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For the Zero Dependency 2026 hackathon, Lakshmi Venkatesan built ChronicleKV: a local, embedded key-value store using only Python’s standard library. Its core is an append-only write-ahead log (WAL): each change is recorded, checked, and replayed after a restart. Venkatesan reports that sync-mode tests recovered every acknowledged write in the runs tried, while async mode lost buffered writes after mid-flight kills. Those are project results—not proof against every kind of crash, storage device, or operating system.
What ChronicleKV is—and what “crash-safe” means here
ChronicleKV is a local key-value database written for the Zero Dependency 2026 hackathon, whose rule required that “your dependency manifest must be empty.” The project uses Python’s standard library rather than third-party runtime packages. Its design goal is to preserve earlier valid writes if a process is interrupted while writing a later record.
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That is a narrower claim than “cannot lose data.” Durability depends on the selected mode and on how the operating system, filesystem, and hardware handle writes and flushes. Venkatesan describes the project and its demonstrations in the September 5, 2026 article; the ChronicleKV repository documents implementation scope and platform limitations. The reported test outcomes are author- and project-reported; this account does not establish independent reproduction.
How the write-ahead log recovers after interruption
Each change is appended as a record
Rather than overwrite a data file in place, ChronicleKV appends records to its WAL. Venkatesan reports a 30-byte fixed header containing magic bytes, a version, operation, sequence number, timestamp, and key and value lengths. The key and value bytes follow, then a 4-byte CRC32 checksum. That makes 34 bytes of fixed record overhead before the key and value themselves, based on the author’s reported layout.
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The checksum lets the reader detect a record whose contents do not match its recorded CRC. On startup, the store replays records in sequence. If it encounters a checksum mismatch, it truncates the file from that point; the repository describes recovery as replaying valid records and discarding an incomplete tail. In the author’s words, recovery was “Just ‘recovery stopped at the last good write.’” This design retains the valid prefix of the log rather than treating a damaged tail as a reason to discard all earlier operations.
What this approach does not guarantee
A checksum detects certain forms of corruption; it does not itself force data onto durable media. Nor does a kill-based demonstration cover every failure mode, such as power loss, a failing storage device, or filesystem-specific behavior. The project README notes that the ability of its crash-demo mechanism to expose interrupted writes varies by operating system. Treat the demo as evidence about the tested setup, not a universal durability certification.
What the durability modes change
ChronicleKV’s modes differ in when a write is acknowledged relative to flushing it. That timing determines the trade-off between write latency and the amount of recent work that may be lost after abrupt termination.
| Mode | When data is flushed | What may be lost after an abrupt stop |
|---|---|---|
| Sync | Each write is fsynced before acknowledgement, according to the project description. | The author reports zero lost writes in every sync-mode run tried; the exact run count is not stated in the article. The repository reports 15 of 15 comparison runs with zero lost writes. Neither result proves behavior on every platform or device. |
| Async | Writes are buffered; the repository documents flush triggers of 100 records or 50 milliseconds. | Unflushed writes may be lost. Venkatesan reports an average of 37–50 lost writes per mid-flight kill, depending on buffer state. The repository reports an average of 37.4 lost writes across its 15 async runs. |
| Batch | Writes wait for an explicit flush. | Writes not yet flushed can be lost if the process stops abruptly. The cited project materials do not give a measured loss count for batch mode. |
These figures are results reported by Venkatesan and the ChronicleKV project, not independent benchmarks or a general database failure rate. In particular, an fsync call requests that the operating system flush file data, but the exact durability guarantees still depend on the full storage stack.
How the standard-library constraint shaped the build
The restriction changed more than the install command: Venkatesan had to replace dependencies that would ordinarily supply command-line parsing, locking, JSON handling, testing, and caching.
| Replaced tool or package | Standard-library or built-in approach |
|---|---|
click or typer |
argparse for CLI parsing |
filelock |
fcntl.flock on POSIX systems |
orjson |
json |
pytest |
unittest |
diskcache |
A dictionary index pointing to WAL offsets |
The author reports roughly 187 lines for WAL and recovery logic, 413 for the storage engine, and 261 for the CLI. The project had 51 passing tests at the time of the article. Venkatesan says the build took about 18 hours during the 72-hour event window; all of these are project-reported figures, not independently audited measurements.
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Features built on top of the log
Because records are sequenced and retained, ChronicleKV can expose more than the latest value. The repository lists basic put, get, and delete operations; prefix and range scans; history and point-in-time queries; compaction; integrity verification; CLI operations; and a TinyDB compatibility layer. The author says the append-only sequence made point-in-time reads, history, diff, and timeline features possible.
The trade-off is that a log grows as changes accumulate. Compaction is the maintenance mechanism the project provides to reduce that accumulated history in the stored database. The README does not present this as a query-optimized engine: scans may be linear or bisect-based, and there is no query optimizer.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Where ChronicleKV fits compared with TinyDB
The project frames ChronicleKV as an option for small, local, crash-sensitive document-storage workloads—not as a complete drop-in replacement for TinyDB. The right fit depends on durability needs, query patterns, concurrency, and deployment.
| Consideration | ChronicleKV, as documented by its project | TinyDB comparison in project materials |
|---|---|---|
| Durability | Offers per-write sync, buffered async, and caller-triggered batch flushing. | The supplied project comparison does not establish matching durability behavior for every TinyDB setup. Actual guarantees depend on implementation and storage stack. |
| Read-heavy use | Emphasizes crash recovery, history, and compaction. | The README says TinyDB may be faster for read-heavy cases because of in-memory caching; this is the project’s characterization, not a universal benchmark. |
| Concurrency and deployment | Local, single-writer and multi-reader; no network protocol, replication, or built-in backup. | Not stated in the cited ChronicleKV comparison as a measured side-by-side result. |
| Queries and data size | No query optimizer; scans may be linear or bisect-based. Positioned for small local workloads. | Not stated as a quantified dataset-size comparison. |
| History and maintenance | Includes point-in-time reads, history, diff/timeline features, integrity verification, and compaction. | Not stated as a direct feature-by-feature comparison in the cited project materials. |
Important scope and platform limits
- One writer process per database file: the README describes the design as single-writer and multi-reader, not a multi-writer database.
- POSIX locking: the project uses
fcntl.flockon POSIX. The README says this implementation does not provide equivalent cross-process enforcement on Windows. - No distributed features: there is no network protocol, replication, distributed transaction support, or built-in backup.
- Partial TinyDB compatibility: the compatibility layer does not make ChronicleKV a complete drop-in replacement.
- Limited query machinery: the project has no query optimizer, and scan costs depend on the operation and data.
For anything where loss would be costly, use an independent backup strategy and evaluate the behavior on the actual operating system and storage hardware rather than relying on a demo alone.
Can you build a database with only Python’s standard library?
ChronicleKV is a concrete example of a small embedded store built without third-party runtime dependencies. It is not evidence that the standard library removes the hard parts of database engineering: the author still had to implement record framing, checksums, replay, locking, flushing policy, indexing, recovery, tests, and maintenance behavior.
Venkatesan’s takeaway is that removing a dependency can expose what that dependency was doing: “Don’t think of ‘zero dependency’ as a restriction you’re working around — it’s forcing you to actually understand what the dependency was for.” Readers can inspect the project repository and the author’s build account; the post also mentions a one-click Colab demo, but its current behavior is not independently verified here.
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