Slater – Low-memory graphdb designed for read-heavy graphs

Current version: v0.24.1 — all releases. In one line: Slater serves graphs that don't fit in memory — hundreds of millions of nodes and billions of edges in low hundreds of MB of RAM — over standard Bolt, so any neo4j driver just works, with disk-native vector search sitting next to the graph, and it takes live, durable writes without giving that up. Resident memory is set by a cache budget you choose, not by the size of the graph. Shortcuts Why Slater exists Reads and writes What you get Features How it works The writable layer Storage backends Mounts ACL Health check Worked example Development Performance License 📖 Full manual Why Slater exists A graph database stores data as things (nodes) and the relationships between them (edges), with the relationships as first-class citizens. That's what you want when your questions are about connections rather than rows — "who's within three hops of this account?", "what's the full dependency chain behind this build?", "which accounts share a device, an address, and a card?" — the queries that become a swamp of recursive joins in SQL but fall out naturally in a graph. The most common complaint about graph databases is that they don't scale past what you can hold in RAM. Many of them (eg neo4j, Memgraph, FalkorDB, etc) keep the whole graph resident: a 40 GB graph wants 40 GB of memory — per instance. Want a replica per region, per tenant, or per pod? Multiply the bill. And past a certain size they simply won't load: eg the 90 million node / 1.5B-edge Wikidata graph needs ~64–128 GiB resident, so the in-memory engines can't open it at all. Slater is the rebuttal. It serves that same 90M-node graph from a few hundred MB of RAM, because it pages the graph from an on-disk image on demand instead of holding it resident — so the graph size and the memory bill are decoupled. Slater takes the opposite approach. You compile the graph once, offline, into a content-addressed on-disk image with slater-build; then any number of Slater servers serve it over Bolt (so your existing neo4j drivers just work) while holding only a fixed cache budget in memory. A 4 GB graph and a 400 GB graph cost the same RAM to serve — you fan out cheap, stateless read replicas and let the store, not the heap, hold the graph. That makes it a natural fit for knowledge graphs behind RAG, recommendation and identity graphs, dependency graphs — anything large and connected you want to query cheaply and often. Disk-native vector search lives right next to the graph, so the same engine is the retrieval layer for embeddings too. Reads and writes Slater is a read-write graph database. You don't rebuild the whole image to correct one property, add a node, or retract an edge — you write the change directly, over Bolt, and it lands durably. The trick is that writes never tax the read path. Writes accumulate in a log-structured-merge (LSM) layer over the immutable core: a write-ahead log and an in-memory table, spilling to immutable delta segments, folded back into a fresh core by a periodic consolidation. What that buys you: Reads over an unwritten graph cost exactly what they did before. An empty delta is a single predictable branch, not a merge — the read path is byte-identical whether or not the writable layer is on. The read cost of a write scales with the size of the delta, not the size of the graph. Whole-graph answers — count(*), the label and relationship-type marginals — stay metadata reads even with writes outstanding: the delta keeps its own counters, so a count(*) over a 91.6M-node core with half a million pending writes still answers in tens of milliseconds without touching a single block. Acknowledged means durable. A single writer drains the queue and returns SUCCESS only after the fsync that covers the write. Group your writes and they're cheap — a write-UNWIND commits one fsync per batch rather than per row. Business-key writes, in either dialect. MERGE / MATCH … SET / DELETE (and CREATE / REMOVE, detach delete, relationship writes) keyed on a node's identity property — or the equivalent ISO GQL data-modifying statements (INSERT / SET / REMOVE / DELETE), which lower onto the same path. Correct, insert, upsert and retract, over nodes and edges, addressed the way your data already is. The writable layer is opt-in (delta.enabled); with it off, Slater serves the pure immutable core and refuses writes. See The writable layer for the full model. On the name. Slater is named after the CIA agent in Archer (a great show) who