242 lines
7.6 KiB
Markdown
242 lines
7.6 KiB
Markdown
# `slingshot-microservice`: A Rust framework for standard microservice design
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`slingshot-microservice` is a Rust package that provides a simple, opinionated
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framework for building microservices. The framework makes the following
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assumptions about a microservice:
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1. A microservice listens to incoming requests on its own dedicated and
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singular queue (RabbitMQ).
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2. Incoming requests are in the form of a 64-bit unsigned integer (`u64`).
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2. Microservices process requests via a `process` function, which takes three
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arguments: the incoming request (`u64`), a `read_file` function, and a
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`write_file` function.
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3. The `process` function returns a set of IDs (also `u64`) that are the result
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of processing the incoming request. Each of these IDs is also associated
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with a "case variable" that is used for routing the result to the
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appropriate outbound queues. Case variables for routing must be one of:
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boolean, integer, or string.
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4. Rather than hard-coding the inbound and outbound queues, the
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microservice communicates with a self-contained configuration service shared
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across all microservices.
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- This service provides inbound queue name, as well as any outbound queues
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and their corresponding case variables.
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- It is also responsible for providing the RabbitMQ connection details
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(host, port, username, password), and the object-storage host plus GNU
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`pass` references for the S3 access key and secret key.
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The `slingshot-microservice` framework handles setting up the RabbitMQ
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connection, listening to the inbound queue and routing results based on case variables.
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## Adding The Framework To Your Project
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Add `slingshot-microservice` to your `Cargo.toml` dependencies directly from Codeberg:
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```toml
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[dependencies]
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slingshot-microservice = { git = "https://codeberg.org/seanhly/slingshot-microservice" }
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```
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Then fetch and build dependencies:
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```bash
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cargo build
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```
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## Python Usage
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`slingshot-microservice` ships Python bindings built with
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[PyO3](https://pyo3.rs) and [maturin](https://www.maturin.rs). Pre-built
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ABI3 wheels work on Python ≥ 3.8 without requiring Rust locally.
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### Installing
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**From PyPI** (once published):
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```bash
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pip install slingshot-microservice
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```
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**From git** (Rust toolchain required):
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```bash
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pip install git+https://codeberg.org/seanhly/slingshot-microservice
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```
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**From a local clone** (for development):
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```bash
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pip install maturin
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pip install -e .
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```
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### Usage
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```python
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from typing import Generator
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from slingshot_microservice.typing import ReadFileFn, WriteFileFn
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from slingshot_microservice import Microservice
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def process(
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request: int,
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read_file: ReadFileFn,
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write_file: WriteFileFn,
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) -> Generator[tuple[int, bool | int | str], None, None]:
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reader = read_file("in", request)
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input_data = reader.read().decode()
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writer = write_file("out", request)
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writer.write(f"Hello {input_data}".encode())
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yield (request, True)
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microservice = Microservice("simple-py-microservice", "sys-map.slingshot.cv", process)
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microservice.start()
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```
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### Type Annotations
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`slingshot_microservice.typing` exports `Protocol`-based types for use in
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editors and type-checkers:
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| Symbol | Description |
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|---|---|
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| `ReadFileFn` | Callable returned by `read_file(key, id)` – behaves like `BinaryIO` |
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| `WriteFileFn` | Callable returned by `write_file(key, id)` – behaves like `BinaryIO` |
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| `ProcessFn` | The generator function signature expected by `Microservice` |
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| `CaseVariable` | `bool \| int \| str` – valid case variable types |
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### Publishing Wheels
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Build and upload to PyPI using maturin:
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```bash
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pip install maturin
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maturin publish
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```
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For CI/cross-compilation (Linux, macOS, Windows), use
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[maturin-action](https://github.com/PyO3/maturin-action) in GitHub/Codeberg
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Actions. Because the extension is compiled with ABI3 (`abi3-py38`), a single
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Linux wheel covers all CPython versions ≥ 3.8.
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## Example Usage
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```rust
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use slingshot_microservice::Microservice;
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use slingshot_microservice::{AnyError, ReadFileFn, WriteFileFn};
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use std::io::{Read, Write};
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fn process(
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request: u64,
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read_file: &ReadFileFn,
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write_file: &WriteFileFn,
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) -> Result<Vec<(u64, String)>, AnyError> {
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let mut input = String::new();
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let mut reader = read_file("in", request)?;
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reader.read_to_string(&mut input)?;
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let mut writer = write_file("out", request)?;
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writer.write_all(input.as_bytes())?;
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Ok(vec![(request, "case_a".to_string())])
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}
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fn main() {
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// Create a new microservice instance with the processing function
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let microservice = Microservice::new(
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"simple-microservice",
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"sys-map.example.com",
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process
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);
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// Start the microservice (this will block and listen for incoming requests)
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microservice.start();
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}
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```
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## How it works:
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The configuration service responds to requests of the form:
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`https://{HOSTNAME}/{MICROSERVICE_NAME}`. All configuration is done over HTTP
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GET. The response contains a JSON object with two fields: an inbound queue name
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and a mapping of case variables to outbound queue names. For example:
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```json
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{
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"in": "simple-microservice-inbound",
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"out": [
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{
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"case": "case_a",
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"queues": ["case_a_outbound_1", "case_a_outbound_2"]
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},
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{
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"case": "case_b",
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"queues": ["case_b_outbound"]
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}
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]
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}
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```
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The case variables used for routing can be one of: string, integer, or boolean.
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E.g. a binary classification microservice might decide on which outbound queue
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to send results to based on a case variable that is either `false` or `true`:
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```json
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{
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"in": "binary-classification-inbound",
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"out": [
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{
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"case": false,
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"queues": ["binary-classification-false-outbound"]
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},
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{
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"case": true,
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"queues": ["binary-classification-true-outbound"]
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}
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]
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}
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```
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The configuration service also provides the RabbitMQ connection details (host,
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port, etc.):
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Object storage credentials are fetched separately from
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`https://sys-map.slingshot.cv/object-storage`. The access-key and secret-key
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values returned there are GNU `pass` entry names, so the runtime resolves the
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actual secrets with `pass show <key>` before constructing the S3 client.
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When the microservice first starts up, it makes a request to the configuration
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service to get the queue metadata. Then it starts to listen to the inbound
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queue. Inbound requests are processed by the user-programmed `process`
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function, which returns a set of tuples of the form `(result_id, case_variable)`.
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Within each `process` pass:
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1. `read_file(key, id)` treats `key` as a bucket reference such as `in`, not
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as the canonical bucket name. On first use, the runtime fetches
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`https://{HOSTNAME}/{MICROSERVICE_NAME}/{key}` to resolve the real bucket
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name, caches that mapping, and then returns a synchronous reader for object
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`id` in that bucket using the AWS SDK.
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2. `write_file(key, id)` resolves `key` through the same cached lookup and
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returns an opened local file handle for writing, staging the output for
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`s3://{resolved_bucket}/{id}`.
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3. After `process` returns, opened files are closed.
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4. Then staged write files are uploaded to S3 with the AWS SDK, local staged
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files are deleted, and local temporary directories are removed.
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5. Only after file finalization is complete are output IDs published to
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outbound queues.
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The output queue routing step looks like this:
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Peudocode:
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```
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for each (result_id, case_variable) in process(request):
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for each outbound_queue in config.out[case_variable]:
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send result_id to outbound_queue
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```
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