La API de Markdown Maker simplifica el proceso de convertir contenido web en markdown estructurado o texto limpio. Su punto final de texto limpio garantiza que solo se recupere el contenido relevante, eliminando menús, anuncios u otros elementos no esenciales. El punto final de markdown permite además a los desarrolladores transformar contenido en markdown, agilizando los flujos de trabajo para sistemas de gestión de contenido, blogs o documentación. Diseñada para la versatilidad, la API admite una amplia gama de páginas web y formatos para una integración fluida y un rendimiento confiable.
Para usar este punto final, envíe una solicitud con la URL de la página web y reciba el texto limpio extraído del contenido de esa página.
Contenido de Markdown Extracto - Características del Endpoint
| Objeto | Descripción |
|---|---|
Cuerpo de la Solicitud |
[Requerido] Json |
{"response":"Spark Basics\nSuppose we have a web application hosted in an application orchestrator like kubernetes. If load in that particular application increases then we can horizontally scale our application simply by increasing the number of pods in our service.\nNow let’s suppose there is heavy compute operation happening in each of the pods. Then there will be certain limit upto which these services can run because unlike horizontal scaling where you can have as many numbers of machines as required, there is limit for vertical scaling because you can’t have unlimited ram and cpu cores for each of the machines in a cluster. Distributed Computing removes this limitation of vertical scaling by distributing the processing across cluster of machines. Now, a group of machines alone is not powerful, you need a framework to coordinate work across them. Spark does just that, managing and coordinating the execution of tasks on data across a cluster of computers. The cluster of machines that Spark will use to execute tasks is managed by a cluster manager like Spark’s standalone cluster manager, Kubernetes, YARN, or Mesos.\nSpark Basics\nSpark is distributed data processing engine. Distributed data processing in big data is simply series of map and reduce functions which runs across the cluster machines. Given below is python code for calculating the sum of all the even numbers from a given list with the help of map and reduce functions.\nfrom functools import reduce\na = [1,2,3,4,5]\nres = reduce(lambda x,y: x+y, (map(lambda x: x if x%2==0 else 0, a)))\nNow consider, if instead of a simple list, it is a parquet file of size in order of gigabytes. Computation with MapReduce system becomes optimized way of dealing with such problems. In this case spark will load the big parquet file into multiple worker nodes (if the file doesn’t support distributed storage then it will be first loaded into driver node and afterwards, it will get distributed across the worker nodes). Then map function will be executed for each task in each worker node and the final result will fetched with the reduce function.\nSpark timeline\nGoogle was first to introduce large scale distributed computing solution with MapReduce and its own distributed file system i.e., Google File System(GFS). GFS provided a blueprint for the Hadoop File System (HDFS), including the MapReduce implementation as a framework for distributed computing. Apache Hadoop framework was developed consisting of Hadoop Common, MapReduce, HDFS, and Apache Hadoop YARN. There were various limitations with Apache Hadoop like it fell short for combining other workloads such as machine learning, streaming, or interactive SQL-like queries etc. Also the results of the reduce computations were written to a local disk for subsequent stage of operations. Then came the Spark. Spark provides in-memory storage for intermediate computations, making it much faster than Hadoop MapReduce. It incorporates libraries with composable APIs for machine learning (MLlib), SQL for interactive queries (Spark SQL), stream processing (Structured Streaming) for interacting with real-time data, and graph processing (GraphX).\nSpark Application\nSpark Applications consist of a driver process and a set of executor processes. The driver process runs your main() function, sits on a node in the cluster. The executors are responsible for actually carrying out the work that the driver assigns them. The driver and executors are simply processes, which means that they can live on the same machine or different machines.\nThere is a SparkSession object available to the user, which is the entrance point to running Spark code. When using Spark from Python or R, you don’t write explicit JVM instructions; instead, you write Python and R code that Spark translates into code that it then can run on the executor JVMs.\nSpark’s language APIs make it possible for you to run Spark code using various programming languages like Scala, Java, Python, SQL and R.\nSpark has two fundamental sets of APIs: the low-level “unstructured” APIs (RDDs), and the higher-level structured APIs (Dataframes, Datasets).\nSpark Toolsets\nA DataFrame is the most common Structured API and simply represents a table of data with rows and columns. To allow every executor to perform work in parallel, Spark breaks up the data into chunks called partitions. A partition is a collection of rows that sit on one physical machine in your cluster.\nIf a function returns a Dataframe or Dataset or Resilient Distributed Dataset (RDD) then it is a transformation and if it doesn’t return anything then it’s an action. An action instructs Spark to compute a result from a series of transformations. The simplest action is count.\nTransformation are of types narrow and wide. Narrow transformations are those for which each input partition will contribute to only one output partition. Wide transformation will have input partitions contributing to many output partitions.\nSparks performs a lazy evaluation which means that Spark will wait until the very last moment to execute the graph of computation instructions. This provides immense benefits because Spark can optimize the entire data flow from end to end.\nSpark-submit\nReferences\n- https://spark.apache.org/docs/latest/\n- spark: The Definitive Guide by Bill Chambers and Matei Zaharia"}
curl --location --request POST 'https://zylalabs.com/api/5661/markdown+maker+api/7371/markdown+content+extracto' --header 'Authorization: Bearer YOUR_API_KEY'
--data-raw '{
"url": "https://techtalkverse.com/post/software-development/spark-basics/"
}'
Para utilizar este punto final, envíe una solicitud con la URL de la página web y reciba el contenido convertido al formato markdown de esa página.
