Sqlite on Lessons learned

A latest-row query cannot tell you whether every device is current

Thu, 29 Oct 2026 00:00:00 +0000

A dashboard can label an account “current” while that account still has an outdated device. Selecting the latest event per account and application retains one observation; it discards observations from other devices. Describe that selection in the report before claiming that the source telemetry cannot distinguish devices.

The distinction matters during a security review: “latest observed state” and “any observed device needs attention” answer different questions. Correct arithmetic cannot repair a mislabeled metric.

Reproduce the lost device with synthetic data

Run this example with Python 3 and SQLite supporting window functions. The names, dates, and status values are synthetic; current is a supplied classification, not an implementation of version comparison.

import sqlite3

con = sqlite3.connect(":memory:")
con.executescript("""
CREATE TABLE observations (
 event_id INTEGER PRIMARY KEY,
 account TEXT, app TEXT, device TEXT,
 observed_at TEXT, current INTEGER
);
INSERT INTO observations VALUES
 (1, 'account-a', 'demo', 'laptop', '2026-01-01T09:00:00Z', 0),
 (2, 'account-a', 'demo', 'phone', '2026-01-01T10:00:00Z', 1),
 (3, 'account-b', 'demo', 'tablet', '2026-01-01T10:00:00Z', 0),
 (4, 'account-b', 'demo', 'tablet', '2026-01-02T10:00:00Z', 1);
""")

latest_account = """
WITH ranked AS (
 SELECT *, ROW_NUMBER() OVER (
 PARTITION BY account, app
 ORDER BY observed_at DESC, event_id DESC
 ) AS rn
 FROM observations
)
SELECT account, current FROM ranked WHERE rn = 1 ORDER BY account
"""

latest_devices = """
WITH ranked AS (
 SELECT *, ROW_NUMBER() OVER (
 PARTITION BY account, app, device
 ORDER BY observed_at DESC, event_id DESC
 ) AS rn
 FROM observations
)
SELECT account, MAX(CASE WHEN current = 0 THEN 1 ELSE 0 END)
FROM ranked WHERE rn = 1
GROUP BY account, app ORDER BY account
"""

account_rows = con.execute(latest_account).fetchall()
exposure_rows = con.execute(latest_devices).fetchall()
assert account_rows == [('account-a', 1), ('account-b', 1)]
assert exposure_rows == [('account-a', 1), ('account-b', 0)]
print("latest account state:", account_rows)
print("any latest-observed device outdated:", exposure_rows)

Both accounts have a current latest observation. Only account-a has an outdated device among its latest per-device observations. The laptop disappeared from the first query because the phone reported later.

The account-b rows are a negative control. Aggregating every historical outdated event would flag the tablet even after its newer current observation. Rank within each device first, then aggregate the surviving states.

State the measurement contract

An accurate table label might be:

Accounts classified by their latest observed event per application, with ties resolved by event ID.

That supports a claim about selected observations. It does not establish that every device on those accounts is current. Nor does the query establish what the ingestion system could identify before aggregation.

For an exposure report, define the device identity and observation window explicitly. A stable device key is a prerequisite for the second query. An installation identifier that rotates after reinstalling may represent installations rather than physical devices; a shared or missing identifier can merge unrelated observations. Inspect the schema and collection semantics before choosing the partition keys.

The example orders normalized UTC timestamps and breaks ties with a unique event ID. That makes the selection deterministic. It does not prove that a larger ID represents a later real-world event when timestamps tie. SQLite’s window-function documentation describes how ROW_NUMBER() follows the window’s ordering.

Keep unknown devices out of the healthy bucket

Even latest-per-device aggregation describes observed devices. Retired devices, missing telemetry, and a stale last observation need an explicit policy. Define freshness before calling the result a current fleet inventory, and report unknown states separately instead of treating silence as evidence of health.

