Consider the following JSON object: { "sides": 4, "colour": "red", "sides": 5, "colour": "blue" } Notice that sides and colour both appear twice. This looks invalid, but I learnt recently that this is actually legal JSON syntax! It’s unusual and discouraged, but it’s not completely forbidden. This was a big surprise to me. I think of JSON objects as key/value pairs, and I associate them with data structures like a dict in Python or a Hash in Ruby – both of which only allow unique keys. JSON has no such restriction, and I started thinking about how to handle it. What does the JSON spec say about duplicate names? JSON is described by several standards, which Wikipedia helpfully explains for us: After RFC 4627 had been available as its “informational” specification since 2006, JSON was first standardized in 2013, as ECMA‑404. RFC 8259, published in 2017, is the current version of the Internet Standard STD 90, and it remains consistent with ECMA‑404. That same year, JSON was also standardized as ISO/IEC 21778:2017. The ECMA and ISO/IEC standards describe only the allowed syntax, whereas the RFC covers some security and interoperability considerations. All three of these standards explicitly allow the use of duplicate names in objects. ECMA‑404 and ISO/IEC 21778:2017 have identical text to describe the syntax of JSON objects, and they say (emphasis mine): An object structure is represented as a pair of curly bracket tokens surrounding zero or more name/value pairs. […] The JSON syntax does not impose any restrictions on the strings used as names, does not require that name strings be unique, and does not assign any significance to the ordering of name/value pairs. These are all semantic considerations that may be defined by JSON processors or in specifications defining specific uses of JSON for data interchange. RFC 8259 goes further and strongly recommends against duplicate names, but the use of SHOULD means it isn’t completely forbidden: The names within an object SHOULD be unique. The same document warns about the consequences of ignoring this recommendation: An object whose names are all unique is interoperable in the sense that all software implementations receiving that object will agree on the name-value mappings. When the names within an object are not unique, the behavior of software that receives such an object is unpredictable. Many implementations report the last name/value pair only. Other implementations report an error or fail to parse the object, and some implementations report all of the name/value pairs, including duplicates. So it’s technically valid, but it’s unusual and discouraged. I’ve never heard of a use case for JSON objects with duplicate names. I’m sure there was a good reason for it being allowed by the spec, but I can’t think of it. Most JSON parsers – including jq, JavaScript, and Python – will silently discard all but the last instance of a duplicate name. Here’s an example in Python: >>> import json >>> json.loads('{"sides": 4, "colour": "red", "sides": 5, "colour": "blue"}') {'colour': 'blue', 'sides': 5} What if I wanted to decode the whole object, or throw an exception if I see duplicate names? This happened to me recently. I was editing a JSON file by hand, and I’d copy/paste objects to update the data. I also had scripts which could update the file. I forgot to update the name on one of the JSON objects, so there were two name/value pairs with the same name. When I ran the script, it silently erased the first value. I was able to recover the deleted value from the Git history, but I wondered how I could prevent this happening again. How could I make the script fail, rather than silently delete data? Decoding duplicate names in Python When Python decodes a JSON object, it first parses the object as a list of name/value pairs, then it turns that list of name value pairs into a dictionary. We can see this by looking at the JSONObject function in the CPython source code: it builds a list pairs, and at the end of the function, it calls dict(pairs) to turn the list into a dictionary. This relies on the fact that dict() can take an iterable of key/value tuples and create a dictionary: >>> dict([('sides', 4), ('colour', 'red')]) {'colour': 'red', 'sides': 4} The docs for dict() tell us that it` will discard duplicate keys: “if a key occurs more than once, the last value for that key becomes the corresponding value in the new dictionary”. >>> dict([('sides', 4), ('colour', 'red'), ('sides', 5), ('colour', 'blue')]) {'colour': 'blue', 'sides': 5} We can customise what Python does with the list of name/value pairs. Rather than calling dict(), we can pass our own function to the object_pairs_hook parameter of json.loads(), and Python will call that function on the list of pairs. This allows us to parse objects in a different way. For example, we can just return the literal list of name/value pairs: >>> import json >>> json.loads( ... '{"sides": 4, "colour": "red", "sides": 5, "colour": "blue"}', ... object_pairs_hook=lambda pairs: pairs ... ) ... [('sides', 4), ('colour', 'red'), ('sides', 5), ('colour', 'blue')] We could also use the multidict library to get a dict-like data structure