gatling 0.3.0.25.9

A unified I/O management toolkit supporting threads, coroutines, files, databases, and data formats

Gatling

A unified I/O management toolkit for Python. Orchestrate processes, threads, and coroutines in one pipeline. Read and write files, databases, and queues through a consistent API.

pip install gatling

Requires Python 3.11+

---

Modules

Module	Description
Runtime	Multi-stage pipeline: process + thread + coroutine workers
Storage — Table	Append-only TSV tables, PostgreSQL, SQLite
Storage — Queue	Thread-safe in-memory queue
Storage — Dict	In-memory dictionary with batch ops
Storage — SFS	Virtual file system with path routing
Schema	Type-safe field definitions — table schemas, constants, config
HTTP	Async/sync HTTP client (GET/POST/PUT/DELETE)
File I/O	JSON, JSONL, Pickle, TOML, text, bytes, zstd compression
Watch	Stopwatch, function timing decorator

---

TaskFlow Pipeline

Run CPU-bound, I/O-bound, and network-bound tasks in a single pipeline with automatic queue management.

from gatling.runtime.taskflow_manager import TaskFlowManager
from gatling.storage import MemoryQueue

if __name__ == '__main__':
    q_wait = MemoryQueue()
    for i in range(100):
        q_wait.put(i)

    tfm = TaskFlowManager(q_wait, retry_on_error=False)

    with tfm.execute(log_interval=1):
        tfm.register_process(cpu_task, worker=4)       # multiprocessing
        tfm.register_coroutine(net_task, worker=10)     # asyncio
        tfm.register_thread(disk_task, worker=4)        # threading

    results = list(tfm.get_qdone())

Workers can be functions (one-in-one-out) or generators (one-in-many-out). Stages chain automatically.

---

Schema (TableDefine)

TableDefine is an extended Enum. It can define table schemas, but also work as a typed constants/config manager.

As table schema

from gatling.define.schema import TableDefine, Field

schema = TableDefine('Users', {
    'id':    Field(int, primary=True),
    'name':  Field(str),
    'score': Field(float),
})

As constants / config

class Config(TableDefine):
    AppName   = Field(str, default="my_app")
    Port      = Field(int, default=8080)
    Debug     = Field(bool, default=False)
    StartDate = Field(datetime.date, default=datetime.date(2025, 1, 1))

Config.keys()                # ['AppName', 'Port', 'Debug', 'StartDate']
Config.items()               # {'AppName': 'my_app', 'Port': 8080, ...}
Config['Port'].default       # 8080
Config.Port.dtype            # int
Config.has('Port')           # True
Config.get('Missing', None)  # None

# Serialize / deserialize
Config.Port.tostr(8080)              # "8080"
Config.Port.fmstr("8080")            # 8080
Config.StartDate.tostr(date.today()) # "2025-03-17"
Config.StartDate.fmstr("2025-03-17") # datetime.date(2025, 3, 17)

---

Tables

All table types share the same schema. Only creation differs.

Create

# SQLite
from gatling.storage.g_table.sql.real_sqlite_table import SQLiteTable
ft = SQLiteTable("users", "app.db")
ft.create(schema)

# PostgreSQL
from gatling.storage.g_table.sql.a_pgsql_base import create_pool
from gatling.storage.g_table.sql.real_pgsql_table import PGSQLTable
pool = create_pool("postgresql://user:pass@localhost:5432/db")
ft = PGSQLTable("users", pool)
ft.create(schema)

# Append-only TSV file
from gatling.storage.g_table.append_only.real_tsv_table import TSVTable
ft = TSVTable("users.tsv")
ft.create(schema)

Usage (same for all SQL tables)

# Insert — single or batch
ft.insert({"id": 1, "name": "Alice", "score": 9.5})
ft.insert(
    {"id": 2, "name": "Bob",   "score": 8.0},
    {"id": 3, "name": "Carol", "score": 7.5},
)

# Query
ft.fetch(where={"name": "Alice"})
ft.fetch(order_by={"score": True}, limit=10)
ft.count(where={"score": 9.5})

# Update / Delete
ft.update({"score": 10.0}, where={"id": 1})
ft.delete(where={"id": 3})

# Transaction (rollback on exception)
with ft:
    ft.insert({"id": 4, "name": "Dan", "score": 6.0})
    ft.update({"score": 0.0}, where={"id": 2})

TSV tables use append / extend and support indexing:

ft.append({"ts": "2025-01-01", "level": "INFO", "msg": "started"})
ft.extend([...])

with ft:
    print(len(ft))      # row count
    print(ft[0])         # first row
    print(ft[-1])        # last row
    print(ft[2:5])       # slice

---

HTTP Client

from gatling.utility.http_fetch_fctns import sync_fetch_http, async_fetch_http, fwrap

# Sync
data, status, size = sync_fetch_http("https://httpbin.org/get", rtype="json")

# Async
data, status, size = await fwrap(async_fetch_http, target_url="https://httpbin.org/get", rtype="json")

---

File I/O

from gatling.utility.io_fctns import (
    save_json, read_json,
    save_jsonl, read_jsonl,
    save_text, read_text,
    save_pickle, read_pickle,
    save_bytes, read_bytes,
    read_toml, remove_file,
)

save_json({"key": "value"}, "data.json")
save_jsonl([{"a": 1}, {"a": 2}], "data.jsonl")
save_text("hello", "msg.txt")

---

Watch

from gatling.utility.watch import Watch, watch_time

@watch_time
def slow():
    time.sleep(1)

w = Watch()
# ... work ...
print(w.see_seconds())    # interval since last check
print(w.total_seconds())  # total elapsed

---

License

MIT