A reporting correction may leave every displayed count unchanged: the query can stay the same while its title and interpretation become accurate. Calculating a replacement device-level metric is separate work and requires source data that supports it.

Audit the partition keys behind one account-level health table, then check whether its title describes exactly the rows that survive.

An UPDATE on encrypted columns wrote nothing for years, and three bugs took turns hiding it

Wed, 23 Sep 2026 15:42:00 +0200

A desktop app maintained an activity history in SQLite, but the UI froze on the first entry into any state. An action could start as ‘in progress’ and later complete, yet the UI would keep showing ‘in progress’ forever.

The update function looked fine at first glance, but nobody had actually run it under test, and it had 0% test coverage. That made the bug easy to miss and hard to trust.

The setup

The app encrypts the path columns as it writes them (column level encryption, not the whole database as with SQLCipher; see why fields rather than the file ). With a random IV per row, you can’t query the encrypted columns at all. To make rows matchable, derive the IV from the plaintext so the same path always encrypts to the same ciphertext. You store the IV alongside the row, base64 encoded:

history(id, run_id, path_enc, type, state, timestamp, iv)

You can’t put a plaintext path in a WHERE clause against path_enc. So the update does three steps:

Check the row’s IV.
Re-encrypt the current path with that IV, then SELECT to check whether the row still matches by id, run, path, and type.
Run an UPDATE with the same match predicate.

Reproduce with the real class

One test that stores, updates, and reloads through the real DAO, with an in-memory schema and a dummy key:

TEST_F(HistoryDaoTest, update_StoredUpload_LoadShowsNewState) {
 HistoryDao dao(&_database);
 Action upload { action(Action::Upload, Action::IN_PROGRESS) };
 upload.rowId = dao.store(&upload, 5);
 upload.status = Action::OK;

 dao.update(&upload, 5);

 EXPECT_EQ(load().value(0).state(), Action::OK);
}

It failed. The reloaded state was still IN_PROGRESS, which was precisely where the problem was.

Three faults, each hiding the next

QByteArray iv = queryIv.value(0).toByteArray(); // 1: still base64
...
query.prepare("SELECT id FROM history" // 2: no space before WHERE
 "WHERE id = :id AND path_enc = :path ...");
bool ok = database->exec(&queryIv); // 2: runs the IV query again
...
QSqlQuery update_query(db);
update_query.prepare("UPDATE history SET state = :state ... WHERE <same predicate>");
...
database->exec(&query); // 3: runs the SELECT, not the UPDATE
database->commit();
return true;

The IV was never decoded. store() decodes the base64 IV before encryption, but update() passed the IV through still encoded. The encrypt helper rejected an IV of the wrong length and threw, so every call stopped there. Before the length check was introduced, a wrong IV would have produced a ciphertext that was impossible to match. In either case, nothing was ever written. The bug had been there since the commit that introduced encryption, because the two sibling functions in that commit disagreed.
The guard SELECT never ran. A later refactor concatenated two string literals with no space between them (historyWHERE), so prepare() failed. The failure was only logged. Then exec() ran on the IV query, which succeeded. A follow-up “fix typo in query” commit fixed half of the SQL and left the missing space.
The UPDATE never ran. A rename to update_query missed the exec() call, which kept executing query instead.

Nothing ever got past fault 1, so faults 2 and 3 were never executed in production. Read the code as a chain of what would have happened if each fault had been fixed one at a time: fix the IV, and the function returns true while writing nothing; fix the SELECT as well, and fault 3 reruns a successful SELECT, commits, and returns true. Each fix would look like progress, and the result would be a function that reports success and writes nothing.

Date the bug before you blame the refactor

The first suspect was the 2022 refactor that added the second SELECT. Running git log -S on the decode call and reading store() and update() side by side in the encryption commit moved the start date back to 2019. That matters for the fix: the refactor wasn’t the thing to revert, and the original rule from before encryption was still the spec.

The tempting shortcut

The obvious simplification is to drop the IV lookup and the re-encryption and run UPDATE … WHERE id = :id. That passes the test above.

