"BERECHIAH "blessed by Jehovah. (1.) Son of Shimea, and father of Asaph the musician (1 Chronicles 6:39; 15:17). (2.) One of the seven Ephraimite chieftains, son of Meshillemoth (2 Chronicles 28:12). (3.) The fourth of the five sons of Zerubbabel, of the royal family of Judah(1 Chronicles 3:20). (4.) The father of the prophet Zechariah (1:1,7).
"BERECHIAH "blessed by Jehovah. (1.) Son of Shimea, and father of Asaph the musician (1 Chronicles 6:39; 15:17).
(2.) One of the seven Ephraimite chieftains, son of Meshillemoth (2 Chronicles 28:12).
(3.) The fourth of the five sons of Zerubbabel, of the royal family of Judah(1 Chronicles 3:20).
(4.) The father of the prophet Zechariah (1:1,7).


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public interface Transaction
The Transaction object. Typically representing a JDBC or JTA transaction.
| Field Summary | |
|---|---|
static int |
READ_COMMITTED
Read Committed transaction isolation. |
static int |
READ_UNCOMMITTED
Read Uncommitted transaction isolation. |
static int |
REPEATABLE_READ
Repeatable read transaction isolation. |
static int |
SERIALIZABLE
Serializable transaction isolation. |
| Method Summary | |
|---|---|
void |
addModification(String tableName,
boolean inserts,
boolean updates,
boolean deletes)
Add table modification information to the TransactionEvent. |
void |
batchFlush()
Deprecated. Please use flushBatch |
void |
commit()
Commit the transaction. |
void |
end()
If the transaction is active then perform rollback. |
void |
flushBatch()
The batch will be flushing automatically but you can use this to explicitly flush the batch if you like. |
Connection |
getConnection()
Return the underlying Connection object. |
boolean |
isActive()
Return true if the transaction is active. |
boolean |
isBatchFlushOnQuery()
Return true if the batch (of persisted beans or executed UpdateSql etc) should be flushed prior to executing a query. |
boolean |
isReadOnly()
Return true if this transaction is read only. |
void |
log(String msg)
Log a comment to the transaction log. |
void |
rollback()
Rollback the transaction. |
void |
rollback(Throwable e)
Rollback the transaction specifying a throwable that caused the rollback to occur. |
void |
setBatchFlushOnMixed(boolean batchFlushOnMixed)
By default when mixing UpdateSql (or CallableSql) with Beans the batch is automatically flushed when you change (between persisting beans and executing UpdateSql or CallableSql). |
void |
setBatchFlushOnQuery(boolean batchFlushOnQuery)
By default executing a query will automatically flush any batched statements (persisted beans, executed UpdateSql etc). |
void |
setBatchGetGeneratedKeys(boolean getGeneratedKeys)
Specify if you want batched inserts to use getGeneratedKeys. |
void |
setBatchMode(boolean useBatch)
Turn on or off statement batching. |
void |
setBatchSize(int batchSize)
Specify the number of statements before a batch is flushed automatically. |
void |
setLoggingOn(boolean isLoggingOn)
Set this to false to disable logging for this transaction. |
void |
setPersistCascade(boolean persistCascade)
Explicitly turn off or on the cascading nature of save() and delete(). |
void |
setReadOnly(boolean readOnly)
Set whether this transaction should be readOnly. |
| Field Detail |
|---|
static final int READ_COMMITTED
static final int READ_UNCOMMITTED
static final int REPEATABLE_READ
static final int SERIALIZABLE
| Method Detail |
|---|
boolean isReadOnly()
void setReadOnly(boolean readOnly)
void log(String msg)
void setLoggingOn(boolean isLoggingOn)
void commit() throws javax.persistence.RollbackException
javax.persistence.RollbackException
void rollback() throws javax.persistence.PersistenceException
javax.persistence.PersistenceException
void rollback(Throwable e) throws javax.persistence.PersistenceException
If you are using transaction logging this will log the throwable in the transaction logs.
javax.persistence.PersistenceException
void end() throws javax.persistence.PersistenceException
javax.persistence.PersistenceException
boolean isActive()
void setPersistCascade(boolean persistCascade)
This is useful if you can getting back entity beans from a layer of code (potentially remote) and you prefer to have exact control.
This may also be useful if you are using jdbc batching with jdbc drivers that do not support getGeneratedKeys.
void setBatchMode(boolean useBatch)
Refer to java.sql.PreparedStatement.addBatch();
Note that you may also wish to use the setPersistCascade method to stop save and delete cascade behaviour. You may do this to have full control over the order of execution rather than the normal cascading fashion.
Note that the execution order in batch mode may be different from non batch mode execution order. Also note that insert behaviour may be different depending on the JDBC driver and its support for getGeneratedKeys. That is, for JDBC drivers that do not support getGeneratedKeys you may not get back the generated IDs (used for inserting associated detail beans etc).
Calls to save(), delete(), insert() and execute() all support batch processing. This includes normal beans, MapBean, CallableSql and UpdateSql.
The flushing of the batched statements is automatic but you can call batchFlush when you like. Note that flushing occurs when a query is executed or when you mix UpdateSql and CallableSql with save and delete of beans.
Example: batch processing executing every 3 rows
String data = "This is a simple test of the batch processing" + " mode and the transaction execute batch method"; String[] da = data.split(" "); String sql = "{call sp_t3(?,?)}"; CallableSql cs = new CallableSql(sql); cs.registerOut(2, Types.INTEGER); // (optional) inform eBean this stored procedure // inserts into a table called sp_test cs.addModification("sp_test", true, false, false); Transaction t = Ebean.beginTransaction(); t.setBatchMode(true); t.setBatchSize(3); try { for (int i = 0; i < da.length;) { cs.setParameter(1, da[i]); Ebean.execute(cs); } // NB: commit implicitly flushes Ebean.commitTransaction(); } finally { Ebean.endTransaction(); }
void setBatchSize(int batchSize)
void setBatchGetGeneratedKeys(boolean getGeneratedKeys)
By default batched inserts will try to use getGeneratedKeys if it is supported by the underlying jdbc driver and database.