Web a Markdown - Características del Endpoint
| Objeto | Descripción |
|---|---|
Cuerpo de la Solicitud |
[Requerido] Json |
{"response":"---\ntitle: Spark Basics\nurl: https://techtalkverse.com/post/software-development/spark-basics/\nhostname: techtalkverse.com\ndescription: Suppose we have a web application hosted in an application orchestrator like kubernetes. If load in that particular application increases then we can horizontally scale our application simply by increasing the number of pods in our service.\nsitename: techtalkverse.com\ndate: 2023-05-01\ncategories: ['post']\n---\n# Spark Basics\n\nSuppose we have a web application hosted in an application orchestrator like kubernetes. If load in that particular application increases then we can horizontally scale our application simply by increasing the number of pods in our service.\n\nNow let’s suppose there is heavy compute operation happening in each of the pods. Then there will be certain limit upto which these services can run because unlike horizontal scaling where you can have as many numbers of machines as required, there is limit for vertical scaling because you can’t have unlimited ram and cpu cores for each of the machines in a cluster. **Distributed Computing** removes this limitation of vertical scaling by distributing the processing across cluster of machines.\nNow, a group of machines alone is not powerful, you need a framework to\ncoordinate work across them. Spark does just that, managing and coordinating the execution of tasks on data across a cluster of computers. The cluster of machines that Spark will use to execute tasks is managed by a cluster manager like Spark’s standalone cluster manager, Kubernetes, YARN, or Mesos.\n\n## Spark Basics\n\nSpark is distributed data processing engine. Distributed data processing in big data is simply series of map and reduce functions which runs across the cluster machines. Given below is python code for calculating the sum of all the even numbers from a given list with the help of map and reduce functions.\n\n```\nfrom functools import reduce\na = [1,2,3,4,5]\nres = reduce(lambda x,y: x+y, (map(lambda x: x if x%2==0 else 0, a)))\n```\n\n\nNow consider, if instead of a simple list, it is a parquet file of size in order of gigabytes. Computation with MapReduce system becomes optimized way of dealing with such problems. In this case spark will load the big parquet file into multiple worker nodes (if the file doesn’t support distributed storage then it will be first loaded into driver node and afterwards, it will get distributed across the worker nodes). Then map function will be executed for each task in each worker node and the final result will fetched with the reduce function.\n\n## Spark timeline\n\nGoogle was first to introduce large scale distributed computing solution with **MapReduce** and its own distributed file system i.e., **Google File System(GFS)**. GFS provided a blueprint for the **Hadoop File System (HDFS)**, including the MapReduce implementation as a framework for distributed computing. **Apache Hadoop** framework was developed consisting of Hadoop Common, MapReduce, HDFS, and Apache Hadoop YARN. There were various limitations with Apache Hadoop like it fell short for combining other workloads such as machine learning, streaming, or interactive SQL-like queries etc. Also the results of the reduce computations were written to a local disk for subsequent stage of operations. Then came the **Spark**. Spark provides in-memory storage for intermediate computations, making it much faster than Hadoop MapReduce. It incorporates libraries with composable APIs for\nmachine learning (MLlib), SQL for interactive queries (Spark SQL), stream processing (Structured Streaming) for interacting with real-time data, and graph processing (GraphX).\n\n## Spark Application\n\n**Spark Applications** consist of a driver process and a set of executor processes. The **driver** process runs your main() function, sits on a node in the cluster. The **executors** are responsible for actually carrying out the work that the driver assigns them. The driver and executors are simply processes, which means that they can live on the same machine or different machines.\n\nThere is a **SparkSession** object available to the user, which is the entrance point to running Spark code. When using Spark from Python or R, you don’t write explicit JVM instructions; instead, you write Python and R code that Spark translates into code that it then can run on the executor JVMs.\n**Spark’s language APIs** make it possible for you to run Spark code using various programming languages like Scala, Java, Python, SQL and R.\nSpark has two fundamental sets of APIs: the **low-level “unstructured” APIs** (RDDs), and the **higher-level structured APIs** (Dataframes, Datasets).