insists on going by a single name — "Just… Slater" — and one of my favourite characters in it. See the character wiki page. What you get RAM set by your cache budget, not your graph size — fan out as many read replicas as you like; the graph never has to fit in memory. A drop-in for the graph — speaks Bolt, so any standard neo4j driver (JS, Python, Go…) works unchanged. It's Cypher (plus a slice of ISO GQL, reads and writes); nothing new to learn. Live, durable writes — an opt-in LSM layer over the immutable core: business-key MERGE / SET / DELETE over nodes and edges, group-committed and fsync-durable, folded back into a fresh core by consolidation. Reads don't pay for it. Deployment by file swap — build a new content-hashed generation offline, atomically flip the current pointer, and servers pick it up. Every block is checksummed, so a half-copied image is refused rather than served. Vector search built in — disk-native approximate-nearest-neighbour (cosine, L2, or dot KNN) sits right next to your graph, for when this is the retrieval layer behind a RAG pipeline, and embeddings are writable in place — no offline rebuild to add or change a vector. Locked down by design — read and write grants are independent, plus optional at-rest encryption, TLS Bolt, argon2id-hashed ACLs, and a read-only container rootfs for read replicas. Features Feature What it means for you Bounded, predictable memory Resident memory is capped by three cache budgets you set — it does not grow with graph size; you tune the performance/RAM trade-off instead of provisioning for the whole graph. A jemalloc allocator with background purge returns freed memory to the OS after heavy query bursts, so resident size falls back toward its idle floor rather than staying pinned at the post-burst high-water mark. Multi-tenant out of the box One server hosts many graphs with per-user read grants — multi-database isolation that most graph DBs reserve for a paid/enterprise tier. Encryption at rest & in transit Per-block XChaCha20-Poly1305 sealing (the key is never written to disk) plus optional TLS (bolt+s://). GDPR-friendly by construction. Tiny, dependency-light install A small stripped binary on a distroless glibc base (no shell/apt) — the multi-arch (amd64/arm64) image pulls at ~22 MB, or ~12 MB for the server-only slater:latest-lite tag; pure-Rust TLS, no OpenSSL. Pull and run. Built for periodic publish Build a graph offline, serve it immutable, then atomically swap in a new version with zero downtime — ideal for data-warehouse / scheduled-refresh workloads. Rugged under load The server and offline builder both compile with #![forbid( )] — the engine's only unsafe lives in the audited jemalloc allocator crate. The core is immutable, so reads take no locks and never wait on a writer; a single writer serialises mutations behind the write path alone. No GC pauses, no data races. One bad query can't take the server down. Works with your neo4j tools Speaks Bolt 5.4 / 4.4 / 4.1 — use the standard neo4j drivers (JS, Python, Go, Java…), cypher-shell, or graph browsers unchanged. Rich Cypher query surface A broad read surface: MATCH/WHERE/WITH/UNION, CALL {…} subqueries, 70+ functions & aggregations, temporal & geospatial values, and regex. Live, durable writes An opt-in single-writer LSM layer over the immutable core (delta.enabled): business-key MERGE / SET / DELETE / CREATE / REMOVE over nodes and relationships, batched write-UNWIND (one fsync per batch), and CALL slater.consolidate() — group-committed, fsync-durable, and folded back into a fresh core by consolidation. The read path is byte-identical when the delta is empty. ISO GQL, read and write Speaks a subset of ISO GQL (ISO/IEC 39075) over the same Bolt connection — quantified paths, path restrictors, shortest-path selectors, label/type boolean expressions, FOR, CAST, an optional GQL/CYPHER dialect prefix — and, with the writable layer on, GQL's data-modifying statements (INSERT / SET / REMOVE / [DETACH] DELETE) lower onto the same durable write path. Cypher and GQL, reads and writes, in one engine. Vectors + graph in one engine Disk-native ANN vector search (Vamana + PQ; cosine / L2 / dot) for embeddings/RAG, plus graph algorithms (PageRank, BFS, betweenness, WCC…) — bounded memory even with millions of vectors. Embeddings are writable (a FreshDiskANN-style write ladder): insert / update / delete a vector, KNN-visible at once, folded into the base without a rebuild. Safe on network storage Every file is BLAKE3 content-hashed and verified on open; torn or half-copied images are