You want wish to turn this off for inserting very large numbers of objects when you don't care about getting back the ids.
void setBatchFlushOnMixed(boolean batchFlushOnMixed)
If you want to execute both WITHOUT having the batch automatically flush you need to call this with batchFlushOnMixed = false.
Note that UpdateSql and CallableSql are ALWAYS executed first (before the beans are executed). This is because the UpdateSql and CallableSql have already been bound to their PreparedStatements. The beans on the other hand have a 2 step process (delayed binding).
void setBatchFlushOnQuery(boolean batchFlushOnQuery)
Calling this method with batchFlushOnQuery = false means that you can execute a query and the batch will not be automatically flushed.
boolean isBatchFlushOnQuery()
The default is for this to be true.
void flushBatch() throws javax.persistence.PersistenceException, javax.persistence.OptimisticLockException
Flushing occurs automatically when:
javax.persistence.PersistenceException
javax.persistence.OptimisticLockException
void batchFlush() throws javax.persistence.PersistenceException, javax.persistence.OptimisticLockException
flushBatch()
.
Exactly the same as flushBatch. Deprecated as a name change.
javax.persistence.PersistenceException
javax.persistence.OptimisticLockException
Connection getConnection()
Useful where a Developer wishes to use the JDBC API directly. Note that the commit() rollback() and end() methods on the Transaction should still be used. Calling these methods on the Connection would be a big no no unless you know what you are doing.
Examples of when a developer may wish to use the connection directly are: Savepoints, advanced CLOB BLOB use and advanced stored procedure calls.
void addModification(String tableName, boolean inserts, boolean updates, boolean deletes)
Use this in conjunction with getConnection() and raw JDBC.
This effectively informs Ebean of the data that has been changed by the transaction and this information is normally automatically handled by Ebean when you save entity beans or use UpdateSql etc.
If you use raw JDBC then you can use this method to inform Ebean for the tables that have been modified. Ebean uses this information to keep its caches in synch and maintain lucene text indexes.
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The application encountered an unexpected error.
To return to the application click here.
You can contribute to the development of Piggydb by creating a new ticket with the following error details (Piggydb Issue Tracking System).
$utils.raw($errorReport) #end


Tiroler Gröstl
- ⏲ Prep time: 15 min
- 🍳 Cook time: 30 min
- 🍽 Servings: 5
Ingredients
- 1 kg / 2 lbs potatoes
- 2 red onions
- 500 g / 1 lbs porkchops
- 400 g / 14 ounce bacon (smoked)
- 5 eggs
- 3 cloves of garlic
- Ample amount of chopped parsley
- Marjoram and cumin to taste
Directions
- Clean potatoes and boil with skin.
- Chop onion, squeeze and mince the garlic.
- Dice pork and bacon.
- Slice cooked potatoes in thick wedges.
- Fry potato wedges in butter, add more butter while stirring.
- Add pork and bacon and bake until crispy.
- Add and stir the rest of the ingredients, except eggs.
- Fry the eggs separately and serve on top.
Contribution
Recipe tags:
austrian
bacon


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|
Lib-Util
A Utility C Library
|
Remove a data entry from a known position. More...
Macros | |
| #define | DS_pop(A) DS_remove_first(A) |
| Pop data off a stack. Used on a DS_list to implement a stack. | |
| #define | DS_dq(A) DS_remove_first(A) |
| Dequeue data. Used on a DS_list to implement a queue. | |
Functions | |
| const void * | DS_remove (DS root) |
| Removes the entry at the current position | |
| const void * | DS_remove_first (DS root) |
| Remove the first entry in the structure. | |
| const void * | DS_remove_last (DS root) |
| Remove the last entry in the structure. | |
Remove a data entry from a known position.
The returned pointer points to the data stored in a temporary space. The caller must copy that data out as there is no guarantee that it will be preserved after the next call to the data structure.
If your data structure is a queue use only DS_nq() and DS_dq(). If your data structure is a stack use only DS_push() and DS_pop().
| root | is the root of a data structure |
NULL
on failure.


--- layout: post title: "À bientôt, Paris" categories: - KDE - Software Libre tags: [] status: publish type: post published: true meta: _edit_last: '2' dsq_thread_id: '2226554054' author: login: afiestas email<EMAIL_ADDRESS>display_name: afiestas first_name: Àlex last_name: Fiestas ---
At home!
After my "small" holidays in Paris, I'm back home. Despite my intentions of finishing some ktuberling/kamoso/kbluetooth work on this holidays
as you may have noticed I didn't, sorry for that :( I only had time to keep myself up to date with kde planet/mailist/commits, leaving only the last 3 days of kde activities as homework to when I'll come back.
So now, I've read everything I had to, as well as answering dozens of emails and I'm ready to hack on KDE again and to finish my kde homework's.
The first thing I'm going to do is to prepare the release of kbluetooth 0.4, as well as to write the property announcement of the project, which will be splited in 3/4 post.
Then some more work on Kamoso will follow (add webcam basic controls), and finally I have to finish the ktuberling patch.
That's all at the moment!.
