Java input output package

Feb 27, 20140 likes39 views

The java.io package contains classes for input and output in Java. It includes abstract classes like InputStream, OutputStream, Reader, and Writer as well as concrete subclasses like FileInputStream, FileOutputStream, BufferedReader, and PrintWriter. The classes use decorator patterns and handle byte streams for binary data and character streams for text. Exceptions like IOException must be caught when using these classes to ensure resources are properly closed.

java.io Package
Sujit Kumar
Zenolocity LLC
Copyright 2012-2024 @

java.io Package
• The java.io package contains a fairly large number
of classes that deal with Java input & output.
Most of the classes consist of:
• Byte streams : that are subclasses of the abstract
classes InputStream or OutputStream
• Character streams : that are subclasses of the
abstract classes Reader and Writer
• The methods for reading & writing data from
byte streams and character streams are similar.

java.io Package (contd…)
• The Reader and Writer classes read and write
16-bit Unicode characters.
• The InputStream and OutputStream classes
read and write 8-bit bytes respectively.
• Derived classes of the above 4 abstract classes
add additional responsibilities using the
Decorator pattern.

How to choose the correct
implementation (derived class)?
•
•
•
•

What is your format: text or binary ?
Do you want random access to a file ?
Are you using objects or non-objects ?
What are your sources and sinks of data (like
sockets, files, strings or array of characters) ?
• Do you need to use filtering techniques like
buffering, checksumming ?

Byte Stream Abstract Classes
InputStream — byte reader

•
•
•
•

read() a single byte or an array of bytes
skip() bytes
mark() and reset() position
close() the stream (always in finally block)

OutputStream —byte writer
• write() a single byte or an array of bytes
• flush() the stream (in case it is buffered)
• close() the stream (always in finally block)

Character Stream Abstract Classes
Reader — character reader

•
•
•
•

read() a single char or into a char array
skip() chars
mark() and reset() position
close() the stream (always in finally block)

Writer — character writer
• write() a single char or from a char array
• flush() the stream (in case it is buffered)
• close() the stream (always in finally block)

Byte Stream Concrete classes
•
•
•
•
•
•
•
•

FileInputStream, FileOutputStream
BufferedInputStream, BufferedOutputStream
ByteArrayInputStream, ByteArrayOutputStream
DataInputStream, DataOutputStream
FilterInputStream, FilterOutputStream
ObjectInputStream, ObjectOutputStream
PipedInputStream, PipedOutputStream
LineNumberInputStream

• PrintStream

Character Stream Concrete classes
•
•
•
•
•
•
•
•

FileReader, FileWriter
BufferedReader, BufferedWriter
CharArrayReader, CharArrayWriter
InputStreamReader, InputStreamWriter
FilterReader, FilterWriter
ObjectReader, ObjectWriter
PipedReader, PipedWriter
StringReader, StringWriter

• LineNumberReader
• PrintWriter

Applying the Decorator Pattern
• Example: Read each line from a file
• BufferedReader => InputStreamReader =>
FileInputStream
• Wrap an InputStreamReader over a
FileInputStream.
• Then, wrap a BufferedReader over the
InputStreamReader.

Applying the Decorator Pattern
(contd…)
• BufferedReader => Reads text from a characterinput stream, buffering characters so as to
provide for the efficient reading of characters,
arrays, and lines.
• FileInputStream => obtains input bytes from a file
in a file system.
• InputStreamReader => bridge from byte streams
to character streams. It reads bytes and decodes
them into characters using a specified charset.

Exception Handling
• The constructors and methods of I/O classes can
throw a checked exception.
• The base class for I/O exceptions is
java.io.IOException. Some methods can throw a
subclass of IOException.
For example, the constructors of FileInputStream, FileOutputStream and
FileReader can throw FileNotFoundException.

• The call to close() should always occur in a finally
block (ensures that the I/O channel is closed
properly even if an exception occurs).

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Crypto Super 500 - 14th Report - June2025.pdfStephen Perrenod

This OrionX's 14th semi-annual report on the state of the cryptocurrency mining market. The report focuses on Proof-of-Work cryptocurrencies since those use substantial supercomputer power to mint new coins and encode transactions on their blockchains. Only two make the cut this time, Bitcoin with $18 billion of annual economic value produced and Dogecoin with $1 billion. Bitcoin has now reached the Zettascale with typical hash rates of 0.9 Zettahashes per second. Bitcoin is powered by the world's largest decentralized supercomputer in a continuous winner take all lottery incentive network.

