sekejap

sekejap is a graph-first, embedded multimodel database that stores your data in several forms at once: plain records, graph relationships, geographic shapes, vectors, and full text. You can query and combine them in a single SQL statement.

It runs inside your application, like SQLite, with no separate server to install or manage. Store your database on local disk for lightweight and offline use, or use S3-compatible object storage for datasets that grow beyond a single machine.

(“sekejap” is Indonesian for “a brief moment”, reflecting how quickly you can set it up and start working with your multimodel data.)

It's available as a Rust/Python/Dart/Kotlin/Swift/Java/Node.js/Go library, and a command-line tool.

📖 Documentation: docs/ — a usage guide (the query language, including the SELECT … FROM MATCH graph reference) and a developer guide (architecture diagrams, storage, indexes, invariants).

📄 Paper: the sekejap architecture and its six-category benchmark (relational · graph · spatial · vector · full-text · hybrid, vs SQLite, DuckDB, PostgreSQL/PostGIS/pgvector, Neo4j, ArangoDB, Qdrant, Redis, Elasticsearch, Solr, Meilisearch) are described in a paper submitted to CIDR 2027. Reproduction harnesses, datasets, and the measured results live in eval/.

Why you might want it

Applications often need more than one kind of database at the same time:

  • a relational store for structured records,
  • a graph database for relationships ("who is connected to what"),
  • a spatial index for location queries ("what's near me"),
  • a vector store for similarity search over embeddings,
  • a full-text search engine for matching words in text.

Running and keeping all of those in sync is a lot of moving parts. sekejap puts them in one embedded engine behind one query language, so a single query can use several of them together.

It's a good fit for:

  • Local and mobile apps — runs in-process with no server and a small footprint, so it works offline on phones and edge devices.
  • Hybrid search and RAG — rank results by combining vector similarity, geographic location, and text relevance in a single query, then follow the graph to pull in related records as context for a model.
  • On-device memory for AI — an agent or robot records what it observes (place, time, a note, a perception vector) as it happens, and later recalls it by any mix of location, similarity, and relationships — a private, queryable memory with no network round-trip.

Install

Each language has its own tutorial — install, first query, and (where available) the typed, reactive API. Index: docs/usage/bindings/.

Python — PyPI · tutorial

pip install sekejap                 # includes S3 support

Rust — crates.io · tutorial

cargo add sekejap                   # library
cargo add sekejap --features s3     # library, with S3 support
cargo install sekejap-cli           # command-line tool

Node.js / TypeScript — npm · typed API tutorial

npm install sekejap                 # prebuilt native binaries, no toolchain needed

Dart / Flutter — pub.dev · typed API tutorial

flutter pub add sekejap             # or: dart pub add sekejap

Kotlin / Android — Maven Central · typed API tutorial

// build.gradle.kts — Android app (typed, reactive; the AAR bundles the native library)
plugins {
  id("com.google.devtools.ksp")
}
dependencies {
  implementation("life.sekejap:sekejap-android:0.16.2")   // typed API + native library
  ksp("life.sekejap:sekejap-processor:0.16.2")            // @SekejapEntity codegen
}
// Desktop / server JVM: life.sekejap:sekejap  (+ sekejap-ffm for the Panama binding)

Swift — build from source for now (see wrappers/swift/ · tutorial); a dedicated SwiftPM package repository is planned.

Go — Go modules (tutorial)

go get github.com/sekejapdb/sekejap/wrappers/go

C / C++ and other native callers — the stable C ABI in wrappers/c/ (tutorial; build from source; ships a single header + static/shared library).

A first look

The examples below all use the same small dataset: some tourists, the flights they arrived on, and places, restaurants, and dishes to visit and eat.