[caption id="" align="alignnone" width="580" caption="Picture from Effiel top // Creative Commons Attribution Non-Commercial Share Alike"][/caption]
See you soon Paris :)


Alternatives, Inspiration and Comparisons
What inspired FastAPI, how it compares to other alternatives and what it learned from them.
Intro¶
FastAPI wouldn't exist if not for the previous work of others.
There have been many tools created before that have helped inspire its creation.
I have been avoiding the creation of a new framework for several years. First I tried to solve all the features covered by FastAPI using many different frameworks, plug-ins, and tools.
But at some point, there was no other option than creating something that provided all these features, taking the best ideas from previous tools, and combining them in the best way possible, using language features that weren't even available before (Python 3.6+ type hints).
Previous tools¶
Django¶
It's the most popular Python framework and is widely trusted. It is used to build systems like Instagram.
It's relatively tightly coupled with relational databases (like MySQL or PostgreSQL), so, having a NoSQL database (like Couchbase, MongoDB, Cassandra, etc) as the main store engine is not very easy.
It was created to generate the HTML in the backend, not to create APIs used by a modern frontend (like React, Vue.js and Angular) or by other systems (like IoT devices) communicating with it.
Django REST Framework¶
Django REST framework was created to be a flexible toolkit for building Web APIs using Django underneath, to improve its API capabilities.
It is used by many companies including Mozilla, Red Hat and Eventbrite.
It was one of the first examples of automatic API documentation, and this was specifically one of the first ideas that inspired "the search for" FastAPI.
Note
Django REST Framework was created by Tom Christie. The same creator of Starlette and Uvicorn, on which FastAPI is based.
Inspired FastAPI to
Have an automatic API documentation web user interface.
Flask¶
Flask is a "microframework", it doesn't include database integrations nor many of the things that come by default in Django.
This simplicity and flexibility allow doing things like using NoSQL databases as the main data storage system.
As it is very simple, it's relatively intuitive to learn, although the documentation gets somewhat technical at some points.
It is also commonly used for other applications that don't necessarily need a database, user management, or any of the many features that come pre-built in Django. Although many of these features can be added with plug-ins.
This decoupling of parts, and being a "microframework" that could be extended to cover exactly what is needed was a key feature that I wanted to keep.
Given the simplicity of Flask, it seemed like a good match for building APIs. The next thing to find was a "Django REST Framework" for Flask.
Inspired FastAPI to
Be a micro-framework. Making it easy to mix and match the tools and parts needed.
Have a simple and easy to use routing system.
Requests¶
FastAPI is not actually an alternative to Requests. Their scope is very different.
It would actually be common to use Requests inside of a FastAPI application.
But still, FastAPI got quite some inspiration from Requests.
Requests is a library to interact with APIs (as a client), while FastAPI is a library to build APIs (as a server).
They are, more or less, at opposite ends, complementing each other.
Requests has a very simple and intuitive design, it's very easy to use, with sensible defaults. But at the same time, it's very powerful and customizable.
That's why, as said in the official website:
Requests is one of the most downloaded Python packages of all time
The way you use it is very simple. For example, to do a GET
request, you would write:
response = requests.get("http://example.com/some/url")
The FastAPI counterpart API path operation could look like:
@app.get("/some/url")
def read_url():
return {"message": "Hello World"}
See the similarities in requests.get(...)
and @app.get(...)
.
Inspired FastAPI to
- Have a simple and intuitive API.
- Use HTTP method names (operations) directly, in a straightforward and intuitive way.
- Have sensible defaults, but powerful customizations.
Swagger / OpenAPI¶
The main feature I wanted from Django REST Framework was the automatic API documentation.
Then I found that there was a standard to document APIs, using JSON (or YAML, an extension of JSON) called Swagger.
And there was a web user interface for Swagger APIs already created. So, being able to generate Swagger documentation for an API would allow using this web user interface automatically.
At some point, Swagger was given to the Linux Foundation, to be renamed OpenAPI.
That's why when talking about version 2.0 it's common to say "Swagger", and for version 3+ "OpenAPI".
Inspired FastAPI to
Adopt and use an open standard for API specifications, instead of a custom schema.
And integrate standards-based user interface tools:
These two were chosen for being fairly popular and stable, but doing a quick search, you could find dozens of additional alternative user interfaces for OpenAPI (that you can use with FastAPI).
Flask REST frameworks¶
There are several Flask REST frameworks, but after investing the time and work into investigating them, I found that many are discontinued or abandoned, with several standing issues that made them unfit.
Marshmallow¶
One of the main features needed by API systems is data "serialization" which is taking data from the code (Python) and converting it into something that can be sent through the network. For example, converting an object containing data from a database into a JSON object. Converting datetime
objects into strings, etc.
Another big feature needed by APIs is data validation, making sure that the data is valid, given certain parameters. For example, that some field is an int
, and not some random string. This is especially useful for incoming data.
Without a data validation system, you would have to do all the checks by hand, in code.
These features are what Marshmallow was built to provide. It is a great library, and I have used it a lot before.
But it was created before there existed Python type hints. So, to define every schema you need to use specific utils and classes provided by Marshmallow.
Inspired FastAPI to
Use code to define "schemas" that provide data types and validation, automatically.
Webargs¶
Another big feature required by APIs is parsing data from incoming requests.
Webargs is a tool that was made to provide that on top of several frameworks, including Flask.
It uses Marshmallow underneath to do the data validation. And it was created by the same developers.
It's a great tool and I have used it a lot too, before having FastAPI.
Info
Webargs was created by the same Marshmallow developers.
Inspired FastAPI to
Have automatic validation of incoming request data.
APISpec¶
Marshmallow and Webargs provide validation, parsing and serialization as plug-ins.
But documentation is still missing. Then APISpec was created.