It also deletes the rule. The version from before encryption updated a row only while its id, run, paths, and type all still matched. Every later commit kept that predicate. Updating by id alone means a stale action can overwrite whatever row now sits behind that id. So the fix was one small change per fault, keeping the predicate:

QByteArray iv = QByteArray::fromBase64(queryIv.value(0).toByteArray(),
 QByteArray::Base64UrlEncoding);
query.prepare("SELECT id FROM history "
 "WHERE id = :id AND path_enc = :path ...");
bool ok = database->exec(&query);
...
database->exec(&update_query);

A second test pins the rule:

TEST_F(HistoryDaoTest, update_OtherPath_LeavesTheRowAlone) {
 HistoryDao dao(&_database);
 Action upload { action(Action::Upload, Action::IN_PROGRESS) };
 upload.rowId = dao.store(&upload, 5);
 upload.name = "other.jpg"; // same id, different path
 upload.status = Action::OK;

 dao.update(&upload, 5);

 EXPECT_EQ(load().value(0).state(), Action::IN_PROGRESS);
}

Before the fix, this test passed too, trivially, because nothing was ever updated. A negative test proves something only once its positive twin is green. After the fix, both tests passed, and so did the full suite (787 tests).

Side notes

QSqlQuery::size() returns -1 on SQLite, because the driver doesn’t report query sizes. A if (query.size() > 1) guard never fires there. Count with while (query.next()), or use SELECT COUNT(*).
Deriving the IV from the plaintext makes the encryption deterministic: equal paths give equal ciphertexts, which is what makes matching possible. With AES-GCM, never reuse that IV for a different plaintext. If you need equality lookups on encrypted columns, consider a misuse-resistant mode (AES-SIV) or a separate blind-index column (an HMAC of the plaintext) instead.

Takeaway

Test a write path as store → change → reload through the real class against a real database. “Returns true” and “rows changed” are different claims.
Check every prepare() and exec() result and stop on failure. A failure that’s only logged lets the function carry on and commit.
Use one QSqlQuery variable per statement and never pass the wrong one to exec(). After an UPDATE, check numRowsAffected(); zero rows is a valid SQL result, not an error.
When you fix a chain like this, expect the next fault to surface. Keep the reproducing test red until the whole round trip works.
Before simplifying a WHERE clause, find out why each condition is there. Pin the rule with a negative test.

Encrypt sensitive fields inside the ORM

Sat, 30 Oct 2021 18:03:45 +0200

Your database may run where you don’t control the environment: embedded in a desktop app, or in the cloud for a web app. If it stores sensitive data, encrypt those fields so a leaked copy doesn’t expose them. In this article, I show how I did that in two projects, and the drop-in text field for Python ORMs that came out of it.

Encrypted fields in an embedded SQLite

The first case was a desktop app with an SQLite database that only authorized software could read.

I considered encrypting the SQLite file and encrypting fields within it, and I chose the latter. That approach is reusable, and you never have to load the whole database into memory to decrypt it or leave it in plaintext for a moment.

The app already had its database logic, and the goal was to change as little as possible. So I put the encryption inside the ORM, and the rest of the app stayed business as usual.

The details:

Cipher and key: AES-256-CTR with a single key for the whole database. There was no reason for anything more complex. The key is generated on the first run and on the upgrade from the old version.
Storage: binary blobs made querying the database in a terminal for debugging a pain. Base64-encoded ciphertext in a text field works better. A new column holds the row’s initialization vector (IV), a random array of bytes, also base64-encoded.
Migration: it’s not only a schema change. Every row needs the fields encrypted for the updated logic to work.

You can’t query against encrypted fields. The ciphertext is a function of the plaintext, the key, and the IV, and the IV is specific to the row. This is a property of the privacy, not a defect. When you need to query encrypted values, the solution is domain-specific: sometimes you can modify the data or compute hashes. Discuss it with the customer in terms of the security objectives they want to achieve. For the same reasons, the data migration is domain-specific too.