1. Create some tables

A table needs a _key column as its primary key. Other columns can be ordinary types (TEXT, INTEGER, REAL, TIMESTAMPTZ) or one of the special ones: GEO for geography, VECTOR for embeddings.

from sekejap import DB

db = DB("./bali")   # a directory on disk; created if it doesn't exist

db.execute("""
    CREATE TABLE tourists (
        _key      TEXT PRIMARY KEY,
        name      TEXT,
        home_city TEXT,
        arrival   TIMESTAMPTZ,
        taste     VECTOR          -- an embedding of what this person likes
    )
""")
db.execute("CREATE TABLE flights     (_key TEXT PRIMARY KEY, airline TEXT, duration_hours INTEGER)")
db.execute("CREATE TABLE restaurants (_key TEXT PRIMARY KEY, name TEXT, area TEXT, geometry GEO)")
db.execute("""
    CREATE TABLE dishes (
        _key TEXT PRIMARY KEY, name TEXT, price INTEGER, protein_g INTEGER,
        description TEXT, geometry GEO, open_now BOOLEAN, embedding VECTOR
    )
""")

2. Add indexes for the query types you'll use

An index makes a certain kind of lookup fast. You only need the ones your queries actually use.

db.execute("CREATE INDEX ON dishes USING spatial (geometry)")     # location queries
db.execute("CREATE INDEX ON dishes USING bm25    (description)")  # text relevance
db.execute("CREATE INDEX ON tourists USING hnsw  (taste)")        # vector similarity

3. Insert data

db.execute("INSERT INTO tourists (_key, name, home_city, arrival) VALUES ('chloe', 'Chloe', 'Melbourne', '2024-06-01')")
db.execute("INSERT INTO tourists (_key, name, home_city, arrival) VALUES ('aiym',  'Aiym',  'Almaty',    '2024-06-02')")

# A relationship (edge): tourist "chloe" flew on flight "qf-mel".
db.execute("INSERT ('tourists/chloe')-[:flew_on]->('flights/qf-mel')")

4. Run a query

Ordinary SQL works as you'd expect:

db.query("SELECT name, home_city FROM tourists WHERE home_city = 'Melbourne'")
# → { name: "Chloe", home_city: "Melbourne" }

That's the whole loop: create tables, add the indexes you need, insert rows and relationships, and query. The rest of this README shows what each data model can do, then how to combine them.

The five data models

Each section is a short, self-contained example. They build toward the last one, where several models are used in a single query.

Records and filters (SQL)

Standard SQL — SELECT, WHERE, ORDER BY, GROUP BY, aggregates.

db.query("""
    SELECT area, COUNT(*) AS n
    FROM restaurants
    GROUP BY area
    ORDER BY n DESC
""")

Relationships (graph)

A relationship between two rows is called an edge. You query edges with a MATCH pattern inside FROM. Everything around the MATCH is ordinary SQL.

# Follow one edge: which flight did Chloe arrive on?
db.query("""
    SELECT f.airline AS airline, f.duration_hours AS hours
    FROM MATCH (t:tourists)-[:flew_on]->(f:flights)
    WHERE t._key = 'chloe'
""")

The pattern reads left to right: start at a tourists row (t), follow a flew_on edge, arrive at a flights row (f). The arrow direction matters — -[:e]-> follows edges forward, <-[:e]- follows them backward.

You can follow a chain of several hops, and *1..3 means "between 1 and 3 hops":

# Places reachable within 2 "near" hops of somewhere Chloe visited.
# DISTINCT removes duplicates when a place can be reached more than one way.
db.query("""
    SELECT DISTINCT p._key AS place
    FROM MATCH (c:tourists)-[:visited]->(m:places)-[:near*1..2]->(p:places)
    WHERE c._key = 'chloe'
""")

Location (spatial)

A GEO column holds a shape (a point, line, or polygon). With a spatial index you can ask distance and containment questions.

# Restaurants within 5 km of a point (longitude, latitude).
db.query("""
    SELECT name FROM restaurants
    WHERE ST_DWithin(geometry, POINT(115.168 -8.690), 5000.0)
""")

Similarity (vector)

A VECTOR column holds an embedding — a list of numbers that captures the "meaning" of something. With an HNSW index you can find the rows whose vectors are closest to a given one.