It is a plug-in for many frameworks (and there's a plug-in for Starlette too).
The way it works is that you write the definition of the schema using YAML format inside the docstring of each function handling a route.
And it generates OpenAPI schemas.
That's how it works in Flask, Starlette, Responder, etc.
But then, we have again the problem of having a micro-syntax, inside of a Python string (a big YAML).
The editor can't help much with that. And if we modify parameters or Marshmallow schemas and forget to also modify that YAML docstring, the generated schema would be obsolete.
Info
APISpec was created by the same Marshmallow developers.
Inspired FastAPI to
Support the open standard for APIs, OpenAPI.
Flask-apispec¶
It's a Flask plug-in, that ties together Webargs, Marshmallow and APISpec.
It uses the information from Webargs and Marshmallow to automatically generate OpenAPI schemas, using APISpec.
It's a great tool, very under-rated. It should be way more popular than many Flask plug-ins out there. It might be due to its documentation being too concise and abstract.
This solved having to write YAML (another syntax) inside of Python docstrings.
This combination of Flask, Flask-apispec with Marshmallow and Webargs was my favorite backend stack until building FastAPI.
Using it led to the creation of several Flask full-stack generators. These are the main stack I (and several external teams) have been using up to now:
- https://github.com/tiangolo/full-stack
- https://github.com/tiangolo/full-stack-flask-couchbase
- https://github.com/tiangolo/full-stack-flask-couchdb
And these same full-stack generators were the base of the FastAPI Project Generators.
Info
Flask-apispec was created by the same Marshmallow developers.
Inspired FastAPI to
Generate the OpenAPI schema automatically, from the same code that defines serialization and validation.
NestJS (and Angular)¶
This isn't even Python, NestJS is a JavaScript (TypeScript) NodeJS framework inspired by Angular.
It achieves something somewhat similar to what can be done with Flask-apispec.
It has an integrated dependency injection system, inspired by Angular two. It requires pre-registering the "injectables" (like all the other dependency injection systems I know), so, it adds to the verbosity and code repetition.
As the parameters are described with TypeScript types (similar to Python type hints), editor support is quite good.
But as TypeScript data is not preserved after compilation to JavaScript, it cannot rely on the types to define validation, serialization and documentation at the same time. Due to this and some design decisions, to get validation, serialization and automatic schema generation, it's needed to add decorators in many places. So, it becomes quite verbose.
It can't handle nested models very well. So, if the JSON body in the request is a JSON object that has inner fields that in turn are nested JSON objects, it cannot be properly documented and validated.
Inspired FastAPI to
Use Python types to have great editor support.
Have a powerful dependency injection system. Find a way to minimize code repetition.
Sanic¶
It was one of the first extremely fast Python frameworks based on asyncio
. It was made to be very similar to Flask.
Technical Details
It used uvloop
instead of the default Python asyncio
loop. That's what made it so fast.
It clearly inspired Uvicorn and Starlette, that are currently faster than Sanic in open benchmarks.
Inspired FastAPI to
Find a way to have a crazy performance.
That's why FastAPI is based on Starlette, as it is the fastest framework available (tested by third-party benchmarks).
Falcon¶
Falcon is another high performance Python framework, it is designed to be minimal, and work as the foundation of other frameworks like Hug.
It uses the previous standard for Python web frameworks (WSGI) which is synchronous, so it can't handle WebSockets and other use cases. Nevertheless, it also has a very good performance.
It is designed to have functions that receive two parameters, one "request" and one "response". Then you "read" parts from the request, and "write" parts to the response. Because of this design, it is not possible to declare request parameters and bodies with standard Python type hints as function parameters.
So, data validation, serialization, and documentation, have to be done in code, not automatically. Or they have to be implemented as a framework on top of Falcon, like Hug. This same distinction happens in other frameworks that are inspired by Falcon's design, of having one request object and one response object as parameters.
Inspired FastAPI to
Find ways to get great performance.
Along with Hug (as Hug is based on Falcon) inspired FastAPI to declare a response
parameter in functions.
Although in FastAPI it's optional, and is used mainly to set headers, cookies, and alternative status codes.
Molten¶
I discovered Molten in the first stages of building FastAPI. And it has quite similar ideas:
- Based on Python type hints.
- Validation and documentation from these types.
- Dependency Injection system.
It doesn't use a data validation, serialization and documentation third-party library like Pydantic, it has its own. So, these data type definitions would not be reusable as easily.
It requires a little bit more verbose configurations. And as it is based on WSGI (instead of ASGI), it is not designed to take advantage of the high-performance provided by tools like Uvicorn, Starlette and Sanic.
The dependency injection system requires pre-registration of the dependencies and the dependencies are solved based on the declared types. So, it's not possible to declare more than one "component" that provides a certain type.
Routes are declared in a single place, using functions declared in other places (instead of using decorators that can be placed right on top of the function that handles the endpoint). This is closer to how Django does it than to how Flask (and Starlette) does it. It separates in the code things that are relatively tightly coupled.
Inspired FastAPI to
Define extra validations for data types using the "default" value of model attributes. This improves editor support, and it was not available in Pydantic before.
This actually inspired updating parts of Pydantic, to support the same validation declaration style (all this functionality is now already available in Pydantic).
Hug¶
Hug was one of the first frameworks to implement the declaration of API parameter types using Python type hints. This was a great idea that inspired other tools to do the same.
It used custom types in its declarations instead of standard Python types, but it was still a huge step forward.
It also was one of the first frameworks to generate a custom schema declaring the whole API in JSON.
It was not based on a standard like OpenAPI and JSON Schema. So it wouldn't be straightforward to integrate it with other tools, like Swagger UI. But again, it was a very innovative idea.
