spatial databases

Posts about spatial or relational databases

SQLite Logo

Looking for a Good SQLite GUI?

Goodbye SQLite Manager…

Late last year, I discovered that my favorite SQLite GUI was defunct. The SQLite Manager was a plugin for Firefox that allowed you to create and interact with SQLite databases with a simple yet highly functional interface. It had good support for importing and exporting csv files, color coding of cells based on data types, and a convenient feature for cycling back and forth between your SQL statements. Since it was a Firefox plugin it was guaranteed to work on any operating system, and since Firefox is installed on machines across my campus I knew I could rely on it for creating data extracts for students and faculty – I’d package data up in SQLite and send it to them along with a link to the plugin.

Firefox goes through about a million versions a year these days, and after a major upgrade last fall (to Firefox Quantum) most of the existing plugins, including the SQLite Manager, were no longer compatible. An upgrade it highly unlikely, as a few things changed under the hood of Firefox that makes the plugin unusable. While it still works on the Firefox Extended Support Release, in the long run the writing is on the wall.

Hello DB Browser for SQLite!

After searching through many alternatives I discovered the DB Browser for SQLite. It runs on Windows, Mac, and Linux and there’s a version for mobile. It was easy to install and has a clean interface. It provides a number of convenient tools and menus that you can use in place of writing SQL DDL, and in some cases it expands the functionality of SQLite by enabling a number of ALTER TABLE commands that are not part of SQLite SQL (like renaming and dropping columns). The Browse Data window makes it easy to quickly thumb through, sort, and filter records and to edit individual values by hand. The Execute SQL window has auto-complete and color-coded syntax, and you can see the database schema in one tab as you write your SQL in another (making it easy to reference table and column names). You can import and export data as CSV (or any delimited text file) or SQL files, and you can save the results of SELECT queries as CSV.

One interesting addition is that there’s actually a Save (Write Changes) and Undo button. So when you create, modify, or drop records, columns, or tables you see the result, but the act isn’t final until you commit the changes. A nice safety feature, especially for db novices.

DB Browser for SQLite - Browse

Browse Data

DB Browser for SQLite - SQL

Execute SQL and View DB Schema

I encountered a few quirks, but nothing insurmountable. I was using the nightly build version without realizing it, and when importing a CSV file the database takes a best guess as to what the data types for the columns should be. Even though the import screen gives you the option to specify that values are quoted, my quoted numeric fields were still saved as numbers and not text. As a result, ID codes like FIPS or ZIP Codes lose their leading zeros and are saved as integers.

The project is managed on github, so I went ahead and posted an issue. The developers were super responsive, and a discussion ensued over whether this behavior was desirable or not. We found two work-arounds. First, if you build an empty table with the desired structure, and then go to import the CSV, if you provide the name of that empty table as the new table name the db will import your data into that table. Alternatively, if I went and downloaded the latest stable release (3.10.1) the default behavior is that all columns are imported as text, which is a safer bet. You can use the GUI to change the types after import. The issue was marked as a bug, and will be addressed in a future release – one possible solution is to provide an option to turn the autodetect feature on (to determine what the types should be) or off (to import everything as text).

The browser also has a feature to attach a database to the current database, but when you do the attachment it appears like nothing happened – you can’t seeĀ  or browse the objects in database number two. But it IS attached (you can see every statement that’s been executed in a helpful log window) and you can copy a table from one db to the other like this:

FROM database2.sometable;

You run this within the current database, and database2 is the attached database (when you attach a db you provide an alias for referencing it).

These are minor quibbles. The DB Browser for SQLite is cross-platform, stable, has a clean interface with nice features, and is actively developed by a responsive and friendly team. I’ll be using it for all my SQLite tasks and projects, and will recommend it to others.


An alternative I considered was to simply use the Spatialite GUI for both regular and spatial databases. It also has a simple, solid, and functional interface and supports spatial SQL, giving you the best of both worlds. So why not? While it works great for my own purposes it’s not something I can recommend to new users who are not GIS folks, either in my work or in the census data book I’m writing. Just figuring out where to download it from the website is overly complex, and while there are binaries for MS Windows there are none for Mac users. You’d have to install it from the source files, which is over the top for novices. Linux users may get lucky and find it in their software repos (it’s included for Debian and Ubuntu). The database browser in QGIS has matured in recent years, so that’s another option for GIS users who want to work with Spatialite or PostGIS.

Now if we only had a good GUI for PostgreSQL… I tried pgAdmin 4 about a year ago, and it was so bad that I’m still clinging to pgAdmin III as long as it still lives. But this is a different story, and one I’ll return to and investigate fully when it comes time to teach my spatial database course next year.