# The 5 tourists whose taste is most similar to a given taste vector.
db.query("""
    SELECT name FROM tourists
    WHERE VECTOR_NEAR(taste, [0.9, 0.1, 0.0, 0.0], 5)
""")

Text (full-text)

For matching words in text, sekejap offers three tools:

  • ILIKE '%word%' — simple substring match (fast with a gin index).
  • BM25(field, 'query') — relevance scoring, like a classic search engine.
  • SEARCH('query') — a positional search index with typo tolerance.
# Dishes whose description is relevant to "grilled chicken", best first.
db.query("""
    SELECT name FROM dishes
    WHERE BM25(description, 'grilled chicken') > 0.0
    ORDER BY BM25(description, 'grilled chicken') DESC
""")

Time

Timestamps are ordinary columns; a few helper functions work on them.

db.query("""
    SELECT name, AGE_DAYS(arrival) AS days_here, NOW() AS current_time
    FROM tourists WHERE _key = 'chloe'
""")
# → { name: "Chloe", days_here: 5, current_time: "2024-06-06T09:00:00Z" }

Combining models in one query

This is the point of a multi-model database: asking one question that would otherwise need several systems.

"What should Chloe order for delivery right now?" — a dish that is near her, still open, in her price range, has enough protein, matches a craving, and is ranked by how well it fits both the words she typed and her taste.

db.query("""
    SELECT r.name AS restaurant, d.name AS dish, d.price AS price
    FROM MATCH (r:restaurants)-[:serves]->(d:dishes)
    WHERE d.open_now = true
      AND d.price BETWEEN 40000 AND 90000                        -- price range (IDR)
      AND d.protein_g >= 25                                      -- enough protein
      AND ST_DWithin(d.geometry, POINT(115.168 -8.690), 5000.0)  -- within 5 km (metres)
      AND BM25(d.description, 'grilled chicken healthy') > 0.0    -- matches the craving
    ORDER BY BM25(d.description, 'grilled healthy') * 0.6         -- text relevance
           + VECTOR_COSINE(d.embedding, [0.7,0.3,0.0,0.0]) * 0.4 -- taste similarity
      DESC
    LIMIT 10
""")

The WHERE clause narrows the results using the graph, spatial, scalar, and text models. The ORDER BY combines a text score and a vector score into one ranking. The whole thing is one statement.

A second example — a personal journal where each entry records a place, a time, some text, and a "mood" vector. Because the entries are just rows (and can be linked into the graph), you can search them by text, by similarity, or by time:

db.execute("""
    CREATE TABLE diary (
        _key TEXT PRIMARY KEY, author TEXT, place TEXT,
        logged_at TIMESTAMPTZ, reflection TEXT, mood VECTOR
    )
""")
db.execute("CREATE INDEX ON diary USING search (reflection)")   # search the text
db.execute("CREATE INDEX ON diary USING hnsw   (mood)")         # find similar moods

# "Where did I write about feeling small?" — text search over the entries.
db.query("""
    SELECT place, logged_at FROM diary
    WHERE author = 'chloe' AND SEARCH('small still')
    ORDER BY logged_at
""")

# "Find an earlier moment that felt like tonight." — nearest mood vector.
db.query("""
    SELECT place, reflection FROM diary
    WHERE author = 'chloe'
    ORDER BY mood <=> [0.2, 0.7, 0.1, 0.0] ASC
    LIMIT 1
""")

Data types

Type SQL keyword Stored as Use for
Text TEXT UTF-8 string names, categories, keys
Integer INTEGER 64-bit integer prices, durations, counts
Float REAL 64-bit float scores, ratings, weights
Boolean BOOLEAN true / false flags, toggles (e.g. open_now)
Timestamp TIMESTAMPTZ ISO-8601 date/time arrivals, log times
Geometry GEO GeoJSON shape points, areas, routes
Vector VECTOR list of floats embeddings (taste, mood, images)
JSON JSON arbitrary JSON nested / unstructured data
  • GEO accepts any GeoJSON geometry — Point, Polygon, LineString, MultiPolygon.
  • VECTOR is written as an array literal: [0.12, -0.03, 0.87, ...].

Indexes

An index speeds up one kind of query. Create only the ones you need.

