<h1><a href="https://github.com/McCache/McCache-for-Python/blob/main/README.md"><img src="https://github.com/McCache/McCache-for-Python/blob/main/docs/McCache%20Logo.png?raw=true" width="200" height="200" alt="McCache for Python"></a>
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## Overview
`McCache` is a, write through cluster aware, local in-memory caching library that is build on Python's [`OrderedDict`](https://docs.python.org/3/library/collections.html#collections.OrderedDict) package. A local cache lookup is faster than retrieving it across a network.
It uses **UDP** multicast as the transport hence the name "Multi-Cast Cache", playfully abbreviated to "`McCache`".
The goals of this package are:
1. Reduce complexity by **not** be dependent on any external caching service such as `memcached`, `redis` or the likes. SEE: [Distributed Cache](https://en.wikipedia.org/wiki/Distributed_cache)
* We are guided by the principal of first scaling up before scaling out.
2. Keep the programming interface consistent with Python's dictionary. The distributed nature of the cache is transparent to you.
* This is an in process cache.
3. Performant
* Need to handle rapid updates that are 0.01sec (10 ms) or faster.
`McCache` is **not** a replacement for your persistent or search data. It is intended to be used to cache your most expensive work. You can consider the Pareto Principle [**80/20**](https://en.wikipedia.org/wiki/Pareto_principle) rule, which states that caching **20%** of the most frequently accessed **80%** data can improve performance for most requests. This principle offers you the option to reduce your hardware requirement. Only you can decide how much to cache.
## Installation
```console
pip install mccache
```
## Example
```python
import mccache
import datetime
from datetime import datetime as dt
from pprint import pprint as pp
c = mccache.get_cache( 'demo' )
k = 'k1'
c[ k ] = dt.now( datetime.UTC ) # Insert a cache entry
print(f"Started at {c[ k ]}")
c[ k ] = dt.now( datetime.UTC ) # Update a cache entry
print(f"Ended at {c[ k ]}")
print(f"Metadata for key is {c.metadata[ k ]}")
del c[ k ] # Delete a cache entry
if k not in c:
print(f" {k} is not in the cache.")
print("At this point all the cache with namespace 'demo' in the cluster are identical.")
# Query the local cache checksum and metrics.
pp( mccache.get_local_checksum( 'demo' ))
pp( mccache.get_local_metrics( 'demo' ))
# Request the other members in the cluster to log out their local cache metrics.
mccache.get_cluster_metrics()
```
In the above example, there is **nothing** different in the usage of `McCache` from a regular Python dictionary. However, the benefit is in a clustered environment where the other member's cache are kept coherent with the changes to your local cache.
## Guidelines
The following are some loose guidelines to help you assess if the `McCache` library is right for your project.
* You have a need to **not** depend on external caching service.
* You want to keep the programming **consistency** of a Python dictionary.
* You have a **small** cluster of identically configured nodes.
* You have a **medium** size set of objects to cache.
* Your cached objects do not mutate **frequently**.
* Your cached objects size is **small**.
* Your cluster environment is secured by **other** means.
* Your nodes clock in the cluster are **well** synchronized.
The adjectives used above have been intended to be loose and should be quantified to your environment and needs.<br>
**SEE**: [Testing](https://github.com/McCache/McCache-for-Python/blob/main/docs/TESTING.md)
You can review the script used in the stress test.<br>
**SEE**: [Test script](https://github.com/McCache/McCache-for-Python/blob/main/tests/unit/start_mccache.py)
You should clone this repo down and run the test in a local `docker`/`podman` cluster.<br>
**SEE**: [Contributing](https://github.com/McCache/McCache-for-Python/blob/main/docs/CONTRIBUTING.md#Tests)
We suggest the following testing to collect metrics of your application running in your environment.
1. Import the `McCache` library into your project.
2. Use it in your data access layer by populating and updating the cache but **don't** use the cached values.
3. Configure to enable the debug logging by providing a path for your log file.
4. Run your application for an extended period and exit.
5. Log the summary metric out to be analyze.
5. Review the metrics to quantify the fit to your application and environment. **SEE**: [Testing](https://github.com/McCache/McCache-for-Python/blob/main/docs/TESTING.md#container)
## Saving
Removing an external dependency in your architecture reduces it's <strong>complexity</strong> and not to mention some capital cost saving.<br>
**SEE**: [Cloud Savings](https://github.com/McCache/McCache-for-Python/blob/main/docs/SAVING.md)
## Configuration
The following are environment variables you can tune to fit your production environment needs.
