The Content Extract API is an advanced tool designed to facilitate the extraction of textual content from web pages in clean and structured formats. This API is especially geared towards users who need to efficiently and accurately obtain and analyze textual data from the web. With a series of specialized endpoints, the API allows the conversion of web content into clean text and markdown formats, adapting to various data processing and analysis needs.
Main Functionalities
Clean Text Extraction: The first API endpoint focuses on providing the clean textual content of a web page. This endpoint removes unwanted elements such as ads, menus and sidebars, leaving only relevant and meaningful text. Clean text extraction is ideal for applications that require clear, unformatted content for analysis or display, such as automatic summaries, search engines or content analysis tools.
Markdown conversion: The second endpoint converts web content into markdown format. Markdown is a lightweight markup language that allows text to be structured in a simple way, facilitating its integration into applications that use this format for document generation, blog posts or content management.
Support for Different Types of Pages: The Content Extract API is designed to handle a wide variety of web pages, from static sites to dynamic pages generated by JavaScript. This ensures that users can extract content from almost any type of page, regardless of its complexity or structure.
In short, the Content Extract API offers advanced solutions for extracting and converting textual content from web pages. With its specialized clean text and markdown endpoints, it provides users with effective tools for obtaining and managing web data in useful formats adaptable to a variety of applications and needs.Its flexibility and integration capabilities make it a valuable option for any task involving web content manipulation and analysis.
This API receives a web page URL and provides the clean text or markdown format of the content extracted from that page.
Content Generation for Blogs: Convert web content into markdown format for easy integration into blogging platforms or content management systems, facilitating publishing and editing.
Data Collection for Market Research: Extract clean text from various web pages to gather data on market trends, consumer behavior or competitive analysis.
News Brief Automation: Use the text extractor to create automated news summaries by removing non-relevant elements and focusing on the main content.
Technical Documentation Creation: Convert web content into markdown to develop technical documentation or user guides that integrate into collaborative documentation systems.
Data Extraction for SEO Tools: Extract clean text from web pages to analyze content and optimize SEO strategies, identifying relevant keywords and topics.
Beside the number of API calls per month allowed, there are no other limitations.
To use this endpoint, send a request with the URL of the web page and receive the clean text extracted from the content of that page.
Extract Info - Endpoint Features
| Object | Description |
|---|---|
Request Body |
[Required] 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/5081/content+extract+api/6473/extract+info' --header 'Authorization: Bearer YOUR_API_KEY'
--data-raw '{
"url": "https://techtalkverse.com/post/software-development/spark-basics/"
}'
To use this endpoint, send a request with the URL of the web page and receive the content converted to markdown format of that page.
Exc Marktdown - Endpoint Features
| Object | Description |
|---|---|
Request Body |
[Required] 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/5081/content+extract+api/6474/exc+marktdown' --header 'Authorization: Bearer YOUR_API_KEY'
--data-raw '{
"url": "https://techtalkverse.com/post/software-development/spark-basics/"
}'
| Header | Description |
|---|---|
Authorization
|
[Required] Should be Bearer access_key. See "Your API Access Key" above when you are subscribed. |
No long-term commitment. Upgrade, downgrade, or cancel anytime. Free Trial includes up to 50 requests.
To use this API, send a web page URL to the corresponding endpoints and receive the extracted content in clean or markdown format.
The Content Extract API extracts and converts web page content into clean text or markdown, facilitating web data analysis and integration.
There are different plans suits everyone including a free trial for small amount of requests, but it’s rate is limit to prevent abuse of the service.
Zyla provides a wide range of integration methods for almost all programming languages. You can use these codes to integrate with your project as you need.
The API returns detailed information about the age and history of a domain, including years, months and days since its creation, as well as expiration and update dates.
The "Extract Info" endpoint returns clean text extracted from a web page, while the "Exc Marktdown" endpoint provides the same content formatted in markdown. Both endpoints focus on delivering structured, readable content for analysis or integration.
The response data typically includes the extracted content as a single text block for the "Extract Info" endpoint and a markdown-formatted string for the "Exc Marktdown" endpoint. Additional metadata may be included based on the implementation.
The response data is structured as a JSON object, containing the extracted content as a key-value pair. For example, the key might be "content" with the corresponding clean text or markdown as the value.
The primary parameter for both endpoints is the "url" of the web page from which content is to be extracted. Users can customize their requests by providing different URLs to target specific web pages.
Each endpoint provides textual content from web pages, focusing on the main body of text while filtering out ads, menus, and other non-essential elements. This ensures users receive relevant information for their needs.
Users can integrate the returned clean text or markdown into applications for content generation, analysis, or documentation. For instance, markdown can be directly used in blogging platforms, while clean text can be analyzed for insights.
Common use cases include content generation for blogs, market research data collection, automated news summaries, technical documentation creation, and SEO analysis. Each use case leverages the API's ability to extract and format web content.
The API employs algorithms to filter out irrelevant content, ensuring that the extracted text is clean and meaningful. Continuous updates and improvements to the extraction process help maintain high data quality and relevance.
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