Nemo Python SDK Examples Guide

This directory contains 50 comprehensive, self-contained executable examples covering every operational capability, relational feature, SQL function, concurrency model, and analytical pipeline in the Nemo database engine (nemo-py).

Complete Index of Examples (1 – 50)

Category 1: Core Operations & Table Schemas

Example	Topic / Feature Demonstrated	Run Command
`example_1.py`	Basic synchronous CRUD, auto-increment keys, single inserts, batch `executemany`, updates, deletes, and instant `truncate()`.	`python example/example_1.py`
`example_2.py`	Asynchronous operations with `asyncio`, coroutine workers, `async with`, async batching, and `AsyncConnectionPool`.	`python example/example_2.py`
`example_3.py`	Demonstration of all 30 native SQL datatypes (Primitives, Collections, Temporals, Arrays, Dicts, JSON, UUID, IP/CIDR, Ranges).	`python example/example_3.py`
`example_4.py`	Built-in SQL functions for JSON extraction, array manipulation, dict lookup, string, math, and range boundaries.	`python example/example_4.py`
`example_5.py`	High-speed in-memory hash joins with selective column projection, SIMD aggregates, window ranking, and CBO query planning.	`python example/example_5.py`
`example_6.py`	High-velocity FIFO queue and LIFO stack operations using native atomic popping (`conn.pop()`) at 7M+ ops/second.	`python example/example_6.py`
`example_7.py`	Database sequences (`nextval`, `currval`) and atomic batch ID range reservation (`allocate_id_range`) for multi-worker ingestion.	`python example/example_7.py`
`example_8.py`	Direct columnar data extraction API (`conn.extract_columnar()`) bypassing row-by-row tuple wrapping for analytical pipelines.	`python example/example_8.py`
`example_9.py`	ACID transactions, automatic rollbacks on exceptions, manual commits/rollbacks, and snapshot isolation.	`python example/example_9.py`
`example_10.py`	Multi-threaded synchronous connection pooling, thread-safe lease acquisition, and concurrent stress testing with zero lock collisions.	`python example/example_10.py`

Category 2: Prepared Statements & Metadata Reflection

Example	Topic / Feature Demonstrated	Run Command
`example_11.py`	Parameterized queries (`?`), SQL injection prevention, and reusable prepared statements (`conn.prepare()`).	`python example/example_11.py`
`example_12.py`	Query performance diagnostics and execution profiling via `result.statistics` (buffer hits, planning/execution times).	`python example/example_12.py`
`example_13.py`	Schema reflection and column metadata inspection (`result.columns`, types, table origin, nullability).	`python example/example_13.py`
`example_14.py`	High-speed table truncation and instantaneous O(1) storage reclamation (`conn.truncate()`).	`python example/example_14.py`

Category 3: Datatypes & Rich Encodings

Example	Topic / Feature Demonstrated	Run Command
`example_15.py`	Numeric datatypes deep-dive (`TINYINT`, `SMALLINT`, `INTEGER`, `BIGINT`, `REAL`, `DOUBLE`, boundary testing).	`python example/example_15.py`
`example_16.py`	Exact financial decimals (`DECIMAL`), preventing binary floating-point rounding errors in banking ledgers.	`python example/example_16.py`
`example_17.py`	Text, international Unicode, and raw cryptographic binary blobs (`VARCHAR`, `TEXT`, `BYTEA`).	`python example/example_17.py`
`example_18.py`	Temporal types and time-series scheduling (`DATE`, `TIME`, `TIMESTAMP`, `TIMESTAMPTZ`, `INTERVAL`).	`python example/example_18.py`
`example_19.py`	Semi-structured JSON and binary JSONB document storage, querying, and updates.	`python example/example_19.py`
`example_20.py`	Native structured collection types: ordered dynamic `ARRAY` and key-value `DICT`.	`python example/example_20.py`
`example_21.py`	System & networking types: `UUID`, IP addresses (`INET`, `CIDR`), MAC addresses, and `RANGE` intervals.	`python example/example_21.py`

Category 4: In-Built SQL Functions

Example	Topic / Feature Demonstrated	Run Command
`example_22.py`	Native JSON SQL functions: `json_extract`, `json_length`, `json_contains`, and `json_set`.	`python example/example_22.py`
`example_23.py`	Collection SQL functions: `array_get`, `array_sum`, `array_avg`, `array_append`, `dict_get`, `dict_has_key`.	`python example/example_23.py`
`example_24.py`	String SQL functions: `upper`, `lower`, `concat`, `substring`, and `trim`.	`python example/example_24.py`
`example_25.py`	Mathematical SQL expressions: `abs`, `round`, `sqrt`, `power`, and `mod`.	`python example/example_25.py`
`example_26.py`	Range, network & utility functions: `range_lower`, `range_upper`, `range_contains`, `host`, `netmask`, `coalesce`.	`python example/example_26.py`