<table>
<thead>
<tr>
<th align="left">Name</th>
<th align="left">Default</th>
<th align="left">Comment</th>
</tr>
</thead>
<tbody>
<tr>
<td><sub>MCCACHE_CACHE_TTL</sub></td>
<td>3600 secs</td>
<td>Maximum number of seconds a cached entry can live before eviction. Update operations shall reset the timer.</td>
</tr>
<tr>
<td><sub>MCCACHE_CACHE_MAX</sub></td>
<td>256 entries</td>
<td>The maximum entries per cache.</td>
</tr>
<tr>
<td><sub>MCCACHE_CACHE_MODE</sub></td>
<td>1</td>
<td>The degree of keeping the cache coherent in the cluster.<br>
<b>0</b>: Only members that has the same key in their cache shall be updated.<br>
<b>1</b>: All members cache shall be kept fully coherent and synchronized.<br></td>
</tr>
<tr>
<td><sub>MCCACHE_CACHE_SIZE</sub></td>
<td>8,388,608 bytes</td>
<td>The maximum size for the cache.</td>
</tr>
<tr>
<td><sub>MCCACHE_CACHE_PULSE</sub></td>
<td>300 secs</td>
<td>The interval to send out a synchronization pulse operation to the other members in the cluster.</td>
</tr>
<tr>
<td><sub>MCCACHE_PACKET_MTU</sub></td>
<td>1472 bytes</td>
<td>The size of the smallest transfer unit of the network packet between all the network interfaces.</td>
</tr>
<tr>
<td><sub>MCCACHE_MULTICAST_IP</sub></td>
<td>224.0.0.3 [ :4000 ]</td>
<td>The multicast IP address and the optional port number for your group to multicast within.
<br><b>SEE</b>: <a href="https://www.iana.org/assignments/multicast-addresses/multicast-addresses.xhtml">IANA multicast addresses</a>.</td>
</tr>
<tr>
<td><sub>MCCACHE_MULTICAST_HOPS</sub></td>
<td>3 hops</td>
<td>The maximum network hops. 1 is just within the same switch/router. [>=1]</td>
</tr>
<tr>
<td><sub>MCCACHE_CALLBACK_WIN</sub></td>
<td>5 secs</td>
<td>The window, in seconds, where the last lookup and the current change falls in to trigger a callback to a function provided by you. </td>
</tr>
<tr>
<td><sub>MCCACHE_DAEMON_SLEEP</sub></td>
<td>1 sec</td>
<td>The snooze duration for the daemon housekeeper before waking up to check the state of the cache.</td>
</tr>
<tr>
<td><sub>MCCACHE_DEBUG_LOGFILE</sub></td>
<td>./log/debug.log</td>
<td>The local filename where output log messages are appended to.</td>
</tr>
<tr>
<td><sub>MCCACHE_LOG_FORMAT</sub></td>
<td></td>
<td>The custom logging format for your project.
<br><b>SEE</b>: Variables <code>log_format</code> and <code>log_msgfmt</code> in <code>__init__.py</code></td>
</tr>
<tr>
<td colspan=3><b>The following are parameters you can tune to fit your stress testing needs.</b></td>
<tr>
<tr>
<td><sub>TEST_RANDOM_SEED</sub></td>
<td>4th octet of the IP address</td>
<td>The random seed for each different node in the test cluster.</td>
</tr>
<tr>
<td><sub>TEST_KEY_ENTRIES</sub></td>
<td>200 key/values</td>
<td>The maximum of randomly generated keys.<br>
The smaller the number, the higher the chance of cache collision.