which supports multiple values per key. This is based on HTTP headers and URL query strings, two environments where it’s common to have multiple values for a single key: >>> from multidict import MultiDict >>> md = json.loads( ... '{"sides": 4, "colour": "red", "sides": 5, "colour": "blue"}', ... object_pairs_hook=lambda pairs: MultiDict(pairs) ... ) ... >>> md <MultiDict('sides': 4, 'colour': 'red', 'sides': 5, 'colour': 'blue')> >>> md['sides'] 4 >>> md.getall('sides') [4, 5] Preventing silent data loss If we want to throw an exception when we see duplicate names, we need a longer function. Here’s the code I wrote: import collections import typing def dict_with_unique_names(pairs: list[tuple[str, typing.Any]]) -> dict[str, typing.Any]: """ Convert a list of name/value pairs to a dict, but only if the names are unique. If there are non-unique names, this function throws a ValueError. """ # First try to parse the object as a dictionary; if it's the same # length as the pairs, then we know all the names were unique and # we can return immediately. pairs_as_dict = dict(pairs) if len(pairs_as_dict) == len(pairs): return pairs_as_dict # Otherwise, let's work out what the repeated name(s) were, so we # can throw an appropriate error message for the user. name_tally = collections.Counter(n for n, _ in pairs) repeated_names = [n for n, count in name_tally.items() if count > 1] assert len(repeated_names) > 0 if len(repeated_names) == 1: raise ValueError(f"Found repeated name in JSON object: {repeated_names[0]}") else: raise ValueError( f"Found repeated names in JSON object: {', '.join(repeated_names)}" ) If I use this as my object_pairs_hook when parsing an object which has all unique names, it returns the normal dict I’d expect: >>> json.loads( ... '{"sides": 4, "colour": "red"}', ... object_pairs_hook=dict_with_unique_names ... ) ... {'colour': 'red', 'sides': 4} But if I’m parsing an object with one or more repeated names, the parsing fails and throws a ValueError: >>> json.loads( ... '{"sides": 4, "colour": "red", "sides": 5}', ... object_pairs_hook=dict_with_unique_names ... ) Traceback (most recent call last): […] ValueError: Found repeated name in JSON object: sides >>> json.loads( ... '{"sides": 4, "colour": "red", "sides": 5, "colour": "blue"}', ... object_pairs_hook=dict_with_unique_names ... ) Traceback (most recent call last): […] ValueError: Found repeated names in JSON object: sides, colour This is precisely the behaviour I want – throwing an exception, not silently dropping data. Encoding non-unique names in Python It’s hard to think of a use case, but this post feels incomplete without at least a brief mention. If you want to encode custom data structures with Python’s JSON library, you can subclass JSONEncoder and define how those structures should be serialised. Here’s a rudimentary attempt at doing that for a MultiDict: class MultiDictEncoder(json.JSONEncoder): def encode(self, o: typing.Any) -> str: # If this is a MultiDict, we need to construct the JSON string # manually -- first encode each name/value pair, then construct # the JSON object literal. if isinstance(o, MultiDict): name_value_pairs = [ f'{super().encode(str(name))}: {self.encode(value)}' for name, value in o.items() ] return '{' + ', '.join(name_value_pairs) + '}' return super().encode(o) and here’s how you use it: >>> md = MultiDict([('sides', 4), ('colour', 'red'), ('sides', 5), ('colour', 'blue')]) >>> json.dumps(md, cls=MultiDictEncoder) {"sides": 4, "colour": "red", "sides": 5, "colour": "blue"} This is rough code, and you shouldn’t use it – it’s only an example. I’m constructing the JSON string manually, so it doesn’t handle edge cases like indentation or special characters. There are almost certainly bugs, and you’d need to be more careful if you wanted to use this for real. In practice, if I had to encode a multi-dict as JSON, I’d encode it as a list of objects which each have a key and a value field. For example: [ {"key": "sides", "value": 4 }, {"key": "colour", "value": "red" }, {"key": "sides", "value": 5 }, {"key": "colour", "value": "blue"}, ] This is a pretty standard pattern, and it won’t trip up JSON parsers which aren’t expecting duplicate names. Do you need to worry about this? This isn’t a big deal. JSON objects with duplicate names are pretty unusual – this is the first time I’ve ever encountered one, and it was a mistake. Trying to account for this edge case in every project that uses JSON would be overkill. It would add complexity to my code and probably never catch a single error. This started when I made a copy/paste error that introduced the initial duplication, and then a script modified the JSON file and caused some data loss. That’s a somewhat unusual workflow, because most JSON files are exclusively modified by computers, and this wouldn’t be an issue. I’ve added this error handling to my javascript-data-files library, but I don’t anticipate adding it to other projects. I use that library for my static website archives, which is where I had this issue. Although I won’t use this code exactly, it’s been good practice at writing custom encoders/decoders in Python. That is something I do all the time – I’m often encoding native Python types as JSON, and I want to get the same type back when I decode later. I’ve been writing my own subclasses of JSONEncoder and JSONDecoder for a while. Now I know a bit more about how Python decodes JSON, and object_pairs_hook is another tool I can consider using. This was a fun deep dive for me, and I hope you found it helpful too. [If the formatting of this post looks odd in your feed reader, visit the original article]