Category 5: Relational & Analytical Queries

Example	Topic / Feature Demonstrated	Run Command
`example_27.py`	Multi-table relational hash joins with predicate pushdown and selective column projections.	`python example/example_27.py`
`example_28.py`	SIMD vectorized aggregate processing (AVX2/NEON 1024-element chunks for `SUM`, `AVG`, `MIN`, `MAX`).	`python example/example_28.py`
`example_29.py`	Analytical window ranking functions: `ROW_NUMBER()` and `RANK() OVER (ORDER BY ...)`.	`python example/example_29.py`
`example_30.py`	Cost-Based Optimizer (CBO) and physical plan tree inspection with `conn.explain()`.	`python example/example_30.py`
`example_31.py`	Common Table Expressions (CTEs) using `WITH ... AS (...)` for modular queries.	`python example/example_31.py`
`example_32.py`	Filter expressions and range queries using `BETWEEN`, equality, and compound conditions.	`python example/example_32.py`

Category 6: Queues, Stacks & Sequences

Example	Topic / Feature Demonstrated	Run Command
`example_33.py`	High-velocity background task processing with atomic FIFO queues (`conn.pop(..., 'fifo')`).	`python example/example_33.py`
`example_34.py`	Document revision undo stack and audit log popping using atomic LIFO operations (`conn.pop(..., 'lifo')`).	`python example/example_34.py`
`example_35.py`	Standalone atomic database sequences with `nextval('seq')` and `currval('seq')`.	`python example/example_35.py`
`example_36.py`	Zero-contention parallel ID range reservation blocks using `conn.allocate_id_range()`.	`python example/example_36.py`

Category 7: High-Speed Extraction & Enterprise Architecture

Example	Topic / Feature Demonstrated	Run Command
`example_37.py`	High-speed columnar data extraction into typed `{col_name: [values]}` dictionaries for analytical pipelines.	`python example/example_37.py`
`example_38.py`	Storage-level pushdown filtering and column projection using `conn.extract()`.	`python example/example_38.py`
`example_39.py`	ACID transaction safety, context manager auto-commit, and automatic rollback on Python exceptions.	`python example/example_39.py`
`example_40.py`	End-to-end e-commerce microservice platform integrating catalogs, orders, fulfillment queue, and SIMD analytics.	`python example/example_40.py`

Category 8: Advanced Engine Mechanics & Architecture Deep-Dive

Example	Topic / Feature Demonstrated	Run Command
`example_41.py`	Slotted page storage architecture: 4096-byte blocks, bottom-up record packing, and tombstone slot reuse without fragmentation.	`python example/example_41.py`
`example_42.py`	Buffer pool CLOCK eviction algorithm, second-chance referencing bits, and warm memory page cache hit ratios.	`python example/example_42.py`
`example_43.py`	Multi-Version Concurrency Control (MVCC) snapshot isolation: simultaneous readers and writers with zero lock contention.	`python example/example_43.py`
`example_44.py`	Write-Ahead Logging (WAL) and ARIES crash recovery mechanics: redo logs, undo rollback chains, and transaction durability.	`python example/example_44.py`
`example_45.py`	Cost-Based Optimizer (CBO) statistical distributions via `conn.analyze()` and physical query plan trees via `conn.explain()`.	`python example/example_45.py`
`example_46.py`	In-memory projected hash join algorithms: build and probe phases with column projection pushdown.	`python example/example_46.py`
`example_47.py`	Vectorized SIMD aggregations in 1024-element aligned CPU chunks (AVX2 / NEON) for ultra-fast `SUM`, `AVG`, `MIN`, `MAX`.	`python example/example_47.py`
`example_48.py`	Contention-free multi-worker partitioning using sequence atomic block reservations via `conn.allocate_id_range()`.	`python example/example_48.py`
`example_49.py`	B+ Tree indexing anatomy: point query index lookups vs contiguous sibling leaf pointer range scans (`BETWEEN`).	`python example/example_49.py`
`example_50.py`	Comprehensive master architecture showcase uniting pooling, transactions, hash joins, SIMD, queues, and columnar extraction.	`python example/example_50.py`

Running the Examples

Each example is a standalone executable script that creates an isolated local database, executes the scenario, prints diagnostic output, and cleans up its database upon exit:

# Run any individual example
python example/example_1.py
python example/example_20.py
python example/example_40.py
python example/example_50.py

# Run all 50 examples sequentially
python -c "import subprocess, sys; [subprocess.run([sys.executable, f'example/example_{i}.py'], check=True) for i in range(1, 51)]"

Nemo Examples