Tune this number down to add stress to the test.</td>
</tr>
<tr>
<td><sub>TEST_DATA_SIZE_MIX</sub></td>
<td>1</td>
<td>The data packet size mix.<br>
<b>1</b>: Cache small objects where size < 1Kb.<br>
<b>2</b>: Cache large objects where size > 9Kb.<br>
<b>3</b>: Random mix of small and large objects.<br>
Tune this number to 2 to add stress to the test.</td>
</tr>
<tr>
<td><sub>TEST_RUN_DURATION</sub></td>
<td>5 mins</td>
<td>The duration in minutes of the testing run. <br>
The larger the number, the longer the test run/duration.
Tune this number up to add stress to the test.</td>
</tr>
<tr>
<td><sub>TEST_APERTURE</sub></td>
<td>0.01 sec</td>
<td>The time scale to keep the randomly generated value to snooze within.
The value of 0.01, 10ms, a random snooze shall be between 6.5ms and 45ms.
Tune this number down to add stress to the test.</td>
</tr>
<tr>
<td><sub>TEST_MONKEY_TANTRUM</sub></td>
<td>0</td>
<td>The percentage of drop packets. <br>
The larger the number, the more unsent packets.
Tune this number up to add stress to the test.</td>
</tr>
</tbody>
</table>
### pyproject.toml
Specifying tuning parameters via `pyproject.toml` file.
```toml
[tool.mccache]
cache_ttl = 900
packet_mtu = 1472
```
### Environment variables
Specifying tuning parameters via environment variables.
```bash
# Unix
export MCCACHE_TTL=900
export MCCACHE_MTU=1472
```
```bat
:: Windows
SET MCCACHE_TTL=900
SET MCCACHE_MTU=1472
```
Environment variables supersede the setting in the `pyproject.toml` file.
## Design
* SEE: [Design gist](https://github.com/McCache/McCache-for-Python/blob/main/docs/DESIGN.md).
## Background Story
* SEE: [Background story](https://github.com/McCache/McCache-for-Python/blob/main/docs/BACKGROUND.md).
## Releases
Releases are recorded [here](https://github.com/McCache/McCache-for-Python/issues).
## License
`McCache` is distributed under the terms of the [MIT](https://spdx.org/licenses/MIT.html) license.
## Contribute
We welcome your contribution. Please read [contributing](https://github.com/McCache/McCache-for-Python/blob/main/docs/CONTRIBUTING.md) to learn how to get setup to contribute to this project.
`McCache` is still a young project. With that said, please try it out in your applications: We need your feedback to fix the bugs and file down the rough edges.
Issues and feature request can be posted [here](https://github.com/McCache/McCache-for-Python/issues). Help us port this library to other languages. The repos are setup under the [GitHub `McCache` organization](https://github.com/mccache).
You can reach our administrator at `elau1004@netscape.net`.
## Support
For any inquiries, bug reports, or feature requests, please open an issue in the [GitHub repository](https://github.com/McCache/McCache-for-Python/issues).
## Miscellaneous
* SEE: [Latency Numbers](https://gist.github.com/hellerbarde/2843375)
* SEE: [Determine the size of the MTU in your network.](https://www.youtube.com/watch?v=Od5SEHEZnVU)
* SEE: [Network maximum transmission unit (MTU) for your EC2 instance](https://docs.aws.amazon.com/AWSEC2/latest/UserGuide/network_mtu.html)
* SEE: [Setting MTU size for jumbo frames on OCI instance interfaces](https://support.checkpoint.com/results/sk/sk167534)
Different cloud provider uses different size.
