Julia Evans

sqlite-utils: a nice way to import data into SQLite for analysis

Hello! This is a quick post about a nice tool I found recently called sqlite-utils, from the tools category.

Recently I wanted to do some basic data analysis using data from my Shopify store. So I figured I’d query the Shopify API and import my data into SQLite, and then I could make queries to get the graphs I want.

But this seemed like a lot of boring work, like I’d have to write a schema and write a Python program. So I hunted around for a solution, and I found sqlite-utils, a tool designed to make it easy to import arbitrary data into SQLite to do data analysis on the data.

sqlite-utils automatically generates a schema

The Shopify data has about a billion fields and I really did not want to type out a schema for it. sqlite-utils solves this problem: if I have an array of JSON orders, I can create a new SQLite table with that data in it like this:

import sqlite_utils

orders = ... # (some code to get the `orders` array here)

db = sqlite_utils.Database('orders.db')
db['shopify_orders'].insert_all(orders)

you can alter the schema if there are new fields (with alter)

Next, I ran into a problem where on the 5th page of downloads, the JSON contained a new field that I hadn’t seen before.

Luckily, sqlite-utils thought of that: there’s an alter flag which will update the table’s schema to include the new fields. ```

Here’s what the code for that looks like

db['shopify_orders'].insert_all(orders, alter=True)

you can deduplicate existing rows (with upsert)

Next I ran into a problem where sometimes when doing a sync, I’d download data from the API where some of it was new and some wasn’t.

So I wanted to do an “upsert” where it only created new rows if the item didn’t already exist. sqlite-utils also thought of this, and there’s an upsert method.

For this to work you have to specify the primary key. For me that was pk="id". Here’s what my final code looks like:

db['shopify_orders'].upsert_all(
    orders,
    pk="id",
    alter=True
)

there’s also a command line tool

I’ve talked about using sqlite-utils as a library so far, but there’s also a command line tool which is really useful.

For example, this inserts the data from a plants.csv into a plants table:

sqlite-utils insert plants.db plants plants.csv --csv

format conversions

I haven’t tried this yet, but here’s a cool example from the help docs of how you can do format conversions, like converting a string to a float:

sqlite-utils insert plants.db plants plants.csv --csv --convert '
return {
  "name": row["name"].upper(),
  "latitude": float(row["latitude"]),
  "longitude": float(row["longitude"]),
}'

This seems really useful for CSVs, where by default it’ll often interpret numeric data as strings if you don’t do this conversions.

metabase seems nice too

Once I had all the data in SQLite, I needed a way to draw graphs with it. I wanted some dashboards, so I ended up using Metabase, an open source business intelligence tool. I found it very straightforward and it seems like a really easy way to turn SQL queries into graphs.

This whole setup (sqlite-utils + metabase + SQL) feels a lot easier to use than my previous setup, where I had a custom Flask website that used plotly and pandas to draw graphs.

that’s all!

I was really delighted by sqlite-utils, it was super easy to use and it did everything I wanted.

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