column in your query and then walk through only that subset of the table to find exact matches. If you don't have indexes on any column in the WHERE clause, the SQL server has to walk through the whole table and check every row to see if it matches, which may be a slow operation on big tables.
The index can also be a UNIQUE index, which means that you cannot have duplicate values in that column, or a PRIMARY KEY which in some storage engines defines where in the database file the value is stored.
In MySQL you can use EXPLAIN in front of your SELECT statement to see if your query will make use of any index. This is a good start for troubleshooting performance problems. Read more here:
http://dev.mysql.com/doc/refman/5.0/en/explain.html
A clustered index is like the contents of a phone book. You can open the book at 'Hilditch, David' and find all the information for all of the 'Hilditch's right next to each other. Here the keys for the clustered index are (lastname, firstname).
This makes clustered indexes great for retrieving lots of data based on range based queries since all the data is located next to each other.
Since the clustered index is actually related to how the data is stored, there is only one of them possible per table (although you can cheat to simulate multiple clustered indexes).
A non-clustered index is different in that you can have many of them and they then point at the data in the clustered index. You could have e.g. a non-clustered index at the back of a phone book which is keyed on (town, address)
Imagine if you had to search through the phone book for all the people who live in 'London' - with only the clustered index you would have to search every single item in the phone book since the key on the clustered index is on (lastname, firstname) and as a result the people living in London are scattered randomly throughout the index.
If you have a non-clustered index on (town) then these queries can be performed much more quickly.
An index is used to speed up the performance of queries. It does this by reducing the number of database data pages that have to be visited/scanned.
In SQL Server, a clustered index determines the physical order of data in a table. There can be only one clustered index per table (the clustered index IS the table). All other indexes on a table are termed non-clustered.
SQL Server Index Basics
SQL Server Indexes: The Basics
SQL Server Indexes
Index Basics
Index (wiki)
Indexes are all about finding data quickly.
Indexes in a database are analogous to indexes that you find in a book. If a book has an index, and I ask you to find a chapter in that book, you can quickly find that with the help of the index. On the other hand, if the book does not have an index, you will have to spend more time looking for the chapter by looking at every page from the start to the end of the book.
In a similar fashion, indexes in a database can help queries find data quickly. If you are new to indexes, the following videos, can be very useful. In fact, I have learned a lot from them.
Index Basics
Clustered and Non-Clustered Indexes
Unique and Non-Unique Indexes
Advantages and disadvantages of indexes
Well in general index is a B-tree. There are two types of indexes: clustered and nonclustered.
Clustered index creates a physical order of rows (it can be only one and in most cases it is also a primary key - if you create primary key on table you create clustered index on this table also).
Nonclustered index is also a binary tree but it doesn't create a physical order of rows. So the leaf nodes of nonclustered index contain PK (if it exists) or row index.
Indexes are used to increase the speed of search. Because the complexity is of O(log N). Indexes is very large and interesting topic. I can say that creating indexes on large database is some kind of art sometimes.
INDEXES - to find data easily
UNIQUE INDEX - duplicate values are not allowed
Syntax for INDEX
CREATE INDEX INDEX_NAME ON TABLE_NAME(COLUMN);
Syntax for UNIQUE INDEX
CREATE UNIQUE INDEX INDEX_NAME ON TABLE_NAME(COLUMN);
First we need to understand how normal (without indexing) query runs. It basically traverse each rows one by one and when it finds the data it returns. Refer the following image. (This image has been taken from this video.)
So suppose query is to find 50 , it will have to read 49 records as a linear search.
Refer the following image. (This image has been taken from this video)
When we apply indexing, the query will quickly find out the data without reading each one of them just by eliminating half of the data in each traversal like a binary search. The mysql indexes are stored as B-tree where all the data are in leaf node.
INDEX is a performance optimization technique that speeds up the data retrieval process. It is a persistent data structure that is associated with a Table (or View) in order to increase performance during retrieving the data from that table (or View).