* SEE: [Enabling Sticky Sessions](https://www.youtube.com/watch?v=hTp4czOrvOY")
* SEE: [In-Process vs Distributed](https://dzone.com/articles/process-caching-vs-distributed)
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"description": "<h1><a href=\"https://github.com/McCache/McCache-for-Python/blob/main/README.md\"><img src=\"https://github.com/McCache/McCache-for-Python/blob/main/docs/McCache%20Logo.png?raw=true\" width=\"200\" height=\"200\" alt=\"McCache for Python\"></a>\n<!--\n<br><sub>This package is still under development.</sub>\n-->\n</h1>\n<!-- Not working in GitHub\n<style scoped>\ntable {\n font-size: 12px;\n}\n</style>\n-->\n\n## Overview\n`McCache` is a, write through cluster aware, local in-memory caching library that is build on Python's [`OrderedDict`](https://docs.python.org/3/library/collections.html#collections.OrderedDict) package. A local cache lookup is faster than retrieving it across a network.\nIt uses **UDP** multicast as the transport hence the name \"Multi-Cast Cache\", playfully abbreviated to \"`McCache`\".\n\nThe goals of this package are:\n1. Reduce complexity by **not** be dependent on any external caching service such as `memcached`, `redis` or the likes. SEE: [Distributed Cache](https://en.wikipedia.org/wiki/Distributed_cache)\n * We are guided by the principal of first scaling up before scaling out.\n2. Keep the programming interface consistent with Python's dictionary. The distributed nature of the cache is transparent to you.\n * This is an in process cache.\n3. Performant\n * Need to handle rapid updates that are 0.01sec (10 ms) or faster.\n\n`McCache` is **not** a replacement for your persistent or search data. It is intended to be used to cache your most expensive work. You can consider the Pareto Principle [**80/20**](https://en.wikipedia.org/wiki/Pareto_principle) rule, which states that caching **20%** of the most frequently accessed **80%** data can improve performance for most requests. This principle offers you the option to reduce your hardware requirement. Only you can decide how much to cache.\n\n## Installation\n```console\npip install mccache\n```\n\n## Example\n```python\nimport mccache\nimport datetime\nfrom datetime import datetime as dt\nfrom pprint import pprint as pp\n\nc = mccache.get_cache( 'demo' )\nk = 'k1'\n\nc[ k ] = dt.now( datetime.UTC ) # Insert a cache entry\nprint(f\"Started at {c[ k ]}\")\n\nc[ k ] = dt.now( datetime.UTC ) # Update a cache entry\nprint(f\"Ended at {c[ k ]}\")\nprint(f\"Metadata for key is {c.metadata[ k ]}\")\n\ndel c[ k ] # Delete a cache entry\nif k not in c:\n print(f\" {k} is not in the cache.\")\n\nprint(\"At this point all the cache with namespace 'demo' in the cluster are identical.\")\n\n# Query the local cache checksum and metrics.\npp( mccache.get_local_checksum( 'demo' ))\npp( mccache.get_local_metrics( 'demo' ))\n\n# Request the other members in the cluster to log out their local cache metrics.\nmccache.get_cluster_metrics()\n```\n\nIn the above example, there is **nothing** different in the usage of `McCache` from a regular Python dictionary. However, the benefit is in a clustered environment where the other member's cache are kept coherent with the changes to your local cache.\n\n## Guidelines\nThe following are some loose guidelines to help you assess if the `McCache` library is right for your project.\n\n* You have a need to **not** depend on external caching service.\n* You want to keep the programming **consistency** of a Python dictionary.\n* You have a **small** cluster of identically configured nodes.\n* You have a **medium** size set of objects to cache.\n* Your cached objects do not mutate **frequently**.\n* Your cached objects size is **small**.\n* Your cluster environment is secured by **other** means.\n* Your nodes clock in the cluster are **well** synchronized.\n\nThe adjectives used above have been intended to be loose and should be quantified to your environment and needs.