I store a lot of data in SQLite databases on remote servers, and I often want to copy them to my local machine for analysis or backup. When I’m starting a new project and the database is near-empty, this is a simple rsync operation: $ rsync --progress username@server:my_remote_database.db my_local_database.db As the project matures and the database grows, this gets slower and less reliable. Downloading a 250MB database from my web server takes about a minute over my home Internet connection, and that’s pretty small – most of my databases are multiple gigabytes in size. I’ve been trying to make these copies go faster, and I recently discovered a neat trick. What really slows me down is my indexes. I have a lot of indexes in my SQLite databases, which dramatically speed up my queries, but also make the database file larger and slower to copy. (In one database, there’s an index which single-handedly accounts for half the size on disk!) The indexes don’t store anything unique – they just duplicate data from other tables to make queries faster. Copying the indexes makes the transfer less efficient, because I’m copying the same data multiple times. I was thinking about ways to skip copying the indexes, and I realised that SQLite has built-in tools to make this easy. Dumping a database as a text file SQLite allows you to dump a database as a text file. If you use the .dump command, it prints the entire database as a series of SQL statements. This text file can often be significantly smaller than the original database. Here’s the command: $ sqlite3 my_database.db .dump > my_database.db.txt And here’s what the beginning of that file looks like: PRAGMA foreign_keys=OFF; BEGIN TRANSACTION; CREATE TABLE IF NOT EXISTS "tags" ( [name] TEXT PRIMARY KEY, [count_uses] INTEGER NOT NULL ); INSERT INTO tags VALUES('carving',260); INSERT INTO tags VALUES('grass',743); … Crucially, this reduces the large and disk-heavy indexes into a single line of text – it’s an instruction to create an index, not the index itself. CREATE INDEX [idx_photo_locations] ON [photos] ([longitude], [latitude]); This means that I’m only storing each value once, rather than the many times it may be stored across the original table and my indexes. This is how the text file can be smaller than the original database. If you want to reconstruct the database, you pipe this text file back to SQLite: $ cat my_database.db.txt | sqlite3 my_reconstructed_database.db Because the SQL statements are very repetitive, this text responds well to compression: $ sqlite3 explorer.db .dump | gzip -c > explorer.db.txt.gz To give you an idea of the potential savings, here’s the relative disk size for one of my databases. File Size on disk original SQLite database 3.4 GB text file (sqlite3 my_database.db .dump) 1.3 GB gzip-compressed text (sqlite3 my_database.db .dump | gzip -c) 240 MB The gzip-compressed text file is 14× smaller than the original SQLite database – that makes downloading the database much faster. My new ssh+rsync command Rather than copying the database directly, now I create a gzip-compressed text file on the server, copy that to my local machine, and reconstruct the database. Like so: # Create a gzip-compressed text file on the server ssh username@server "sqlite3 my_remote_database.db .dump | gzip -c > my_remote_database.db.txt.gz" # Copy the gzip-compressed text file to my local machine rsync --progress username@server:my_remote_database.db.txt.gz my_local_database.db.txt.gz # Remove the gzip-compressed text file from my server ssh username@server "rm my_remote_database.db.txt.gz" # Uncompress the text file gunzip my_local_database.db.txt.gz # Reconstruct the database from the text file cat my_local_database.db.txt | sqlite3 my_local_database.db # Remove the local text file rm my_local_database.db.txt A database dump is a stable copy source This approach fixes another issue I’ve had when copying SQLite databases. If it takes a long time to copy a database and it gets updated midway through, rsync may give me an invalid database file. The first half of the file is pre-update, the second half file is post-update, and they don’t match. When I try to open the database locally, I get an error: database disk image is malformed By creating a text dump before I start the copy operation, I’m giving rsync a stable copy source. That text dump isn’t going to change midway through the copy, so I’ll always get a complete and consistent text file. This approach has saved me hours when working with large databases, and made my downloads both faster and more reliable. If you have to copy around large SQLite databases, give it a try. [If the formatting of this post looks odd in your feed reader, visit the original article]

