Member Avatar for zyrus001

I'm looking to write a program that should be capable of holding around 1000 records, each record will contain a varying number of fields and values.

Usually, I'd create a list of dictionaries for something like this but wanted to know if that's the right way to go about it. This data will be routinely sorted and searched.

For something of this volume, is it better (from a performance perspective) to use an SQL database or can I build an equally fast solution in Python. What does industry use? Can I get better performance implementing a different type of data structure in Python?

Any input would be much appreciated!

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  • Latest Post Latest Post by griswolf

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For the dataset size you described, the right choice depends more on access patterns than on raw record count. is correct that an in-memory structure is simplest; and point toward persistence options. If your program is single-process and most operations are lookups by a specific key, an in-memory mapping (fast hash lookups) plus a tiny persistence layer is the easiest and usually fastest approach.

If you need multi-field queries or sorted results frequently, keep the records separate from your indexes: store each record once (tuple or small object) and maintain lightweight indexes for the fields you query often. Example patterns:

  • Inverted index (field,value) -> set(record_id) for fast multi-value filters.
  • Sorted list of keys with bisect for range queries and maintaining order.

Example: maintain an index and a sorted key list

# simple inverted index
index = {}
def add_index(field, value, rec_id):
    index.setdefault((field,value), set()).add(rec_id)

# sorted-key list for range queries
from bisect import bisect_left, insort
sorted_keys = []
insort(sorted_keys, (sort_key, rec_id))
pos = bisect_left(sorted_keys, (lookup_key, -1))

Memory and perf tips: if per-record overhead matters, use small classes with __slots__ or tuples/namedtuples instead of big per-record dicts. Keep heavy numeric columns in compact arrays or columnar structures if you’ll do a lot of numeric work. For persistence and multi-process access, an embedded SQL engine that ships with Python is convenient and gives durability/locking; otherwise serializing to a compact interchange format is fast and portable.

Measure with realistic data: profile common operations with timeit or a profiler before optimizing. For ~1000 records the simplest in-memory design is usually best—add indexing or durable storage only when access patterns or concurrency demand it.

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Member Avatar for Gribouillis

A nice alternative is to use a ZODB, or "Zope object database". In such a database, you can store persistent python objects and use them in very much the same way as you handle ordinary python objects. You don't need Zope to install and use ZODB: they form a standalone database module. ZODB are not relational databases, so you don't need SQL either. See the doc here http://www.zodb.org/

Edited by Gribouillis because: n/a
Member Avatar for griswolf

From your description "... a varying number of fields and values..." I would think you want a non-relational data store. Depending on things you didn't say, you might want to use your described structure, using pickle/unpickle to persist it to disk; or you might want to go with something like ZODB or some other third party product. 1000 records is very small for a database. A quick search for 'non relational data store' gives quite a lot of good stuff in the first few pages:

An article in Linux Magazine
A large overview
may also be something to look at.

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