It has an interesting, uncommon feature: using the same framework, it's possible to create APIs and also CLIs.
As it is based on the previous standard for synchronous Python web frameworks (WSGI), it can't handle Websockets and other things, although it still has high performance too.
Info
Hug was created by Timothy Crosley, the same creator of isort
, a great tool to automatically sort imports in Python files.
Ideas inspired in FastAPI
Hug inspired parts of APIStar, and was one of the tools I found most promising, alongside APIStar.
Hug helped inspiring FastAPI to use Python type hints to declare parameters, and to generate a schema defining the API automatically.
Hug inspired FastAPI to declare a response
parameter in functions to set headers and cookies.
APIStar (<= 0.5)¶
Right before deciding to build FastAPI I found APIStar server. It had almost everything I was looking for and had a great design.
It was one of the first implementations of a framework using Python type hints to declare parameters and requests that I ever saw (before NestJS and Molten). I found it more or less at the same time as Hug. But APIStar used the OpenAPI standard.
It had automatic data validation, data serialization and OpenAPI schema generation based on the same type hints in several places.
Body schema definitions didn't use the same Python type hints like Pydantic, it was a bit more similar to Marshmallow, so, editor support wouldn't be as good, but still, APIStar was the best available option.
It had the best performance benchmarks at the time (only surpassed by Starlette).
At first, it didn't have an automatic API documentation web UI, but I knew I could add Swagger UI to it.
It had a dependency injection system. It required pre-registration of components, as other tools discussed above. But still, it was a great feature.
I was never able to use it in a full project, as it didn't have security integration, so, I couldn't replace all the features I was having with the full-stack generators based on Flask-apispec. I had in my backlog of projects to create a pull request adding that functionality.
But then, the project's focus shifted.
It was no longer an API web framework, as the creator needed to focus on Starlette.
Now APIStar is a set of tools to validate OpenAPI specifications, not a web framework.
Info
APIStar was created by Tom Christie. The same guy that created:
- Django REST Framework
- Starlette (in which FastAPI is based)
- Uvicorn (used by Starlette and FastAPI)
Inspired FastAPI to
Exist.
The idea of declaring multiple things (data validation, serialization and documentation) with the same Python types, that at the same time provided great editor support, was something I considered a brilliant idea.
And after searching for a long time for a similar framework and testing many different alternatives, APIStar was the best option available.
Then APIStar stopped to exist as a server and Starlette was created, and was a new better foundation for such a system. That was the final inspiration to build FastAPI.
I consider FastAPI a "spiritual successor" to APIStar, while improving and increasing the features, typing system, and other parts, based on the learnings from all these previous tools.
Used by FastAPI¶
Pydantic¶
Pydantic is a library to define data validation, serialization and documentation (using JSON Schema) based on Python type hints.
That makes it extremely intuitive.
It is comparable to Marshmallow. Although it's faster than Marshmallow in benchmarks. And as it is based on the same Python type hints, the editor support is great.
FastAPI uses it to
Handle all the data validation, data serialization and automatic model documentation (based on JSON Schema).
FastAPI then takes that JSON Schema data and puts it in OpenAPI, apart from all the other things it does.
Starlette¶
Starlette is a lightweight ASGI framework/toolkit, which is ideal for building high-performance asyncio services.
It is very simple and intuitive. It's designed to be easily extensible, and have modular components.
It has:
- Seriously impressive performance.
- WebSocket support.
- GraphQL support.
- In-process background tasks.
- Startup and shutdown events.
- Test client built on requests.
- CORS, GZip, Static Files, Streaming responses.
- Session and Cookie support.
- 100% test coverage.
- 100% type annotated codebase.
- Zero hard dependencies.
Starlette is currently the fastest Python framework tested. Only surpassed by Uvicorn, which is not a framework, but a server.
Starlette provides all the basic web microframework functionality.
But it doesn't provide automatic data validation, serialization or documentation.
That's one of the main things that FastAPI adds on top, all based on Python type hints (using Pydantic). That, plus the dependency injection system, security utilities, OpenAPI schema generation, etc.
Technical Details
ASGI is a new "standard" being developed by Django core team members. It is still not a "Python standard" (a PEP), although they are in the process of doing that.
Nevertheless, it is already being used as a "standard" by several tools. This greatly improves interoperability, as you could switch Uvicorn for any other ASGI server (like Daphne or Hypercorn), or you could add ASGI compatible tools, like python-socketio
.
FastAPI uses it to
Handle all the core web parts. Adding features on top.
The class FastAPI
itself inherits directly from the class Starlette
.
So, anything that you can do with Starlette, you can do it directly with FastAPI, as it is basically Starlette on steroids.
Uvicorn¶
Uvicorn is a lightning-fast ASGI server, built on uvloop and httptools.
It is not a web framework, but a server. For example, it doesn't provide tools for routing by paths. That's something that a framework like Starlette (or FastAPI) would provide on top.
It is the recommended server for Starlette and FastAPI.
FastAPI recommends it as
The main web server to run FastAPI applications.
You can combine it with Gunicorn, to have an asynchronous multi-process server.
Check more details in the Deployment section.
Benchmarks and speed¶
To understand, compare, and see the difference between Uvicorn, Starlette and FastAPI, check the section about Benchmarks.


Gecode::ActorLink Class Reference
Inherited by
Gecode::Actor [private]
, and
Gecode::Advisor [private]
.
List of all members.
Detailed Description
Double-linked list for actors.
Used to maintain which actors belong to a space and also (for propagators) to organize actors in the queue of waiting propagators.