Index based search is applied more particularly when your queries include WHERE filter. Otherwise, i.e, a query without WHERE-filter selects whole data and process. Searching whole table without INDEX is called Table-scan.
You will find exact information for Sql-Indexes in clear and reliable way:
follow these links:
For cocnept-wise understanding:
http://dotnetauthorities.blogspot.in/2013/12/Microsoft-SQL-Server-Training-Online-Learning-Classes-INDEX-Overview-and-Optimizations.html
For implementation-wise understanding:
http://dotnetauthorities.blogspot.in/2013/12/Microsoft-SQL-Server-Training-Online-Learning-Classes-INDEX-Creation-Deletetion-Optimizations.html
If you're using SQL Server, one of the best resources is its own Books Online that comes with the install! It's the 1st place I would refer to for ANY SQL Server related topics.
If it's practical "how should I do this?" kind of questions, then StackOverflow would be a better place to ask.
Also, I haven't been back for a while but sqlservercentral.com used to be one of the top SQL Server related sites out there.
An index is used for several different reasons. The main reason is to speed up querying so that you can get rows or sort rows faster. Another reason is to define a primary-key or unique index which will guarantee that no other columns have the same values.
So, How indexing actually works?
Well, first off, the database table does not reorder itself when we put index on a column to optimize the query performance.
An index is a data structure, (most commonly its B-tree {Its balanced tree, not binary tree}) that stores the value for a specific column in a table.
The major advantage of B-tree is that the data in it is sortable. Along with it, B-Tree data structure is time efficient and operations such as searching, insertion, deletion can be done in logarithmic time.
So the index would look like this -
Here for each column, it would be mapped with a database internal identifier (pointer) which points to the exact location of the row. And, now if we run the same query.
Visual Representation of the Query execution
So, indexing just cuts down the time complexity from o(n) to o(log n).
A detailed info - https://pankajtanwar.in/blog/what-is-the-sorting-algorithm-behind-order-by-query-in-mysql
INDEX is not part of SQL. INDEX creates a Balanced Tree on physical level to accelerate CRUD.
SQL is a language which describe the Conceptual Level Schema and External Level Schema. SQL doesn't describe Physical Level Schema.
The statement which creates an INDEX is defined by DBMS, not by SQL standard.
An index is an on-disk structure associated with a table or view that speeds retrieval of rows from the table or view. An index contains keys built from one or more columns in the table or view. These keys are stored in a structure (B-tree) that enables SQL Server to find the row or rows associated with the key values quickly and efficiently.
Indexes are automatically created when PRIMARY KEY and UNIQUE constraints are defined on table columns. For example, when you create a table with a UNIQUE constraint, Database Engine automatically creates a nonclustered index.
If you configure a PRIMARY KEY, Database Engine automatically creates a clustered index, unless a clustered index already exists. When you try to enforce a PRIMARY KEY constraint on an existing table and a clustered index already exists on that table, SQL Server enforces the primary key using a nonclustered index.
Please refer to this for more information about indexes (clustered and non clustered):
https://learn.microsoft.com/en-us/sql/relational-databases/indexes/clustered-and-nonclustered-indexes-described?view=sql-server-ver15
Hope this helps!
I have a large collection of data, about 10 million entries and part of my program required very many membership checks...
if a in data:
return True
return False
right now I have data as dictionary entries with all their values equal to '1'
I also have a program that uses an algorithm to figure out the same information, but for now it is slower then the dictionary method however I expect the size of data to continue growing...
For my current dictionary solution, would type(data) as a frozenset, or set (or something else?) be faster?
And for the future to find out when I need to switch to my program, does anyone know how the speed of checking membership correlated with increasing the size of a type that's hashable?
Is a dictionary with 1 billion entries still fast?
On Principal
If you expect the data to keep growing you can't use a frozenset.
A set would be smaller than a dictionary storage wise for testing if an element exist in it. It would be similar in speed to a dictionary lookup since the keys and items of a set are both hashed for storage and always unique. If you don't need data associated with the username, use a set.