refused, not served. Designed for NFS/remote volumes (no mmap surprises). Pluggable storage backends Serve the same generation format from a local filesystem, an S3 (S3-compatible) bucket, or a Google Cloud Storage bucket — publish once, fan out to stateless replicas — with an optional local-SSD cache tier in front of the object store. See Storage backends. Two binaries make up the workspace: Binary Role slater The online Bolt server (the container ENTRYPOINT): serves reads and, with delta.enabled, the single-writer durable write path. slater-build The offline compiler: turns a primitive-Cypher dump into an immutable, content-hashed generation directory. Slater splits bulk building from serving: slater-build does the heavy lifting offline — ingesting your data and compiling it into an immutable generation — so a cold graph is never assembled on the serving hot path. Within the server, the read surface answers a broad Cypher slice — pattern matching, WITH/UNION/CALL {…} subqueries, 70+ scalar & aggregate functions, temporal & geospatial values, graph algorithms (algo.*), and disk-native vector KNN (db.idx.vector.queryNodes) — while the writable layer's delta overlay sits below that surface and is zero-cost when empty, so reads never carry the write-side machinery. You can update a graph two ways: write to it live over Bolt (see The writable layer), or build a new generation offline and atomically swap the current pointer, which the running server picks up via its generation guard (see Generation guard). Documentation The complete user manual lives in docs/manual/ — a feature-by-feature guide that explains, for every capability, what it is, why it exists, and how to use it, with worked examples you can run against a bundled sample graph. Start there for anything beyond this overview. New here? Quickstart builds and serves a graph in five steps. Writing queries? Querying, Functions & expressions, Procedures & algorithms, Vector search, Writing data. Building graphs? Building graphs and the Build CLI reference. Operating Slater? Deployment, Storage, Configuration reference, Security, Performance tuning. Running with Docker Slater is designed to be run as a Docker deployment — that's the expected way to use it. Prebuilt multi-arch images (linux/amd64 + linux/arm64) are published to Docker Hub at hikarisystems/slater, tagged:latest and:vX.Y.Z on every release: docker pull hikarisystems/slater:latest A Docker-command-only usage, configuration, and operations guide lives in DOCKERHUB.md (and is mirrored to the Docker Hub overview page) — start there if you're deploying. In short: # Build a graph generation with the offline writer: docker run --rm -v slater-data:/data -v "$PWD/dumps:/dumps:ro" \ --entrypoint /app/slater-build hikarisystems/slater:latest \ --input /dumps/people.cypher --graph people --data-dir /data # Serve it over Bolt on 7687 (read-only unless `delta.enabled`): docker run -d --name slater -p 7687:7687 \ -v slater-data:/data:ro -v "$PWD/acl.json:/config/acl.json:ro" \ hikarisystems/slater:latest To build the image locally instead (e.g. for development): # Build the image (both binaries). docker compose build # Serve (expects generations under the slater-data volume / your /data mount). docker compose up slater # Build a generation with the offline writer (profile `build`): docker compose run --rm builder \ --input /dumps/people.cypher --graph people --data-dir /data The builder stage installs cmake, clang and libclang-dev for the rustls aws-lc-rs backend; git (already in the base image) is required for the hs-utils git+tag dependency, which.cargo/config.toml fetches via the git CLI. The sections below cover the on-disk format, configuration, ACLs, and a local (non-Docker) worked example. How it works slater-build slater (Bolt server) dump.cypher ──────────▶ /data/ / / ──────────▶ neo4j driver (offline, atomic) MANIFEST.json, *.blk, (bolt / bolt+s) range/*.isam, vector/*.{vamana,pq}, current → A generation is one immutable directory: a MANIFEST.json (symbol tables, index descriptors, an optional encryption header), columnar block files (node_props.blk, node_labels.blk, edge_props.blk, topology.csr.blk, vectors.f32.blk), range indexes (range/.isam), above-threshold ANN indexes (vector/..