<br>\n**SEE**: [Testing](https://github.com/McCache/McCache-for-Python/blob/main/docs/TESTING.md)\n\nYou can review the script used in the stress test.<br>\n**SEE**: [Test script](https://github.com/McCache/McCache-for-Python/blob/main/tests/unit/start_mccache.py)\n\nYou should clone this repo down and run the test in a local `docker`/`podman` cluster.<br>\n**SEE**: [Contributing](https://github.com/McCache/McCache-for-Python/blob/main/docs/CONTRIBUTING.md#Tests)\n\nWe suggest the following testing to collect metrics of your application running in your environment.\n1. Import the `McCache` library into your project.\n2. Use it in your data access layer by populating and updating the cache but **don't** use the cached values.\n3. Configure to enable the debug logging by providing a path for your log file.\n4. Run your application for an extended period and exit.\n5. Log the summary metric out to be analyze.\n5. Review the metrics to quantify the fit to your application and environment. **SEE**: [Testing](https://github.com/McCache/McCache-for-Python/blob/main/docs/TESTING.md#container)\n\n## Saving\nRemoving an external dependency in your architecture reduces it's <strong>complexity</strong> and not to mention some capital cost saving.<br>\n**SEE**: [Cloud Savings](https://github.com/McCache/McCache-for-Python/blob/main/docs/SAVING.md)\n\n## Configuration\nThe following are environment variables you can tune to fit your production environment needs.\n<table>\n<thead>\n <tr>\n <th align=\"left\">Name</th>\n <th align=\"left\">Default</th>\n <th align=\"left\">Comment</th>\n </tr>\n</thead>\n<tbody>\n <tr>\n <td><sub>MCCACHE_CACHE_TTL</sub></td>\n <td>3600 secs</td>\n <td>Maximum number of seconds a cached entry can live before eviction. Update operations shall reset the timer.</td>\n </tr>\n <tr>\n <td><sub>MCCACHE_CACHE_MAX</sub></td>\n <td>256 entries</td>\n <td>The maximum entries per cache.</td>\n </tr>\n <tr>\n <td><sub>MCCACHE_CACHE_MODE</sub></td>\n <td>1</td>\n <td>The degree of keeping the cache coherent in the cluster.<br>\n <b>0</b>: Only members that has the same key in their cache shall be updated.<br>\n <b>1</b>: All members cache shall be kept fully coherent and synchronized.<br></td>\n </tr>\n <tr>\n <td><sub>MCCACHE_CACHE_SIZE</sub></td>\n <td>8,388,608 bytes</td>\n <td>The maximum size for the cache.</td>\n </tr>\n <tr>\n <td><sub>MCCACHE_CACHE_PULSE</sub></td>\n <td>300 secs</td>\n <td>The interval to send out a synchronization pulse operation to the other members in the cluster.</td>\n </tr>\n <tr>\n <td><sub>MCCACHE_PACKET_MTU</sub></td>\n <td>1472 bytes</td>\n <td>The size of the smallest transfer unit of the network packet between all the network interfaces.</td>\n </tr>\n <tr>\n <td><sub>MCCACHE_MULTICAST_IP</sub></td>\n <td>224.0.0.3 [ :4000 ]</td>\n <td>The multicast IP address and the optional port number for your group to multicast within.\n <br><b>SEE</b>: <a href=\"https://www.iana.org/assignments/multicast-addresses/multicast-addresses.xhtml\">IANA multicast addresses</a>.</td>\n </tr>\n <tr>\n <td><sub>MCCACHE_MULTICAST_HOPS</sub></td>\n <td>3 hops</td>\n <td>The maximum network hops. 1 is just within the same switch/router. [>=1]</td>\n </tr>\n <tr>\n <td><sub>MCCACHE_CALLBACK_WIN</sub></td>\n <td>5 secs</td>\n <td>The window, in seconds, where the last lookup and the current change falls in to trigger a callback to a function provided by you. </td>\n </tr>\n <tr>\n <td><sub>MCCACHE_DAEMON_SLEEP</sub></td>\n <td>1 sec</td>\n <td>The snooze duration for the daemon housekeeper before waking up to check the state of the cache.</td>\n </tr>\n <tr>\n <td><sub>MCCACHE_DEBUG_LOGFILE</sub></td>\n <td>./log/debug.log</td>\n <td>The local filename where output log messages are appended to.</td>\n </tr>\n <tr>\n <td><sub>MCCACHE_LOG_FORMAT</sub></td>\n <td></td>\n <td>The custom logging format for your project.\n <br><b>SEE</b>: Variables <code>log_format</code> and <code>log_msgfmt</code> in <code>__init__.py</code></td>\n </tr>\n <tr>\n <td colspan=3><b>The following are parameters you can tune to fit your stress testing needs.