Copying Tables from SQLite to PostgreSQL

We recently created a PostgreSQL / PostGIS database on a server on our local campus network and spent the last few months loading data into it. We have a couple of different SQLite / Spatialite projects that we produce and I needed to move a large number of attribute tables from them into the Postgres database. My initial idea was to simply create a SQL dump file out of SQLite and then restore it in Postgres. I encountered a number of problems in doing this; there are slight differences in how each database creates and handles dump files. The solutions I found involved lousy things like opening the dump file in an editor (not feasible if the file is huge) and finding and replacing parentheses and commas, or running the file through a script to remove them.

That gave me a better idea – Python has SQLite and PostgreSQL modules (sqlite3 and psycopg2 respectfully). I could connect to the SQLite database and load the tables into Python’s data structures, and then simply connect to my PostgreSQL database and write them out. The original CREATE TABLE statements are stored in a master table; if I grab those statements and then the data that goes with them, I can simply recreate everything. The code is below.

First I define a number of variables that I hard code for each batch of tables. The SQLite variables are the name and path to the database (sqdb) and a string that I’ll use in a LIKE clause to grab certain tables (sqlike). For example, if my series of tables starts with yr followed by a year (yr2017) my string would be ‘yr%’. The remaining variables are for the Postgres database: pgdb (database name), pguser, pgpswd (my username and password), pghost, pgport (address of the database server on port 5432), and pgschema which is the name of the schema I want to write to.

I connect to the SQLite database and read all the names of the tables that match my LIKE statement from the master table. This returns a series of tuples where the name of the table is in the first position; I grab these and save them in a list. Then I loop through that list of tables and get the CREATE TABLE statement that’s stored in the master table and save that in string variable called create. Then I fetch all the rows for that table and save them in a tuple called rows. Lastly, I count the number of columns and create the number of string substitutions I’ll need in my SQL statement in a place holder variable (minus the last character, to remove a comma from the end of the statement).

That gives me everything I need for the first table. Then I connect to Postgres, set my schema path, execute the create table statement, and load the values in. Voila! If successful, we return to the top of the table loop and grab the next table. When we’re all done, we close the connection to the database.

#Frank Donnelly, Geospatial Data Librarian
#May 22, 2017
#Copies tables and data from a SQLite database and recreates them
#in a PostgreSQL database

import psycopg2, sqlite3, sys

#Change these values as needed




cursq.execute("SELECT name FROM sqlite_master WHERE type='table' AND name LIKE '%s'" % sqlike)
tabgrab = cursq.fetchall()
for item in tabgrab:

for table in tabnames:
    cursq.execute("SELECT sql FROM sqlite_master WHERE type='table' AND name = ?;", (table,))
    create = cursq.fetchone()[0]
    cursq.execute("SELECT * FROM %s;" %table)

        conpg = psycopg2.connect(database=pgdb, user=pguser, password=pgpswd,
                               host=pghost, port=pgport) 
        curpg = conpg.cursor()
        curpg.execute("SET search_path TO %s;" %pgschema)
        curpg.execute("DROP TABLE IF EXISTS %s;" %table)
        curpg.executemany("INSERT INTO %s VALUES (%s);" % (table, newholder),rows)
        print('Created', table)
    except psycopg2.DatabaseError as e:
        print ('Error %s') % e    
        if conpg:


There are a few caveats to this. First, data in SQLite is loosely typed, so you’re allowed to get away with storing strings in numeric columns and vice versa. PostgreSQL will balk at this, so if your SQLite data is pretty loose this approach (and any other approach really) will fall flat. You’d have to tighten up your SQLite data first.

Second, this approach doesn’t handle spatial data / geometry columns. In instances where I had spatial data and I tried copying it over, it failed; there are differences with how spatial data is tied to the underlying tables in each database so moving it requires a special process. I tried using some spatial database modules in Python but couldn’t get them working. Ultimately, since my spatial layers were point features and I had the original X and Y coordinates stored in numeric columns, I simply copied the data over and left the geometry behind, and once I was in PostGIS I recreated the geometry from the coordinates. Another alternative would be to use the OGR tools to copy spatial (and attribute) data over – I’ve tried this in a few other instances in the past with success, but was going in the opposite direction (from PostGIS to Spatialite) at the time.

While I haven’t tried it, you could modify the code if you wanted to go in the other direction (copy data from PostgeSQL to SQLite). You would just need to identify the system tables in PostgreSQL where the table names and create statements are stored. Going in this direction, the abundance of data types in PostgreSQL may be a problem – in SQLite your only options are: integer, real, text, and blob. SQLite may be able to take certain types and convert them to what it needs (i.e. take a varchar and save it as text) but I’m not sure it can handle every case. You could always run each create table statement string through a find and replace operation to modify the data types.