Definition at line 514 of file core.icc.
|
| ActorLink * | prev (void) const |
| | Routines for double-linked list.
|
| void | prev (ActorLink *) |
| | Routines for double-linked list.
|
| ActorLink * | next (void) const |
| | Routines for double-linked list.
|
| void | next (ActorLink *) |
| | Routines for double-linked list.
|
| ActorLink ** | next_ref (void) |
| | Routines for double-linked list.
|
Public Member Functions |
| void | init (void) |
| | Initialize links (self-linked).
|
| void | unlink (void) |
| | Remove from predecessor and successor.
|
| void | head (ActorLink *al) |
| | Insert al directly after this.
|
| void | tail (ActorLink *al) |
| | Insert al directly before this.
|
Static Public Member Functions |
| template<class T> |
| static ActorLink * | cast (T *a) |
| | Static cast for a non-null pointer (to give a hint to optimizer).
|
| template<class T> |
| static const ActorLink * | cast (const T *a) |
| | Static cast for a non-null pointer (to give a hint to optimizer).
|
Member Function Documentation
| ActorLink * Gecode::ActorLink::prev |
( |
void |
|
) |
const [inline] |
Routines for double-linked list.
Definition at line 1488 of file core.icc.
| void Gecode::ActorLink::prev |
( |
ActorLink * |
al |
) |
[inline] |
Routines for double-linked list.
Definition at line 1503 of file core.icc.
| ActorLink * Gecode::ActorLink::next |
( |
void |
|
) |
const [inline] |
Routines for double-linked list.
Definition at line 1493 of file core.icc.
| void Gecode::ActorLink::next |
( |
ActorLink * |
al |
) |
[inline] |
Routines for double-linked list.
Definition at line 1508 of file core.icc.
| ActorLink ** Gecode::ActorLink::next_ref |
( |
void |
|
) |
[inline] |
Routines for double-linked list.
Definition at line 1498 of file core.icc.
| void Gecode::ActorLink::init |
( |
void |
|
) |
[inline] |
Initialize links (self-linked).
Definition at line 1519 of file core.icc.
| void Gecode::ActorLink::unlink |
( |
void |
|
) |
[inline] |
Remove from predecessor and successor.
Definition at line 1513 of file core.icc.
| void Gecode::ActorLink::head |
( |
ActorLink * |
al |
) |
[inline] |
Insert al directly after this.
Definition at line 1524 of file core.icc.
| void Gecode::ActorLink::tail |
( |
ActorLink * |
al |
) |
[inline] |
Insert al directly before this.
Definition at line 1532 of file core.icc.
template<class T>
| ActorLink * Gecode::ActorLink::cast |
( |
T * |
a |
) |
[inline, static] |
Static cast for a non-null pointer (to give a hint to optimizer).
Definition at line 1541 of file core.icc.
template<class T>
| const ActorLink * Gecode::ActorLink::cast |
( |
const T * |
a |
) |
[inline, static] |
Static cast for a non-null pointer (to give a hint to optimizer).
Definition at line 1550 of file core.icc.
The documentation for this class was generated from the following file:


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Use this document as a way to quick start any new project.
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The State of the State of the States
Name: Viraj Rathod; E-Mail<EMAIL_ADDRESS>UID: u1269659
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Rain
Maybe it's because i never liked clear skies. The vast emptiness of the sky above always filled me with some kind of boredom. As if the sky was empty and there was nothing above. If it was raining, i could feel the sky on my skin through the falling raindrops.
Maybe it's because I liked the rain jokes. Cynical family members perverting the purple dinosaur's "Rain, Rain, Go Away" song.
Maybe it's because the rain is the perfect excuse. It's raining, so let's just stay inside today. It's raining, so let's just watch the T.V. And waste the time away. It's raining, so let's drink some of that hot coffee, hit the couch and play some games. It's raining, so lets just stay in bed, 3 layers of blankets, closed shutters, and me napping the morning away.
It's raining, so lets not do that thing that we were suppose to do today.


>>
<<
Usr
Pri
JfC
LJ
Phr
Dic
Voc
!:
Help
User
Starting J - Details
profile sequence
JFE loads profile which in turn loads several other files to initialize your J session. Foreign 4!:3 returns script names in the order they were loaded. This can be a powerful tool in understanding the initialization process.
Start Jconsole and see what scripts are loaded.
>4!:3''
C:\Users\Eric\j807\bin\profile.ijs
C:\Users\Eric\j807\system\util\boot.ijs
C:\Users\Eric\j807\system\main\stdlib.ijs
C:\Users\Eric\j807\system\util\scripts.ijs
C:\Users\Eric\j807\system\main\regex.ijs
C:\Users\Eric\j807\system\main\task.ijs
C:\Users\Eric\j807\system\util\configure.ijs
...
J Front End initialization sentence
JFE runs a first sentence that defines BINPATH_z_ and ARGV_z and performs other required initialization (such as loading J profile).
BINPATH_z_ is set as the full path to the J bin folder.
ARGV_z_ is set as list of boxed string parameters from the command line.
The load of a script is done with an unamed verb (3 : '0!:0 y') so that =. is local to the verb.
The sentence always ends with:
[ARGV_z_=:...[BINPATH_z_=:...
[A[B indicates this standard ending in the following examples.
Without -jprofile the first sentence has the form:
(3 : '0!:0 y')<BINPATH,'\profile.ijs'[A[B
With -jprofile F:
(3 : '0!:0 y')2{ARGV[A[B
Jconsole with just -jprofile:
i.0 0[A[B
>>
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Prizes!
These kind supporters are providing prizes to be raffled off to attendees at the conference.
