33 posts filed under mongreldb.
MongrelDB Kit exposes the engine's storage knobs as one-line calls per table: transaction spill thresholds, mutable-run spill, compaction zstd level, result cache budget, and the deferred-versus-eager index build policy.
A synchronous native database call that takes forty milliseconds stalls every request your Node process is serving, so MongrelDB Kit ships genuinely non-blocking async twins for the hot read and write paths, and this post covers which ones are real and which ones are not.
Installing a database driver in PHP used to mean phpize, a compiler, and a prayer; the MongrelDB PHP client is pure PHP installed with one Composer command, and this post explains why that is still a feature in 2026.
An embedded database has no daemon to scrape and no slow query log to tail, so MongrelDB Kit puts the storage engine's own per-table counters directly on the database handle.
A durable commit costs about a thousand times more than the write itself, so MongrelDB Kit ships WriteBuffer, an opt-in micro-batching surface that amortizes one fsync across a thousand rows.
A walk through the actual HTTP surface mongreldb-server exposes, from /sql and /txn to sessions, pagination cursors, and the /kit/* typed routes, and why plain HTTP was the right transport bet.
MongrelDB ships as one compiled binary per platform and one shared library per ABI, with cluster and cloud integrations excluded at compile time, and that shape is an economic decision as much as an engineering one.
MongrelDB evaluates unique, foreign-key, and CHECK constraints atomically at transaction commit instead of per statement, which changes what a batch import can promise and what it has to fear.
A technical comparison of MongrelDB and Couchbase Lite for embedded documents, offline sync, edge vector search, transactions, analytics, and security.
A technical comparison of MongrelDB and MongoDB Atlas for documents, transactions, vector and full-text search, hybrid retrieval, deployment, and encryption.
A technical comparison of MongrelDB and Qdrant for dense and sparse vectors, filtering, hybrid search, transactions, deployment, and local RAG.
A technical comparison of MongrelDB with PostgreSQL, pgvector, and ParadeDB for transactions, hybrid search, analytics, deployment, and operations.
A technical comparison of MongrelDB and DuckDB for embedded analytics, transactions, vector and full-text search, Arrow workflows, and mixed workloads.
A technical comparison of MongrelDB with Turso and libSQL for embedded replicas, SQLite compatibility, vector search, analytics, synchronization, and security.
A technical comparison of MongrelDB and ObjectBox for embedded applications, on-device vector search, transactions, synchronization, analytics, and encryption.
A technical comparison of MongrelDB and LanceDB for embedded vector search, hybrid retrieval, columnar data, transactions, RAG, and local deployment.
A technical comparison of MongrelDB and SurrealDB across embedded deployment, data models, transactions, SQL, vector search, hybrid retrieval, and operations.
A technical comparison of MongrelDB and SQLite for transactions, analytics, vector and text search, encryption, deployment, and local-first applications.
An end-to-end technical walkthrough of MongrelDB, from transaction validation, WAL durability, MVCC, and PAX columnar runs to DataFusion SQL and hybrid retrieval.
A JSON column in MongrelDB is a typed column that the engine validates at write time and queries from the inside, so the schema-versus-schemaless argument stops being a binary you have to pick a side of.
A column type is not documentation, it is a promise the storage engine can spend, and MongrelDB's type system covers fixed-width integers, decimal128 money, five flavors of time, sortable UUIDs, native JSON, and typed arrays so the engine can plan bytes instead of guessing at strings.
A pure embedded engine never has to think about other processes, but MongrelDB is also a server, so it takes an exclusive advisory lock at open, fails fast by default, waits politely when you ask it to, and pushes genuine multi-process access through the daemon instead of through the filesystem.
Most database auth lives in middleware, which means every code path that skips the middleware skips the auth. MongrelDB stores Argon2id-hashed users and roles in the catalog and can enforce permissions on every transaction at the storage layer itself.
MongrelDB reconstructs change events from its own WAL and streams committed records to follower copies, so an embedded database can participate in replication and change data capture without bolting on a separate toolchain.
MongrelDB binds idempotency keys into the commit log itself, so a retried write is safe across process crashes and restarts, not just across network drops.
MongrelDB separates accepted writes from fsync-backed durable commits, batches WAL work with group commit, and flushes log-structured columnar runs later.
PHP 8.4 is the floor of the pure PHP client, and PHP 8.5, released in November 2025, is where the language gives us enough to stop writing workarounds for cURL and readonly cloning.
MongrelDB runs in-process or behind mongreldb-server, keeping one storage and query engine while changing ownership, authentication, serialization, and resource boundaries.
SQLite SEE is the official paid reference for SQLite encryption. Here is MongrelDB's open-source AES-GCM design, key hierarchy, WAL coverage, and search tradeoff.
MongrelDB's PGM learned-range index uses an epsilon-bounded piecewise linear model to predict sorted-key positions, then verifies the bounded range exactly.
How an embedded vector database keeps HNSW candidates, operational rows, SQL filters, sparse retrieval, and exact reranking inside one Rust engine.
MongrelDB uses a Burrows-Wheeler transform and wavelet-tree FM-index for exact substring candidates that combine with SQL, vectors, ranges, and equality filters.
MongrelDB supports 35 languages through nine embedded native bindings and 26 language-native HTTP clients, with one engine contract under both boundaries.