Practically speaking...
When you are dealing with that many entries move the data to a database. You will eventually run out of memory trying to store and read all of that into memory. With a database, you can issue a specific query to check membership. Seriously. Put that data in a database.
For this amount of data RyPeck is right - a DB will do the job much better.
One more point:
Something seems odd to me in what you've written:
If you use a dictionary to store the objects of the memberships, what the value of said key-value pair in the dictionary is '1'? Shouldn't the key-value pair of the dictionary be: "id of a"-"a" where 'a' is the object.
There are several bytes overhead per entry in a hash-able (whether dictionary or set doesn't make much of a difference), so for billions of entries you will run into swapping unless you have 32+Gb of memory for the application. I would start looking for a fast DB
For frozenset you also need to have all data in memory in some acceptable form at creation time, which probably doubles the amount of mem needed
I have these entity kinds:
Molecule
Atom
MoleculeAtom
Given a list(molecule_ids) whose lengths is in the hundreds, I need to get a dict of the form {molecule_id: list(atom_ids)}. Likewise, given a list(atom_ids) whose length is in the hunreds, I need to get a dict of the form {atom_id: list(molecule_ids)}.
Both of these bulk lookups need to happen really fast. Right now I'm doing something like:
atom_ids_by_molecule_id = {}
for molecule_id in molecule_ids:
moleculeatoms = MoleculeAtom.all().filter('molecule =', db.Key.from_path('molecule', molecule_id)).fetch(1000)
atom_ids_by_molecule_id[molecule_id] = [
MoleculeAtom.atom.get_value_for_datastore(ma).id() for ma in moleculeatoms
]
Like I said, len(molecule_ids) is in the hundreds. I need to do this kind of bulk index lookup on almost every single request, and I need it to be FAST, and right now it's too slow.
Ideas:
Will using a Molecule.atoms ListProperty do what I need? Consider that I am storing additional data on the MoleculeAtom node, and remember it's equally important for me to do the lookup in the molecule->atom and atom->molecule directions.
Caching? I tried memcaching lists of atom IDs keyed by molecule ID, but I have tons of atoms and molecules, and the cache can't fit it.
How about denormalizing the data by creating a new entity kind whose key name is a molecule ID and whose value is a list of atom IDs? The idea is, calling db.get on 500 keys is probably faster than looping through 500 fetches with filters, right?
Your third approach (denormalizing the data) is, generally speaking, the right one. In particular, db.get by keys is indeed about as fast as the datastore gets.
Of course, you'll need to denormalize the other way around too (entity with key name atom ID, value a list of molecule IDs) and will need to update everything carefully when atoms or molecules are altered, added, or deleted -- if you need that to be transactional (multiple such modifications being potentially in play at the same time) you need to arrange ancestor relationships.. but I don't see how to do it for both molecules and atoms at the same time, so maybe that could be a problem. Maybe, if modifications are rare enough (and depending on other aspects of your application), you could serialize the modifications in queued tasks.
Sorry for the very general title but I'll try to be as specific as possible.
I am working on a text mining application. I have a large number of key value pairs of the form ((word, corpus) -> occurence_count) (everything is an integer) which I am storing in multiple python dictionaries (tuple->int). These values are spread across multiple files on the disk (I pickled them). To make any sense of the data, I need to aggregate these dictionaries Basically, I need to figure out a way to find all the occurrences of a particular key in all the dictionaries, and add them up to get a total count.
If I load more than one dictionary at a time, I run out of memory, which is the reason I had to split them in the first place. When I tried , I ran into performance issues. I am currently trying to store the values in a DB (mysql), processing multiple dictionaries at a time, since mysql provides row level locking, which is both good (since it means I can parallelize this operation) and bad (since it slows down the insert queries)
What are my options here? Is it a good idea to write a partially disk based dictionary so I can process the dicts one at a time? With an LRU replacement strategy? Is there something that I am completely oblivious to?