{{ range $prizes }}
{{ partial "prize.html" . }}
{{ end }}
Get involved
Local company? Want to join in the fun (or just ingratiate yourselves with attendees through the medium of swag)? Email us and let us know what prize you're donating. If you're not sure what to donate, here are some ideas submitted by local devs:
- Your own product or service
- Drones
- Amazon Echo
- Philips Hue kit
- Raspberry Pi 3 dev kit
- GoPro
- Headphones (noise cancelling)
- Coffee machine
- Sonos
- Chromebook
- Online training subscription (Safari, Pluralsight, etc)
- Software subscription (e.g. JetBrains)
- Amazon vouchers


--- notes: | Widths are percentages represented by fractions. All classes are constructed like `.w-{nominator}/{denominator}`. By default you have halves, thirds, quarters, fifths and sixths. You can easily add more by changing the `$widths-columns` setting. Widths are independent from the grid, you’re free to use them anywhere, with other components such as mosaic, media, etc. ---


Sorting and Layouting¶
Synopsis¶
Pangenome graphs built from raw sets of alignments may have complex structures which can introduce difficulty in
downstream analyses, visualization, mapping, and interpretation. Graph sorting aims to find the best node order for
a 1D and 2D layout to simplify these complex regions. Pangenome graphs embed linear pangenomic sequences as paths in
the graph, but to our knowledge, no algorithm takes into account this biological information in the sorting. Moreover,
existing 2D layout methods struggle to deal with large graphs. odgi
implements a new layout algorithm to simplify a pangenome
graph, by using path-guided stochastic gradient descent
(PG-SGD) to move a single pair of nodes at a time.
The PG-SGD is memory polite, because it uses a path index, a strict subset of the xg index. Following a parallelized, lock-free SGD approach,
the PG-SGD can go Hogwild!
The 1D path-guided SGD implementation is a key step in general pangenome analyses such as pangenome graph linearization and simplification. It is applied in the PangenomeGraph Builder (PGGB) pipeline.
This tutorial shows how to sort and visualize a graph in 1D. It explains how to generate a 2D layout of a graph, and how to take a look at the calculated layout using static and interactive tools.
Note
Be aware that odgi sort offers much more sorting algorithms than this tutorial could cover here.
Steps¶
Build the unsorted DRB1-3123 graph¶
Assuming that your current working directory is the root of the odgi
project, to construct an odgi
graph from the
DRB1-3123
dataset in GFA
format, execute:
odgi build -g test/DRB1-3123_unsorted.gfa -o DRB1-3123_unsorted.og
The command creates a file called DRB1-3123_unsorted.og
, which contains the input graph in odgi
format. This graph contains
12 ALT sequences of the HLA-DRB1 gene from the GRCh38 reference genome.
The graph was initially created with the seqwish variation graph inducer which produces unsorted, raw graphs from
all versus all alignments of the input sequences.
Visualize the unsorted DRB1-3123 graph¶
odgi viz -i DRB1-3123_unsorted.og -o DRB1-3123_unsorted.png
To obtain the following PNG image:
In this 1-Dimensional visualization:
The graph nodes are arranged from left to right, forming the
pangenome sequence
.The colored bars represent the binned, linearized renderings of the embedded paths versus this
pangenome sequence
in a binary matrix.The path names are visualized on the left.
The black lines under the paths are the links, which represent the graph topology.
The graph is very complex and, in this form, the underlying structure is unclear. It is hard to reason from it. It is a perfect candidate for the 1D PG-SGD: It will find the best 1D node order effectively linearizing it.
1D layout metrics of the unsorted DRB1-3123 graph¶
odgi stats provides metrics to evaluate the goodness of the sort of a variation graph. Let’s take a look:
odgi stats -i DRB1-3123_unsorted.og -s -d -l -g
Where:
-s
calculates the sum of path node distances.-l
calculates the mean links length.-d
additionally penalizes links which connect nodes with different orientation.-g
ensures that gap links, links directly travelling from left to right encoding simple structural variants, are not penalized.
For further details on these metrics please take a look at the odgi stats command.
We observe on stdout:
#mean_links_length
path in_node_space in_nucleotide_space num_links_considered num_gap_links_not_penalized
all_paths<PHONE_NUMBER>.92 21870 11116
#sum_of_path_node_distances
path in_node_space in_nucleotide_space nodes nucleotides num_penalties num_penalties_different_orientation
all_paths 1029.84 1076.32 21882<PHONE_NUMBER> 1
Sort the unsorted DRB1-3123 graph in 1D¶
Let’s sort the graph with the 1D PG-SGD algorithm:
odgi sort -i DRB1-3123_unsorted.og --threads 2 -P -Y -o DRB1-3123_sorted.og
-Y
selects the PG-SGD algorithm for sorting. This algorithm moves a single pair of nodes at a time, optimizing
the disparity between the layout distance of a node pair and the actual nucleotide distance of a path traversing these
nodes.
Figure from Zheng et al., IEEE 2019.
The first node Xi of a pair is a uniform path step pick from all nodes.
The second node Xj of a pair is sampled from the same path following a Zipfian distribution.
The path nucleotide distance of the nodes in the pair guides the actual layout distance dij update of these nodes.
The magnitude r of the update depends on the current learning rate of the SGD.
Note
The PG-SGD is not deterministic, because of its Hogwild! approach.
To reproduce the visualization below, the sorted graph can be found under test/DRB1-3123_sorted.og
.
Visualize the 1D sorted DRB1-3123 graph¶
odgi viz -i DRB1-3123_sorted.og -o DRB1-3123_sorted.png
The graph lost it’s complexity and is now linear.