{vamana,pq}), and a current text pointer. Every block is zstd-compressed and BLAKE3-checksummed; with --encrypt each block is additionally sealed with XChaCha20-Poly1305 (AEAD at rest). The server opens a generation by re-hashing every file against the manifest, so a half-copied / truncated image — a torn copy onto the data dir, which may be remote/network storage — is refused rather than served. Reads flow through three bounded cache pools — a decompressed-block LRU, a vector-index pool (resident PQ codes + a Vamana-block LRU), and a result LRU — each with its own byte budget. This is what keeps RSS flat. The writable layer With delta.enabled, the immutable generation becomes the fully-compacted bottom level (the "core") of a small log-structured-merge tree, and live writes ride on top of it: write (Bolt) read (Bolt) │ │ ▼ ▼ ┌──────────────┐ flush ┌──────────────┐ ┌──────────────────────┐ │ WAL + active │ ───────▶ │ L0 delta │ │ a query pins one │ │ memtable │ │ segments │ │ (core, delta) view │ └──────────────┘ └──────┬───────┘ │ and reads the merge │ (fsync = ack) │ └──────────────────────┘ consolidation │ (folds core + delta → fresh core) ▼ ┌─────────────┐ │ new core │ (atomic `current` swap) └─────────────┘ Durability floor — the WAL. Each mutation is serialised behind a single writer per graph, appended to a per-graph write-ahead log, and fsynced before the Bolt SUCCESS is returned — so acknowledged ⇒ durable, and a torn tail is dropped on replay. A batched write-UNWIND appends its rows and commits one fsync for the whole batch. The WAL is local disk only (it is not routed through the storage backend), which makes a writer node stateful: it needs a durable local volume at delta.walDir. Read replicas stay stateless. Memtable → L0 → consolidation. Writes accumulate in an in-RAM memtable (bounded by delta.memtableBytes); when it fills it flushes to an immutable L0 delta segment. A consolidation folds {core + delta} into a fresh core by serialising the merged view back through slater-build and swapping current atomically — the same content-hash guard as any published generation. Trigger it by hand with CALL slater.consolidate(), automatically at delta.deltaCorePercent of the core's size (optionally gated to an off-peak delta.consolidateWindow), or let the delta.deltaHardBytes throttle backstop runaway growth. The overlay sits below the read surface. The executor reads through a ReadView that is either the bare core (delta always empty) or a merged (core, delta) view; the engine is monomorphised over it, so an empty delta compiles to a single predictable branch and the read-only path is byte-identical. Whole-graph counters (count(*), label/reltype marginals) are served from the delta's own live counters, so they stay metadata reads even with writes pending. A query sees a stable snapshot. It pins one (core, delta) tuple for its whole life. There are no multi-statement transactions and no rollback — a write is a durable, business-key-addressed correction, not an OLTP transaction. The exact write grammar and knobs are in the Configuration table (delta.*) and the Worked example below. Range indexes (ISAM) A range index (range/.isam, one per indexed (label, property)) lets a MATCH (n:Label {prop: v}) or WHERE n.prop v resolve to the matching node ids without scanning the label. It is an ISAM (Indexed Sequential Access Method) structure — the classic static, sorted, block-structured index, which is exactly the right shape for an immutable generation: there are no inserts to rebalance, so the simplicity of ISAM buys what a B-tree's mutation machinery would only complicate. Entries (value, ) are sorted by value and packed into the same zstd-compressed 256 KiB blocks as everything else. A small resident top-level holds the first key of each block (a sparse index). A lookup binary-searches that in-memory top level to find the one block a key can be in, reads + decompresses that block, and scans it — so an equality lookup is one block read, and a range scan walks the contiguous run of blocks it spans. (This is why a meshUi-indexed lookup is single-digit milliseconds while the same match on an unindexed property scans the whole label.) The planner picks it via NodeScan::RangeEq / RangeRange; an unindexed predicate falls back to a label sweep or full scan, with the executor re-checking every predicate either way. Vector search (Vamana + PQ) — cosine, L2 and dot, read and write Vector KNN (db.idx.vector.queryNodes) runs over cosine, L2, or dot-product (MIPS) indexes. The base index is built offline with two execution paths, chosen per index by the --ann-threshold (default 50 000 vectors): Below the threshold — brute force. The full f32 vectors live in vectors.f32.blk; a query scans the index's group and computes the exact distance in the index's metric. Simple and exact; fine when the vector set