Thanks!
A disk-based dictionary-like exists -- see the shelve module. Keys into a shelf must be strings, but you could simply use str on your tuples to obtain equivalent string keys; plus, I read your Q as meaning that you want only word as the key, so that's even easier (either str -- or, for vocabularies < 4GB, a struct.pack -- will be fine).
A good relational engine (especially PostgreSQL) would serve you well, but processing one dictionary at a time to aggregate each word occurrences over all corpora into a shelf object should also be OK (not quite as fast, but simpler to code, since a shelf is so similar to a dict except for the type constraint on keys [[and a caveat for mutable values, but as your values are ints that need not concern you).
Something like this, if I understand your question correctly
from collections import defaultdict
import pickle
result = defaultdict(int)
for fn in filenames:
data_dict = pickle.load(open(fn))
for k,count in data_dict.items():
word,corpus = k
result[k]+=count
If I understood your question correctly and you have integer ids for the words and corpora, then you can gain some performance by switching from a dict to a list, or even better, a numpy array. This may be annoying!
Basically, you need to replace the tuple with a single integer, which we can call the newid. You want all the newids to correspond to a word,corpus pair, so I would count the words in each corpus, and then have, for each corpus, a starting newid. The newid of (word,corpus) will then be word + start_newid[corpus].
If I misunderstood you and you don't have such ids, then I think this advice might still be useful, but you will have to manipulate your data to get it into the tuple of ints format.
Another thing you could try is rechunking the data.
Let's say that you can only hold 1.1 of these monsters in memory. Then, you can load one, and create a smaller dict or array that only corresponds to the first 10% of (word,corpus) pairs. You can scan through the loaded dict, and deal with any of the ones that are in the first 10%. When you are done, you can write the result back to disk, and do another pass for the second 10%. This will require 10 passes, but that might be OK for you.
If you chose your previous chunking based on what would fit in memory, then you will have to arbitrarily break your old dicts in half so that you can hold one in memory while also holding the result dict/array.
I want to store a list of numbers along with some other fields into MySQL. The number of elements in the list is dynamic (some time it could hold about 60 elements)
Currently I'm storing the list into a column of varchar type and the following operations are done.
e.g. aList = [1234122433,1352435632,2346433334,1234122464]
At storing time, aList is coverted to string as below
aListStr = str(aList)
and at reading time the string is converted back to list as below.
aList = eval(aListStr)
There are about 10 million rows, and since I'm storing as strings, it occupies lot space. What is the most efficient way to do this?
Also what should be the efficient way for storing list of strings instead of numbers?
Since you wish to store integers, an effective way would be to store them in an INT/DECIMAL column.
Create an additional table that will hold these numbers and add an ID column to relate the records to other table(s).
Also what should be the efficient way
for storing list of strings instead of
numbers?
Beside what I said, you can convert them to HEX code which will be very easy & take less space.
Note that a big VARCHAR may influence badly on the performance.
VARCHAR(2) and VARCHAR(50) does matter when actions like sotring are done, since MySQL allocates fixed-size memory slices for them, according to the VARCHAR maximum size.
When those slices are too large to store in memory, MySQL will store them on disk.
MySQL also has a SET type, it works like ENUM but can hold multiple items.
Of course you'd have to have a limited list, currently MySQL only supports up to 64 different items.
I'd be less worried about storage space and more worried about record retrieveal i.e., indexability/searching.
For example, I imagine performing a LIKE or REGEXP in a WHERE clause to find a single item in the list will be quite bit more expensive than if you normalized each list item into a row in a separate table.
However, if you never need to perform such queries agains these columns, then it just won't matter.
Since you are using relational database you should know that storing non-atomic values in individual fields breaks even the first normal form. More likely than not you should follow Don's advice and keep those values in related table. I can't say that for certain because I don't know your problem domain. It may well be that choosing RDBMS for this data was a bad choice altogether.