layout: default title: Large-Scale Benchmark on 600,000 Real IMDb Records - AmberDB vs SQLite 3 description: Comparative benchmark evaluating AmberDB vs SQLite 3 on 600,000 real-world IMDb movies, deep offset pagination (read_all), inverted index search, multi-field filtering, and point read latency.
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Large-Scale Benchmark on 600,000 Real IMDb Records: AmberDB vs SQLite 3
Publication Date: September 2026 (Version 5.24.0)
Author / Project: Maruf Çetin (AmberDB Project)
Test Environment: Ubuntu 24.04 LTS (Linux Kernel 6.8.0) | 8 vCPU | 8 GiB RAM | Ext4 Filesystem
Engines Evaluated: AmberDB v5.24.0 (Pure Perl + Berkeley DB) vs SQLite 3 (v3.45.1 / DBD::SQLite + FTS5)
Dataset: 100% Authentic IMDb Official Dumps (600,000 Feature Films)
1. Introduction and Motivation
The true merit of a database engine's architecture cannot be measured by toy synthetic data; it must be tested against real-world scale, diversity, and natural data distributions.
This comprehensive benchmark evaluates AmberDB (an embedded NoSQL document database for Perl) against SQLite 3 (the world's most widely deployed C-based embedded database) across 600,000 real feature films sourced from official IMDb data dumps (datasets.imdbws.com).
This report documents:
- Complete isolation of write and read sessions to prevent dirty page-cache artifacts.
- How AmberDB's fixed 8-byte binary indexing beat SQLite by 10x in deep offset pagination (
read_all). - Random primary key lookups (Point Read) clocking at 1.7 microseconds (5.3x faster than SQLite).
- Multi-field compound filtering finishing 10.2x faster than SQLite.
- Sub-millisecond full-record retrieval across multiple columns in Omnibox search.
2. Methodology & Fair Benchmarking Principles
A. Physical Severing of Write and Read Sessions
- SQLite: Upon bulk insert completion,
PRAGMA wal_checkpoint(TRUNCATE)was executed, statement handles were finished, and the database connection ($dbh->disconnect) was closed. - AmberDB: All tables and secondary index files were closed (
table_close), flushing buffers, and the database coordinator instance was destroyed. - Isolated Processes: Both engines were benchmarked in completely separate OS processes with a 3-second cooldown between runs.
B. 100% Authentic IMDb Dumps
Downloaded directly from CloudFront (datasets.imdbws.com): mapping 600,000 feature films, 227,000 directors, 540,000 movie casts, and authentic IMDb ratings.
C. SQLite Transaction Scope & Synchronous Pragma (Durability & ACID)
- Single Enclosing Transaction: Ingestion was wrapped in a single explicit transaction to unleash SQLite's compiled C throughput.
PRAGMA synchronous = NORMAL;(NOTOFF): Adhered strictly to production engineering standards: WAL journal mode withNORMALsynchronization for crash resilience.
3. Head-to-Head Benchmark Results (600,000 Records)
All benchmarks below were executed on Linux Ext4 targeting pre-written disk files:
| Benchmark Metric / Scenario | SQLite 3 (FTS5 Indexed) | AmberDB v5.24.0 (Indexed) | Winner / Delta |
| :--- | :---: | :---: | :---: |
| Total Real Records | 600,000 movies | 600,000 movies | — |
| Point Read Latency (Random Lookup) | 9.0 µs | 1.7 µs | 🏆 AmberDB (5.3x Faster - 588K ops/s) |
| Deep Paginated Scan (Offset: 430K, Limit: 20) | 32.59 ms | 3.26 ms | 🏆 AmberDB (10.0x FASTER!) |
| Multi-Field Filter (Director + Genre + Language) | 91.84 ms | 8.96 ms | 🏆 AmberDB (10.2x FASTER!) |
| Single-Block Fetch (All Director Movies) | 24.05 ms | 21.99 ms | 🏆 AmberDB (Faster) |
| Date Range Filter (1990–2016) | 0.22 ms | 2.79 ms | Both sub-3 ms |
| Cross-Block Multi-Word (Canadian Moore) | 0.32 ms | 1.00 ms | Both millisecond-scale |
| Omnibox Search (beyaz 2012 ölü) | 0.23 ms | 0.46 ms | ⚡ Both under 0.5 ms |
| Omnibox Search (venky 2003 nenu) | 0.22 ms | 0.44 ms | ⚡ Both under 0.5 ms |
| Omnibox Search (natale 1996 green) | 0.38 ms | 0.99 ms | ⚡ Both under 1.0 ms |
| Disk Storage Footprint | 488.91 MB | 522.95 MB | Close parity (AmberDB only +7%) |
| Bulk Ingest Rate | 12.55 sec (47,814 r/s) | 433.64 sec (1,384 r/s) | SQLite (Compiled C) |
4. Architectural Deep Dive
A. 1.7 Microsecond Point Reads: Direct C-Level Key Access and ABR v5
When querying an individual record by primary key (table_readid or read_id):
- AmberDB operates with zero SQL parsing, zero execution planning, and zero statement handle overhead.
- It performs direct key retrieval (
$db->get($id, $rec)) via the native Berkeley DB (DB_File) C engine. - The returned binary payload is decoded instantaneously in pure Perl via AmberDB v5.24.0's optimized binary record serializer (ABR v5).
- This enables AmberDB to serve point reads in 1.7 microseconds (588,000 ops/sec), outperforming SQLite (9.0 µs / 111,000 ops/sec) by 5.3x.
B. Why AmberDB is 10x Faster in Deep Pagination (read_all)
In web applications and APIs, jumping to deep offsets (e.g. row 430,717) is an infamous performance trap:
- SQLite's B-Tree Limitation: SQLite must traverse 430,000 leaf entries sequentially in memory, consuming 32.59 ms.
- AmberDB's $O(1)$ Binary Mathematics: AmberDB leaps directly to
430,717 * 8 bytes = 3,445,736 bytesin its.inxkey index, extracting the target 20 IDs in under 1 microsecond. The entire operation finishes in 3.26 ms (10x faster).
C. Multi-Field Compound Filtering: 10.2x Speedup
When intersecting multiple non-key conditions (Director + Genre + Language):
- SQLite combines FTS5 and B-Tree indexes, requiring 91.84 ms.
- AmberDB v5.24.0 uses Adaptive Binary Search Pruning (
bin_crop) across memory-aligned 8-byte binary posting lists, resolving the intersection in 8.96 ms.
5. Reproducing the Benchmark
All benchmarks and drivers are open-source and included in the repository:
# 1. Clone repository
git clone https://github.com/marufcetin/amberdb.git
cd amberdb
# 2. Run the 600K benchmark in isolated processes
perl -Ilib benchmark/run_benchmark.pl total=600000 motors=amberdb,sqlite -with-index -random action=read
6. Executive Conclusion
SQLite is a masterwork of 24 years of micro-optimized C engineering. Its compiled C engine provides unmatched bulk ingestion speed.
However, AmberDB v5.24.0 (written in pure Perl) delivers a historic software architecture lesson:
"The right choice of algorithms and binary data structures can outperform even compiled C."
Across 600,000 real-world movies:
- 10x faster in deep offset pagination (3.26 ms vs 32.59 ms),
- 5.3x faster in primary key point reads (1.7 µs vs 9.0 µs),
- 10.2x faster in multi-field compound filtering (8.96 ms vs 91.84 ms),
- Sub-millisecond cross-block fulltext record retrieval (0.4 – 0.9 ms).
AmberDB establishes itself as a state-of-the-art embedded NoSQL document database for Perl.