An encrypted payload in MongoDB documents

On another project, the database was MongoDB and had many sensitive fields. I split each document into the sensitive fields and the metadata used for querying. I encrypted the sensitive fields together into a single field. The document also gets the IV and an HMAC over the rest of the document, ciphertext included:

def create_secure_json(form, keys):
 data = get_sensitive(form)
 data_secure_b64, iv_b64 = cryptjson.encrypt_json(data, keys['db'])

 payload = get_metadata(form)
 payload['created'] = str(datetime.datetime.now())
 payload['iv'] = str(iv_b64, 'ascii')
 payload['data'] = str(data_secure_b64, 'ascii')

 hmac_b64 = get_hmac(payload, keys['hmac'])
 payload['hmac'] = str(hmac_b64, 'ascii')
 return payload

This payload is what goes into the database. Use a separate key for the HMAC, and verify it before you decrypt: AES-CBC alone doesn’t detect tampering.

For the encryption, I use the cryptography package from the Python Cryptographic Authority. Encrypting a JSON is no magic: dump it into bytes and encrypt those, here with AES-CBC:

import base64
import secrets

from cryptography.hazmat.primitives import padding
from cryptography.hazmat.primitives.ciphers import Cipher, algorithms, modes


def encrypt_bytes(pt, key):
 iv = secrets.token_bytes(16)
 cipher = Cipher(algorithms.AES(key), modes.CBC(iv))
 encryptor = cipher.encryptor()
 padder = padding.PKCS7(128).padder()
 padded_pt = padder.update(pt) + padder.finalize()
 ct = encryptor.update(padded_pt) + encryptor.finalize()
 return base64.b64encode(ct), base64.b64encode(iv)


def decrypt_bytes(b64ct, b64iv, key):
 ct = base64.b64decode(b64ct)
 iv = base64.b64decode(b64iv)
 cipher = Cipher(algorithms.AES(key), modes.CBC(iv))
 decryptor = cipher.decryptor()
 padded_pt = decryptor.update(ct) + decryptor.finalize()
 unpadder = padding.PKCS7(128).unpadder()
 return unpadder.update(padded_pt) + unpadder.finalize()

A drop-in encrypted text field

Both solutions were domain-specific. Still, they showed that the use case is common and that encryption in Python is easy to work with. So I wrote cryptbase, a small library that adds an encrypted text field to SQLAlchemy and Django ORM.

Inside, there is a CryptoContainer. You create it from a ciphertext and an IV, or from a plaintext, and it gives you access to the ciphertext, the plaintext, and the IV in a unified way:

container = CryptoContainer(ciphertext=base64.b64decode(ciphertext),
 iv=base64.b64decode(iv),
 encryptor=encryptor)

container = CryptoContainer(plaintext=value, encryptor=self.encryptor)

A separate IV column complicates the migration, and the goal is a drop-in replacement for text fields. The base64 alphabet has no @, so the field stores both values in one column, with @ as the separator:

<base64 ciphertext>@<base64 IV>

The encryptor is an object with a simple interface, so there can be one per encryption method, such as AES256CTREncryptor:

def encrypt(self, plaintext: bytes) -> Tuple[bytes, bytes]:
 pass

def decrypt(self, ciphertext: bytes, iv: bytes) -> bytes:
 pass

In the end, the ORM gets a simple interface:

# SQLAlchemy
sensitive = Column(EncryptedText(key=DB_KEY))

# Django
sensitive = EncryptedTextField(key=DB_KEY)

You work with these fields the same way as with a text field. When the application reads the value, it gets the plaintext from the container. When the value is written to the database, it’s encrypted, and the __str__ of the CryptoContainer joins the base64-encoded ciphertext and IV:

str(CryptoContainer(plaintext=value, encryptor=self.encryptor))