is small. At or above the threshold — Vamana + PQ, the disk-native ANN path that keeps resident memory bounded regardless of how many vectors there are: Vamana is the graph index from the DiskANN line of work: a single proximity graph whose edges are pruned (the --vamana-r out-degree and --vamana-alpha long-edge factor) so a greedy beam search — start at the medoid, repeatedly hop toward the query, keeping a candidate list of width vectorQuery.beamWidth — reaches a node's true neighbours in few hops, i.e. few random block reads per query. The graph blocks (vector/..vamana) are paged in through the vector cache, not held wholesale. Product quantisation (PQ) compresses each vector into a short code (--pq-subspaces × --pq-bits): the dimensions are split into subspaces, each independently k-means-clustered, and the vector is stored as the tuple of nearest-centroid ids. These codes (vector/..pq) are small enough to keep resident, so the beam search scores candidates from RAM and only the chosen few full vectors are read from disk. That resident PQ set is what the cache.vectorCacheBytes pool pins. Writable embeddings — the vector write ladder (FreshDiskANN-style). An indexed embedding is a first-class writable value. SET n.embedding = vecf32([…]) (and REMOVE) lands in the write delta and is immediately KNN-visible with exact rank, then survives a segment flush, a merge, and a consolidation. A query merges up to three levels — the sealed base index, a sealed per-segment index, and an in-memory RW-index (a live mutable Vamana over the write delta) — so latency stays flat as writes accumulate rather than growing with the count of pending writes. A delete leaves a hole: the node stops being returned but stays a navigational waypoint until a background delete-consolidation splices it out of the graph, so deletes stop costing query IO. And because the on-disk graph addresses its neighbours by layout position rather than node id, CALL slater.consolidate() carries the Vamana by reference — hard-linked, byte-identical — and rewrites only a small id column, folding vector writes into the base without the O(N·R·L) graph rebuild. Measured numbers, with caveats, are in the performance report. Storage backends (filesystem / S3 / GCS) Every generation file is opened through an ObjectStore abstraction rather than std::fs directly, so the same on-disk byte format — blocks, indexes, manifest, current pointer — is served unchanged from any backend; only where the bytes come from differs, never the readers, the query engine, or the integrity checks. The hot path is positional reads (read_exact_at), which map onto a pread on a local file and an HTTP byte-range request on an object store — Slater never mmaps, so the explicit, bounded-read model is identical everywhere. Three first-class backends, selected by dataBackend.kind. The filesystem is the simple default; Amazon S3 and Google Cloud Storage are equal, fully supported object-store backends — the published image ships with both compiled in, so each is configuration-only, and a generation built once can be served from any of them (even migrated fs → S3 → GCS) without a rebuild. dataBackend.kind Positional read Integrity at open Credentials fs (default) pread full BLAKE3 re-hash of each file — s3 HTTP Range GET server SHA-256 via HEAD (→ BLAKE3 body re-hash if absent) config keys, AWS chain, or IAM role gcs HTTP range read server CRC32C via get_object (→ BLAKE3 body re-hash if absent) ADC / Workload Identity, or service-account JSON Both object stores verify integrity from the checksum the store already computes and keeps, fetched as object metadata: slater-build sends the checksum on upload (the store validates the bytes against it and stores it), and the server reads it back at open and compares it to the manifest — one metadata request per file, no body download. It is content-grade and identical in spirit across S3 (SHA-256) and GCS (CRC32C). When an object carries no server-stored checksum (copied in out-of-band, or uploaded with a different default), the server re-hashes the object body against the manifest BLAKE3 rather than trusting its byte length — a requested integrity check is never silently downgraded to a size comparison. Slater-published generations always carry the checksum, so they stay on the cheap metadata path. Filesystem (fs) The default, rooted at dataBackend.fs.dir. The right choice for most deployments: a generation on a local SSD (or an NFS/EBS mount) served read-only. Integrity is a full BLAKE3 re-hash of every file at open. Amazon S3 (s3) An S3 or S3-compatible bucket (AWS, MinIO, localstack). Credentials come first from config (dataBackend.s3.awsAccessKey / awsSecretKey, plus awsSessionToken for temporary STS credentials) and fall back to the standard AWS chain (AWS_ACCESS_KEY_ID / AWS_SECRET_ACCESS_KEY env, shared profile, or instance/IRSA role) when left empty. # serve from S3 (env-var form; see the config table for every key) dataBackend__kind=s3 dataBackend__s3__bucket=slater dataBackend__s3__region=eu-west-2 dataBackend__s3__awsAccessKey=… # omit to use the AWS chain / instance role dataBackend__s3__awsSecretKey=… # S3-compatible (e.g. MinIO): also set dataBackend__s3__endpoint=http://minio:9000 dataBackend__s3__pathStyle=true # required by most S3-compatible servers # publish a generation into the bucket (remote `current` pointer written last) slater-build --input people.cypher --graph people --data-dir /data \ --publish-s3-bucket slater --publish-s3-region eu-west-2 --publish-s3-prefix prod # MinIO: add --publish-s3-endpoint http://localhost:9000 --publish-s3-path-style Google Cloud Storage (gcs) A GCS bucket, reached over the JSON API. Authorization is GCP-native: by default it resolves Application Default Credentials — GKE Workload Identity, the GCE metadata server, or a gcloud / key. Set dataBackend.gcs.credentialsPath (a service-account JSON key file) or inline credentialsJson for an explicit key. dataBackend.gcs.endpoint points at a fake-gcs-server emulator, and dataBackend.gcs.anonymous=true enables unauthenticated access for that emulator only — never against real GCS. # serve from GCS (env-var form; see the config table for every key) dataBackend__kind=gcs dataBackend__gcs__bucket=slater dataBackend__gcs__prefix=prod dataBackend__gcs__credentialsPath=/secrets/sa.json # omit for ADC / Workload Identity # publish a generation into the bucket (remote `current` pointer written last) slater-build --input people.cypher --graph people --data-dir /data \ --publish-gcs-bucket slater --publish-gcs-prefix prod # explicit key: add --publish-gcs-credentials /secrets/sa.json In all cases slater-build writes the finished generation to --data-dir first (its local staging area) and additionally uploads it to the bucket; the remote current pointer is written last, so a serving node never sees a half-published generation. When to use an object store (S3 or GCS) Reach for s3 or gcs when you want generations in durable, central object storage rather than on a node's disk — typically: publish once and fan out to many stateless, disk-less server replicas that all read the same bucket; decouple the build host from the serve hosts; or lean on the store's durability/versioning/lifecycle instead of managing volumes. The trade-off is latency: a cold block is a network round-trip (~10–50 ms) instead of a local read (~0.1 ms). Slater hides most of it with the in-memory block cache, concurrent read-ahead, and the optional disk cache below. If your generations already sit on fast local storage and you don't need the central-bucket model, fs is simpler and faster. Local-disk block cache (object-store second tier) The in-memory BlockCache is deliberately small (bounded RSS is the headline guarantee), so on a working set larger than RAM the same blocks would be re-fetched from the object store on every spill. An optional local-SSD second cache tier fixes that: a block evicted from RAM is served from local disk (~0.1 ms) instead of a fresh object GET, surviving in-memory eviction and cutting object-store request count/cost — bringing an object-store-backed node close to local-filesystem performance once warm. It is opt-in for both s3 and gcs, enabled by setting dataBackend..diskCacheBytes > 0 and a writable diskCacheDir. It caches the sealed bytes exactly as fetched — already compressed, and (for --encrypt generations) still AEAD-sealed — below decrypt/decompress. The cache layer never holds the encryption key and never re-encrypts, so at-rest status is preserved for free: an encrypted generation lands on disk still sealed. Writes are write-behind: a miss returns the fetched bytes to the query immediately, then a background thread does the disk write and LRU trim, so the query path never blocks on disk I/O. Eviction keeps the cache within its byte budget; a per-file checksum verified on every read self-heals a corrupt cache file to a miss (→ refetch from the object store). diskCacheDir must point at a real writable volume — never tmpfs (tmpfs is RAM and would defeat the bounded-RSS guarantee). The in-memory index that tracks it costs a little