1D layout metrics of the sorted DRB1-3123 graph¶
odgi stats -i DRB1-3123_sorted.og -s -d -l -g
This prints to stdout:
#mean_links_length
path in_node_space in_nucleotide_space num_links_considered num_gap_links_not_penalized
all_paths 2.15542 15.0529 21870 9481
#sum_of_path_node_distances
path in_node_space in_nucleotide_space nodes nucleotides num_penalties num_penalties_different_orientation
all_paths 4.66114 4.72171 21882<PHONE_NUMBER> 1
Compare to before, these metrics show that the goodness of the sorting of the graph improved significantly. Great!
2D layout of the unsorted DRB1-3123 graph¶
We want to have a 2D layout of our DRB1-3123 graph:
odgi layout -i DRB1-3123_unsorted.og -o DRB1-3123_unsorted.og.lay -P --threads 2
Drawing the 2D layout of the DRB1-3123 graph¶
Calculate the 2D layout:
odgi draw -i DRB1-3123_unsorted.og -c DRB1-3123_unsorted.og.lay -p DRB1-3123_unsorted.og.lay.png -C -w 50
Interactive visualization with gfaestus¶
odgi layout -i DRB1-3123_unsorted.og -o DRB1-3123_unsorted.og.lay -P --threads 2 -T DRB1-3123_unsorted.og.tsv
gfaestus test/DRB1-3123_unsorted.gfa DRB1-3123_unsorted.og.tsv


We have deployed Suma Crm version v.1.1
We are introducing following new feature in this deployment :
-
Super Admin : Under this we will add Group/Company and can register new users to that group.
Currently all registered users will fall under the "SumaSoft" Group.
-
Admin : This will be accessible only for admin users, so we can have the Role Management and Access Management under this.
The following users are admin/superadmin at this point :
- Pankaj Dewangam
- Parag Waghela
- Naitik Soni
- Bharat Tambe
- Arun Sahni
- Aditya Chitale
I mean they can make you admin too... :-)
Please visit our application : http://suma-crm.herokuapp.com/
You are welcome with your suggestions
SumaSoft Development Team


Home
What is FGO?
Fate Gand order is an online, free to play Japanese role-playing game mobile game that was released in Japan in 2015.
The game was then brought to North America and Europe in 2017. The game is based on the anime franchise and light novel
series of Fate or Fate Stay Night.
The game is centered around turn-based combat where the player (known as the master
within the game) controls a wide variety of characters (called servants within the game) to complete different objectives
and progress in the story. Servents have different classes giving them different advantages in the game and each character
has a unique special attack called a noble phantasm. These servents can be acquired through a system known as "gacha" where
you spend saint quarts to potentially roll your desired servent. The player has a party of 6 servents in each battle,
3 active members, and 3 on reserve. Each turn, the player is given a set of 5 command cards and may use 3 to attack.
The Cards are categorized as a buster, arts, and crit cards with each having different advantages. Buster cards do more
damage, Arts cards help increase your noble phantasm gauge more efficiently, and crit cards generate more crit stars
for your servents. The player also has three special abilities called “command spells” that can be used at any time
during a battle.
Objective
This webpage aims to show some of the class advantages and disadvantages the main 7 classes have within the game. Following this guide will allow you to create a good team for any mission you confront. Along with the class advantages/disadvantages, there will be a short explanation behind every class. There is also a gacha to simulate your potential servent rolls.


Notebooks
Story: The great Star !gaunu, which singing named the stars
The great Star !gaunu, which singing named the stars
Title
The great Star !gaunu, which singing named the stars
Collection
Summary
The great star !gãunu, which singing, named the stars. By the position of certain stars, named by !gãunu, the porcupine knows the time for returning home. In the Katkop dialect, by Díä!kwãin (L V.-20. 5576-5580). The star !gaunu named small stars 'aquilae' – they influence the porcupine at dawn and are thus called the Dawn's stars.
Comments
1) p.5575v: Dia!kwain's paternal grandfather (!xugen-ddi) told him the stars' stories, 2) See also Stars and flowers and A song sung by the star !gaunu, especially by the Bushman women and !gaunu and Names of stars and Names for the star Canopus and The name of a star in the Katkop dialect: the Hare's star, 3) This story is found in Book V-20
Contributors
Date
7 –11 December 1875
Categories
Celestial bodies and aeroscopy
Keywords
stars (||xwhai) , stars (little) , stars (great) , stars (the names of) , stars (!gaunu) , stars (the naming of) , stars (!gaunu names them in song) , stars (their song) , stars (the Dawn's, or aquilae) , stars (and the nature and habits of the Porcupine) , stars (turn back at dawn) , stars (their actions) , !gaunu (named the stars) , !gaunu (great Star) , !gaunu (and the Dawn's stars) , !gaunu (his song) , !gaunu (singing named the stars) , Porcupine (its nature and habits and the stars) , Porcupine (and the actions of the Dawn's stars) , Porcupine (knows the time for returning home from the Dawn's stars) , Porcupine (and the great Star !gaunu, which singing named the stars) , Dawn's stars (aquilae) , Dawn's stars (||xwhai) , Dawn's stars (and the nature and habits of porcupines) , Dawn's stars (!gaunu named them) , song (!gaunu's naming of the stars) , name (the great Star !gaunu, singing named the stars) , name (|xam names of stars) , name (names of the Dawn's stars or aquilae)
Story Pages
5576-5580


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Let's play a number game! It's called ping pong, but it's a bit different than the table game. It's super simple, just enter a number! Then the program will give you alllll numbers leading up to that number and it will:
Replace all numbers divisible by 3 with "ping"
Replace all numbers divisible by 5 with "pong"
Replace all numbers divisible by 15 with "ping-pong"