I’m a big fan of keyring, a Python module made by Jason R. Coombs for storing secrets in the system keyring. It works on multiple operating systems, and it knows what password store to use for each of them. For example, if you’re using macOS it puts secrets in the Keychain, but if you’re on Windows it uses Credential Locker. The keyring module is a safe and portable way to store passwords, more secure than using a plaintext config file or an environment variable. The same code will work on different platforms, because keyring handles the hard work of choosing which password store to use. It has a straightforward API: the keyring.set_password and keyring.get_password functions will handle a lot of use cases. >>> import keyring >>> keyring.set_password("xkcd", "alexwlchan", "correct-horse-battery-staple") >>> keyring.get_password("xkcd", "alexwlchan") "correct-horse-battery-staple" Although this API is simple, it’s not perfect – I have some frustrations with the get_password function. In a lot of my projects, I’m now using a small function that wraps get_password. What do I find frustrating about keyring.get_password? If you look up a password that isn’t in the system keyring, get_password returns None rather than throwing an exception: >>> print(keyring.get_password("xkcd", "the_invisible_man")) None I can see why this makes sense for the library overall – a non-existent password is very normal, and not exceptional behaviour – but in my projects, None is rarely a usable value. I normally use keyring to retrieve secrets that I need to access protected resources – for example, an API key to call an API that requires authentication. If I can’t get the right secrets, I know I can’t continue. Indeed, continuing often leads to more confusing errors when some other function unexpectedly gets None, rather than a string. For a while, I wrapped get_password in a function that would throw an exception if it couldn’t find the password: def get_required_password(service_name: str, username: str) -> str: """ Get password from the specified service. If a matching password is not found in the system keyring, this function will throw an exception. """ password = keyring.get_password(service_name, username) if password is None: raise RuntimeError(f"Could not retrieve password {(service_name, username)}") return password When I use this function, my code will fail as soon as it fails to retrieve a password, rather than when it tries to use None as the password. This worked well enough for my personal projects, but it wasn’t a great fit for shared projects. I could make sense of the error, but not everyone could do the same. What’s that password meant to be? A good error message explains what’s gone wrong, and gives the reader clear steps for fixing the issue. The error message above is only doing half the job. It tells you what’s gone wrong (it couldn’t get the password) but it doesn’t tell you how to fix it. As I started using this snippet in codebases that I work on with other developers, I got questions when other people hit this error. They could guess that they needed to set a password, but the error message doesn’t explain how, or what password they should be setting. For example, is this a secret they should pick themselves? Is it a password in our shared password vault? Or do they need an API key for a third-party service? If so, where do they find it? I still think my initial error was an improvement over letting None be used in the rest of the codebase, but I realised I could go further. This is my extended wrapper: def get_required_password(service_name: str, username: str, explanation: str) -> str: """ Get password from the specified service. If a matching password is not found in the system keyring, this function will throw an exception and explain to the user how to set the required password. """ password = keyring.get_password(service_name, username) if password is None: raise RuntimeError( "Unable to retrieve required password from the system keyring!\n" "\n" "You need to:\n" "\n" f"1/ Get the password. Here's how: {explanation}\n" "\n" "2/ Save the new password in the system keyring:\n" "\n" f" keyring set {service_name} {username}\n" ) return password The explanation argument allows me to explain what the password is for to a future reader, and what value it should have. That information can often be found in a code comment or in documentation, but putting it in an error message makes it more visible. Here’s one example: get_required_password( "flask_app", "secret_key", explanation=( "Pick a random value, e.g. with\n" "\n" " python3 -c 'import secrets; print(secrets.token_hex())'\n" "\n" "This password is used to securely sign the Flask session cookie. " "See https://flask.palletsprojects.com/en/stable/config/#SECRET_KEY" ), ) If you call this function and there’s no keyring entry for flask_app/secret_key, you get the following error: Unable to retrieve required password from the system keyring! You need to: 1/ Get the password. Here's how: Pick a random value, e.g. with python3 -c 'import secrets; print(secrets.token_hex())' This password is used to securely sign the Flask session cookie. See