</b></td>\n <tr>\n <tr>\n <td><sub>TEST_RANDOM_SEED</sub></td>\n <td>4th octet of the IP address</td>\n <td>The random seed for each different node in the test cluster.</td>\n </tr>\n <tr>\n <td><sub>TEST_KEY_ENTRIES</sub></td>\n <td>200 key/values</td>\n <td>The maximum of randomly generated keys.<br>\n The smaller the number, the higher the chance of cache collision.\n Tune this number down to add stress to the test.</td>\n </tr>\n <tr>\n <td><sub>TEST_DATA_SIZE_MIX</sub></td>\n <td>1</td>\n <td>The data packet size mix.<br>\n <b>1</b>: Cache small objects where size < 1Kb.<br>\n <b>2</b>: Cache large objects where size > 9Kb.<br>\n <b>3</b>: Random mix of small and large objects.<br>\n Tune this number to 2 to add stress to the test.</td>\n </tr>\n <tr>\n <td><sub>TEST_RUN_DURATION</sub></td>\n <td>5 mins</td>\n <td>The duration in minutes of the testing run. <br>\n The larger the number, the longer the test run/duration.\n Tune this number up to add stress to the test.</td>\n </tr>\n <tr>\n <td><sub>TEST_APERTURE</sub></td>\n <td>0.01 sec</td>\n <td>The time scale to keep the randomly generated value to snooze within.\n The value of 0.01, 10ms, a random snooze shall be between 6.5ms and 45ms.\n Tune this number down to add stress to the test.</td>\n </tr>\n <tr>\n <td><sub>TEST_MONKEY_TANTRUM</sub></td>\n <td>0</td>\n <td>The percentage of drop packets. <br>\n The larger the number, the more unsent packets.\n Tune this number up to add stress to the test.</td>\n </tr>\n</tbody>\n</table>\n\n### pyproject.toml\nSpecifying tuning parameters via `pyproject.toml` file.\n```toml\n[tool.mccache]\ncache_ttl = 900\npacket_mtu = 1472\n```\n### Environment variables\nSpecifying tuning parameters via environment variables.\n```bash\n# Unix\nexport MCCACHE_TTL=900\nexport MCCACHE_MTU=1472\n```\n```bat\n:: Windows\nSET MCCACHE_TTL=900\nSET MCCACHE_MTU=1472\n```\nEnvironment variables supersede the setting in the `pyproject.toml` file.\n\n## Design\n* SEE: [Design gist](https://github.com/McCache/McCache-for-Python/blob/main/docs/DESIGN.md).\n\n## Background Story\n* SEE: [Background story](https://github.com/McCache/McCache-for-Python/blob/main/docs/BACKGROUND.md).\n\n## Releases\nReleases are recorded [here](https://github.com/McCache/McCache-for-Python/issues).\n\n## License\n`McCache` is distributed under the terms of the [MIT](https://spdx.org/licenses/MIT.html) license.\n\n## Contribute\nWe welcome your contribution. Please read [contributing](https://github.com/McCache/McCache-for-Python/blob/main/docs/CONTRIBUTING.md) to learn how to get setup to contribute to this project.\n\n`McCache` is still a young project. With that said, please try it out in your applications: We need your feedback to fix the bugs and file down the rough edges.\n\nIssues and feature request can be posted [here](https://github.com/McCache/McCache-for-Python/issues). Help us port this library to other languages. The repos are setup under the [GitHub `McCache` organization](https://github.com/mccache).\nYou can reach our administrator at `elau1004@netscape.net`.\n\n## Support\nFor any inquiries, bug reports, or feature requests, please open an issue in the [GitHub repository](https://github.com/McCache/McCache-for-Python/issues).\n\n## Miscellaneous\n* SEE: [Latency Numbers](https://gist.github.com/hellerbarde/2843375)\n* SEE: [Determine the size of the MTU in your network.](https://www.youtube.com/watch?v=Od5SEHEZnVU)\n* SEE: [Network maximum transmission unit (MTU) for your EC2 instance](https://docs.aws.amazon.com/AWSEC2/latest/UserGuide/network_mtu.html)\n* SEE: [Setting MTU size for jumbo frames on OCI instance interfaces](https://support.checkpoint.com/results/sk/sk167534)\nDifferent cloud provider uses different size.\n* SEE: [Enabling Sticky Sessions](https://www.youtube.com/watch?v=hTp4czOrvOY\")\n* SEE: [In-Process vs Distributed](https://dzone.com/articles/process-caching-vs-distributed)\n",
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