RAM (~tens of bytes per cached block), which counts against your RSS ceiling — size the directory ≫ the in-memory block cache. The tier's other RAM cost is the write-behind queue, which stages blocks on their way to disk. It is bounded at blockCacheBytes / 8 (floored by diskCacheBytes) — 8 MiB at the default — and sheds rather than grows, so a cold scan cannot inflate it; a shed block simply refetches on its next miss. It needs no configuration: it scales with blockCacheBytes, so the disk tier adds no new number to the RSS budget beyond its index. Mounts A read replica runs with a read-only root filesystem and a non-root user (appuser:1000) — everything it needs is mounted read-only. A writer (delta.enabled) additionally needs one durable, writable volume for its WAL. Path Purpose Notes /data The graph generations ( / /… + current). Read-only for replicas; produced by slater-build. May live on remote/network storage (e.g. NFS), so reads are not assumed to be fast local-SSD latencies. /sandbox Per-environment config overlay + secrets. /sandbox/config.json is deep-merged over the baked-in config.json; also holds acl.json, TLS PEM material, the at-rest key file. /tmp, /run Scratch (tmpfs). A read replica never writes to disk by default. (writer) delta.walDir The write-ahead log + L0 delta segments, when delta.enabled. Writable, and a durable, real volume — never tmpfs (it is the durability floor). A relative path resolves under the data dir; give a writer its own persistent volume here. (optional) disk cache The local-disk block cache, when dataBackend.s3.diskCacheBytes / dataBackend.gcs.diskCacheBytes > 0. Writable, and a real volume — not tmpfs. Used by the s3 and gcs backends; see Storage backends. Environment / configuration Config is loaded by the house-standard layered loader: the baked-in config.json, then /sandbox/config.json deep-merged over it, then KEY__sub environment overrides (double underscore for nesting; keys match the camelCase config). Every configuration knob — its camelCase key, the KEY__sub environment override, its default, and what it does — is tabulated in the Configuration reference. The most-tuned knobs are the cache budgets (cache.*), the query guards (query.*), the connection caps (server.*), the storage backend (dataBackend.*), and the writable layer (delta.*). Resident memory is approximately blockCacheBytes + vectorCacheBytes + resultCacheBytes + a small fixed overhead (plus up to degreeColumnBytes for the lazy degree column, once the degree-sum count(endpoint) fast path is exercised), independent of graph size — that is the headline guarantee, exercised by the rss_stays_bounded_under_sustained_knn_load integration test. Per-connection buffers live outside the cache budgets, so the guarantee holds under adversarial load only because server.maxConnections bounds how many can exist at once. Network posture Slater is a read replica handle; the primary connection-security control is the network, not the binary. Bind it to a private interface, restrict source ranges at the network layer (security groups / NetworkPolicy), and — if it faces anything but trusted clients — front it with a connection-limiting L4 proxy (HAProxy maxconn + a per-source stick-table, or nftables connlimit + hashlimit). That sits before the file descriptor is ever handed to the process, so it is the most robust limit. The in-binary limits above (maxConnections, maxPreAuthConnections, maxConnectionsPerIp, the differential byte caps, and loginTimeoutMs) are defence-in-depth: they default on and generous so they are invisible to a legitimate client population, but they make the bounded-RSS guarantee hold even when the proxy is forgotten. See docs/HARDENING.md for the full defensive posture, and.md /.md for the canonical detail. Generation guard Slater polls each graph's current pointer every generationPollMs (poll, not inotify — the data dir may be remote/network storage like NFS, where filesystem change events are unreliable). When it changes: reloadStrategy=exit (default): the server logs fatal and exits non-zero so the orchestrator restarts it cleanly against the new generation. reloadStrategy=swap: the server opens and validates the new generation (same content-hash guard as boot), atomically swaps it in, and lets in-flight queries finish on the old one. A corrupt/incomplete new image is refused and the old generation keeps serving. ACL acl.json maps users to argon2id password hashes and per-graph read / write grants. Mint a hash (never store cleartext) with: slater hash-password 's3cret'