https://flask.palletsprojects.com/en/stable/config/#SECRET_KEY 2/ Save the new password in the system keyring: keyring set flask_app secret_key It’s longer, but this error message is far more informative. It tells you what’s wrong, how to save a password, and what the password should be. This is based on a real example where the previous error message led to a misunderstanding. A co-worker saw a missing password called “secret key” and thought it referred to a secret key for calling an API, and didn’t realise it was actually for signing Flask session cookies. Now I can write a more informative error message, I can prevent that misunderstanding happening again. (We also renamed the secret, for additional clarity.) It takes time to write this explanation, which will only ever be seen by a handful of people, but I think it’s important. If somebody sees it at all, it’ll be when they’re setting up the project for the first time. I want that setup process to be smooth and straightforward. I don’t use this wrapper in all my code, particularly small or throwaway toys that won’t last long enough for this to be an issue. But in larger codebases that will be used by other developers, and which I expect to last a long time, I use it extensively. Writing a good explanation now can avoid frustration later. [If the formatting of this post looks odd in your feed reader, visit the original article]

I’ve been writing some internal dashboards recently, and one hard part is displaying timestamps. Our server does everything in UTC, but the team is split across four different timezones, so the server timestamps aren’t always easy to read. For most people, it’s harder to understand a UTC timestamp than a timestamp in your local timezone. Did that event happen just now, an hour ago, or much further back? Was that at the beginning of your working day? Or at the end? Then I remembered that I tried to solve this five years ago at a previous job. I wrote a JavaScript snippet that converts UTC timestamps into human-friendly text. It displays times in your local time zone, and adds a short suffix if the time happened recently. For example: today @ 12:00 BST (1 hour ago) In my old project, I was using writing timestamps in a <div> and I had to opt into the human-readable text for every date on the page. It worked, but it was a bit fiddly. Doing it again, I thought of a more elegant solution. HTML has a <time> element for expressing datetimes, which is a more meaningful wrapper than a <div>. When I render the dashboard on the server, I don’t know the user’s timezone, so I include the UTC timestamp in the page like so: <time datetime="2025-04-15 19:45:00Z"> Tue, 15 Apr 2025 at 19:45 UTC </time> I put a machine-readable date and time string with a timezone offset string in the datetime attribute, and then a more human-readable string in the text of the element. Then I add this JavaScript snippet to the page: window.addEventListener("DOMContentLoaded", function() { document.querySelectorAll("time").forEach(function(timeElem) { // Set the `title` attribute to the original text, so a user // can hover over a timestamp to see the UTC time. timeElem.setAttribute("title", timeElem.innerText); // Replace the display text with a human-friendly date string // which is localised to the user's timezone. timeElem.innerText = getHumanFriendlyDateString( timeElem.getAttribute("datetime") ); }) }); This updates any <time> element on the page to use a human friendly date string, which is localised to the user’s timezone. For example, I’m in the UK so that becomes: <time datetime="2025-04-15 19:45:00Z" title="Tue, 15 Apr 2025 at 19:45 UTC"> Tue, 15 Apr 2025 at 20:45 BST </time> In my experience, these timestamps are easier and more intuitive for people to read. I always include a timezone string (e.g. BST, EST, PDT) so it’s obvious that I’m showing a localised timestamp. If you really need the UTC timestamp, it’s in the title attribute, so you can see it by hovering over it. (Sorry, mouseless users, but I don’t think any of my team are browsing our dashboards from their phone or tablet.) If the JavaScript doesn’t load, you see the plain old UTC timestamp. It’s not ideal, but the page still loads and you can see all the information – this behaviour is an enhancement, not an essential. To me, this is the unfulfilled promise of the <time> element. In my fantasy world, web page authors would write the time in a machine-readable format, and browsers would show it in a way that makes sense for the reader. They’d take into account their language, locale, and time zone. I understand why that hasn’t happened – it’s much easier said than done. You need so much context to know what’s the “right” thing to do when dealing with datetimes, and guessing without that context is at the heart of many datetime bugs. These sort of human-friendly, localised timestamps are very handy sometimes, and a complete mess at other times. In my staff-only dashboards, I have that context. I know what these timestamps mean, who’s going to be reading them, and I think they’re a helpful addition that makes the data easier to read. [If the formatting of this post looks odd in your feed reader, visit the original article]