Index USING keyword Makes this fast
Hash hash equality: field = 'x', IN (...)
B-tree btree ranges and ordering: >, <, BETWEEN, ORDER BY
GIN gin substring text match: ILIKE '%pattern%'
Spatial spatial location: ST_DWithin, ST_Contains, ST_Within, ST_Intersects
HNSW hnsw vector similarity: VECTOR_NEAR(...), <=> ordering
BM25 bm25 ranked text search: BM25(field, 'query')
Search search positional, typo-tolerant search: SEARCH('query') 
CREATE INDEX ON dishes   USING spatial (geometry)
CREATE INDEX ON dishes   USING bm25    (description)
CREATE INDEX ON diary    USING search  (reflection)
CREATE INDEX ON tourists USING hnsw    (taste)

All index types survive a restart. After a large bulk load, run compact() (.compact in the CLI) so later startups are fast. A single index can be rebuilt in place with REINDEX ON <table> USING <index> (<field>).

Interfaces

sekejap has three ways to use it. They query the same database.

SQL

The main interface. A quick tour of what the dialect supports:

-- Schema
CREATE TABLE places (_key TEXT PRIMARY KEY, name TEXT, category TEXT, geometry GEO)
ALTER TABLE places ADD COLUMN rating REAL
ALTER TABLE places RENAME COLUMN category TO kind

-- Rows
INSERT INTO places (_key, name, category) VALUES ('uluwatu', 'Uluwatu Temple', 'temple')
UPDATE places SET rating = 4.8 WHERE _key = 'uluwatu'
DELETE FROM places WHERE kind = 'closed'

-- Edges, optionally with properties
INSERT ('tourists/chloe')-[:visited {rating: 4.8, hours: 2}]->('places/uluwatu')
DELETE ('tourists/chloe')-[:visited]->('places/uluwatu')

-- Graph traversal: forward -[:e]-> and backward <-[:e]-
SELECT dest._key AS place
FROM MATCH (a:places)-[:near*1..3]->(dest:places)
WHERE a._key = 'seminyak-beach'

-- Aggregation over a pattern: COUNT / SUM / AVG / MIN / MAX, and COUNT(DISTINCT ...)
SELECT p._key AS place, COUNT(DISTINCT t.home_city) AS cities
FROM MATCH (p:places)<-[:visited]-(t:tourists)
GROUP BY p._key
ORDER BY cities DESC

-- Edge properties: a named edge (-[v:type]->) exposes its rating + metadata
SELECT t.name AS visitor, v.rating AS rating
FROM MATCH (p:places)<-[v:visited]-(t:tourists)
WHERE p._key = 'uluwatu'
ORDER BY v.rating DESC

-- Multi-stage traversal: carry a result into a follow-on MATCH with WITH
SELECT d.name AS dish, COUNT(*) AS orders
FROM MATCH (c:tourists)-[:similar_taste]->(peer:tourists)
WHERE c._key = 'chloe'
WITH peer
MATCH (peer)-[:ate]->(d:dishes)
GROUP BY d.name

-- Shortest path: 0 rows if unreachable, 1 row if a path exists
SELECT a.name AS from_n, b.name AS to_n, length(r) AS hops, nodes(r) AS via
FROM MATCH SHORTEST (a:tourists)-[r*]->(b:dishes)
WHERE a._key = 'chloe' AND b._key = 'betutu-chicken'

-- Spatial, vector, and text
SELECT * FROM places   WHERE ST_DWithin(geometry, POINT(115.168 -8.690), 5000.0)
SELECT * FROM tourists WHERE VECTOR_NEAR(taste, [0.9, 0.1, 0.0, 0.0], 5)
SELECT * FROM places   WHERE name ILIKE '%uluwatu%'

-- Transactions: all statements commit together, or none do
BEGIN
INSERT ('tourists/chloe')-[:booked]->('flights/qf-mel')
INSERT ('tourists/chloe')-[:stayed_at]->('villas/seminyak-01')
COMMIT

-- Inspect the database
SHOW TABLES
SHOW EDGES FROM tourists TO places

Rust

Besides raw SQL, the Rust library has a builder API for lower-level control:

use sekejap::CoreDB;

let mut db = CoreDB::open("./bali")?;

// Restaurants within 3 km of a point (latitude, longitude, metres).
let nearby = db.collection("restaurants")
    .st_dwithin(-8.690, 115.168, 3000.0)
    .collect();