\n\n## Spark Toolsets\n\nA **DataFrame** is the most common Structured API and simply represents a table of data with rows and columns. To allow every executor to perform work in parallel, Spark breaks up the data into chunks called partitions. A **partition** is a collection of rows that sit on one physical machine in your cluster.\n\nIf a function returns a Dataframe or Dataset or Resilient Distributed Dataset (RDD) then it is a **transformation** and if it doesn’t return anything then it’s an **action**. An action instructs Spark to compute a result from a series of transformations. The simplest action is count.\n\nTransformation are of types narrow and wide. **Narrow transformations** are those for which each input partition will contribute to only one output partition. **Wide transformation** will have input partitions contributing to many output partitions.\n\nSparks performs a **lazy evaluation** which means that Spark will wait until the very last moment to execute the graph of computation instructions. This provides immense benefits because Spark can optimize the entire data flow from end to end.\n\n## Spark-submit\n\n## References\n\n- https://spark.apache.org/docs/latest/\n- spark: The Definitive Guide by Bill Chambers and Matei Zaharia"}
curl --location --request POST 'https://zylalabs.com/api/5661/markdown+maker+api/7372/web+to+markdown' --header 'Authorization: Bearer YOUR_API_KEY'
--data-raw '{
"url": "https://techtalkverse.com/post/software-development/spark-basics/"
}'
| Encabezado | Descripción |
|---|---|
Autorización
|
[Requerido] Debería ser Bearer access_key. Consulta "Tu Clave de Acceso a la API" arriba cuando estés suscrito. |
Sin compromiso a largo plazo. Mejora, reduce o cancela en cualquier momento. La Prueba Gratuita incluye hasta 50 solicitudes.
La función principal de la API Markdown Maker es convertir páginas web en markdown estructurado o texto limpio, lo que permite una fácil integración y procesamiento del contenido web.
El endpoint de texto limpio recupera solo el contenido relevante de una página web, eliminando menús, anuncios y otros elementos no esenciales para proporcionar una salida enfocada.
Sí, la API Markdown Maker está diseñada para soportar una amplia gama de páginas web y formatos, asegurando versatilidad y un rendimiento confiable para diferentes tipos de contenido.
El endpoint de markdown permite a los desarrolladores transformar contenido web en formato markdown, lo que optimiza los flujos de trabajo para sistemas de gestión de contenido, blogs y documentación, facilitando la gestión y visualización del contenido.
Sí, la API Markdown Maker es particularmente adecuada para sistemas de gestión de contenido, ya que simplifica el proceso de extracción y formateo de contenido web, mejorando la eficiencia y la organización.
El punto final de texto limpio devuelve una salida de texto enfocada, eliminando elementos no esenciales como anuncios y menús. El punto final de markdown devuelve contenido estructurado en markdown, incluyendo metadatos como título, URL, descripción y categorías, junto con el contenido principal formateado en markdown.
Para el punto de acceso de texto limpio, el campo clave es "respuesta," que contiene el texto extraído. Para el punto de acceso de markdown, los campos clave incluyen "título," "url," "descripción," "nombre del sitio," "fecha," "categorías," y el contenido principal formateado en markdown.
La respuesta en texto limpio es una cadena simple bajo la clave "respuesta". La respuesta en markdown está estructurada con campos de metadatos seguidos del contenido principal, lo que permite un fácil análisis e integración en aplicaciones.
El endpoint de texto limpio proporciona contenido textual relevante de una página web, mientras que el endpoint de markdown ofrece tanto el contenido como los metadatos asociados, como el título, la URL y las categorías, facilitando una mejor gestión del contenido.
Los usuarios pueden personalizar las solicitudes especificando diferentes URL para los puntos finales. La API procesa el contenido de la URL proporcionada, lo que permite a los usuarios extraer o convertir varias páginas web según sea necesario.
Los casos de uso típicos incluyen la extracción de contenido para blogs, documentación y sistemas de gestión de contenido. Los desarrolladores pueden automatizar el proceso de recopilación y formateo de contenido web para facilitar la integración y visualización.
La API Markdown Maker se basa en la estructura de las páginas web que procesa. Aunque tiene como objetivo extraer contenido relevante con precisión, la calidad de la salida depende de la estructura de la página fuente y de la calidad del contenido.
Si la API devuelve resultados parciales o vacíos, los usuarios deben verificar la URL proporcionada para la accesibilidad y disponibilidad del contenido. Implementar un manejo de errores en las aplicaciones puede ayudar a gestionar tales situaciones de manera efectiva.
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