// Filter and sort.
let picks = db.collection("dishes")
    .where_gte("protein_g", 25.0)
    .sort("price", true)   // true = ascending
    .take(10)
    .collect();

// Add a plain edge.
db.link("tourists/chloe", "places/uluwatu", "visited");

// Add an edge with attributes (any names; primitives are stored efficiently).
db.link_meta("tourists/chloe", "places/uluwatu", "visited", r#"{"rating": 4.8, "hours": 2}"#)?;

Python (with pandas)

The Python library can load from and return pandas DataFrames:

import pandas as pd
from sekejap import DB

db = DB("./bali")

# Load a DataFrame as rows in a table.
df = pd.read_csv("tourists.csv")
db.df.load_nodes(df, "tourists", id_col="tourist_id",
                 mapping={"tourist_id": "_key", "full_name": "name"})

# Get query results back as a DataFrame.
result = db.df.query("SELECT * FROM dishes WHERE protein_g >= 25")

Data larger than local disk (S3)

sekejap can keep its data on S3-compatible storage and fetch pieces on demand, so you can query datasets bigger than the local disk. Works with AWS S3, MinIO, Cloudflare R2, and other S3-compatible stores.

from sekejap import DB

db = DB.open_s3("s3://my-bucket/bali",
                access_key_id="AKID...", secret_access_key="secret...",
                region="ap-southeast-1",
                cache_budget_bytes=256 * 1024 * 1024)   # in-memory cache size

# MinIO / R2 / other S3-compatible stores: point at their endpoint
db = DB.open_s3("s3://my-bucket/bali", "AKID...", "secret...", "auto",
                cache_budget_bytes=256 * 1024 * 1024,
                endpoint="https://<account>.r2.cloudflarestorage.com")

db.query("SELECT * FROM places WHERE ST_DWithin(geometry, POINT(115.168 -8.690), 10000.0)")

Command-line tool

sekejap                                   # in-memory session
sekejap ./bali                            # open a database on disk
sekejap ./bali "SELECT * FROM places;"    # run one statement and exit
echo "SELECT ...;" | sekejap ./bali       # pipe in a script

Inside the interactive session:

sekejap> CREATE TABLE places (_key TEXT, name TEXT, geometry GEO);
sekejap> SELECT * FROM places WHERE ST_DWithin(geometry, POINT(115.168 -8.690), 5000.0);
sekejap> .tables          # list tables
sekejap> .schema places   # show a table's columns
sekejap> .compact         # compact the database after a big load
sekejap> .help

License

MIT

Project links

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Release history Release notifications | RSS feed

0.16.5

19 files

0.16.4

19 files

This release

0.16.2 This release

19 files

0.16.1

19 files

0.16.0

19 files

0.15.0

19 files

0.14.0

19 files

0.13.5

19 files

0.13.4

19 files

0.13.3

19 files

0.13.2

19 files

0.13.1

19 files

0.13.0

19 files

0.12.1

19 files

0.12.0

19 files

0.11.3

19 files

0.10.1

19 files

0.10.0

19 files

0.9.1

19 files

0.8.18

19 files

0.8.17

19 files

0.8.16

19 files

0.8.15

19 files

0.8.14

19 files

0.8.13

19 files

0.8.11

19 files

0.8.10

19 files

0.8.9

19 files

0.8.8

19 files

0.8.7

19 files

0.8.6

19 files

0.8.4

19 files

0.8.2

19 files

0.8.1

19 files

0.8.0

19 files

0.7.0

19 files

0.6.7

19 files

0.6.6

19 files

0.6.5

19 files

0.6.4

19 files

0.6.3

19 files

0.6.0

19 files

0.5.3

19 files

0.5.1

12 files

0.5.0

12 files

0.4.0

12 files

0.3.0

12 files

0.2.4

12 files

0.2.3

12 files

0.2.0

12 files

0.1.8

12 files

0.1.6

12 files

0.1.5

1 file

0.1.4

1 file

0.1.3

1 file

0.1.2

1 file

0.1.1

1 file

0.1.0

1 file

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