National Fuels Treatments Initiative: Building a Seamless Map of Hazardous Fu...Safe Software

The National Fuels Treatments Initiative (NFT) is transforming wildfire mitigation by creating a standardized map of nationwide fuels treatment locations across all land ownerships in the United States. While existing state and federal systems capture this data in diverse formats, NFT bridges these gaps, delivering the first truly integrated national view. This dataset will be used to measure the implementation of the National Cohesive Wildland Strategy and demonstrate the positive impact of collective investments in hazardous fuels reduction nationwide. In Phase 1, we developed an ETL pipeline template in FME Form, leveraging a schema-agnostic workflow with dynamic feature handling intended for fast roll-out and light maintenance. This was key as the initiative scaled from a few to over fifty contributors nationwide. By directly pulling from agency data stores, oftentimes ArcGIS Feature Services, NFT preserves existing structures, minimizing preparation needs. External mapping tables ensure consistent attribute and domain alignment, while robust change detection processes keep data current and actionable. Now in Phase 2, we’re migrating pipelines to FME Flow to take advantage of advanced scheduling, monitoring dashboards, and automated notifications to streamline operations. Join us to explore how this initiative exemplifies the power of technology, blending FME, ArcGIS Online, and AWS to solve a national business problem with a scalable, automated solution.

Bridging the divide: A conversation on tariffs today in the book industry - T...BookNet Canada

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PyData - Graph Theory for Multi-Agent Integrationbarqawicloud

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When projects depend on fast, reliable spatial data, every minute counts. AI Clearing needed a faster way to handle complex spatial data from drone surveys, CAD designs and 3D project models across construction sites. With FME Form, they built no-code workflows to clean, convert, integrate, and validate dozens of data formats – cutting analysis time from 5 hours to just 30 minutes. Join us, our partner Globema, and customer AI Clearing to see how they: -Automate processing of 2D, 3D, drone, spatial, and non-spatial data -Analyze construction progress 10x faster and with fewer errors -Handle diverse formats like DWG, KML, SHP, and PDF with ease -Scale their workflows for international projects in solar, roads, and pipelines If you work with complex data, join us to learn how to optimize your own processes and transform your results with FME.

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Jacobs has developed a 3D utility solids modelling workflow to improve the integration of utility data into 3D Building Information Modeling (BIM) environments. This workflow, a collaborative effort between the New Zealand Geospatial Team and the Australian Data Capture Team, employs FME to convert 2D utility data into detailed 3D representations, supporting enhanced spatial analysis and clash detection. To enable the automation of this process, Jacobs has also developed a survey data standard that standardizes the capture of existing utilities. This standard ensures consistency in data collection, forming the foundation for the subsequent automated validation and modelling steps. The workflow begins with the acquisition of utility survey data, including attributes such as location, depth, diameter, and material of utility assets like pipes and manholes. This data is validated through a custom-built tool that ensures completeness and logical consistency, including checks for proper connectivity between network components. Following validation, the data is processed using an automated modelling tool to generate 3D solids from 2D geometric representations. These solids are then integrated into BIM models to facilitate compatibility with 3D workflows and enable detailed spatial analyses. The workflow contributes to improved spatial understanding by visualizing the relationships between utilities and other infrastructure elements. The automation of validation and modeling processes ensures consistent and accurate outputs, minimizing errors and increasing workflow efficiency. This methodology highlights the application of FME in addressing challenges associated with geospatial data transformation and demonstrates its utility in enhancing data integration within BIM frameworks. By enabling accurate 3D representation of utility networks, the workflow supports improved design collaboration and decision-making in complex infrastructure projects

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Jacobs has successfully utilized FME to tackle the complexities of integrating diverse data sources in a confidential $1 billion campus improvement project. The project aimed to create a comprehensive digital twin by merging Building Information Modeling (BIM) data, Construction Operations Building Information Exchange (COBie) data, and various other data sources into a unified Geographic Information System (GIS) platform. The challenge lay in the disparate nature of these data sources, which were siloed and incompatible with each other, hindering efficient data management and decision-making processes. To address this, Jacobs leveraged FME to automate the extraction, transformation, and loading (ETL) of data between ArcGIS Indoors and IBM Maximo. This process ensured accurate transfer of maintainable asset and work order data, creating a comprehensive 2D and 3D representation of the campus for Facility Management. FME's server capabilities enabled real-time updates and synchronization between ArcGIS Indoors and Maximo, facilitating automatic updates of asset information and work orders. Additionally, Survey123 forms allowed field personnel to capture and submit data directly from their mobile devices, triggering FME workflows via webhooks for real-time data updates. This seamless integration has significantly enhanced data management, improved decision-making processes, and ensured data consistency across the project lifecycle.

Boosting MySQL with Vector Search -THE VECTOR SEARCH CONFERENCE 2025 .pdfAlkin Tezuysal

As the demand for vector databases and Generative AI continues to rise, integrating vector storage and search capabilities into traditional databases has become increasingly important. This session introduces the *MyVector Plugin*, a project that brings native vector storage and similarity search to MySQL. Unlike PostgreSQL, which offers interfaces for adding new data types and index methods, MySQL lacks such extensibility. However, by utilizing MySQL's server component plugin and UDF, the *MyVector Plugin* successfully adds a fully functional vector search feature within the existing MySQL + InnoDB infrastructure, eliminating the need for a separate vector database. The session explains the technical aspects of integrating vector support into MySQL, the challenges posed by its architecture, and real-world use cases that showcase the advantages of combining vector search with MySQL's robust features. Attendees will leave with practical insights on how to add vector search capabilities to their MySQL systems.

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AI agents are reshaping logistics and supply chain operations by enabling automation, predictive insights, and real-time decision-making across key functions such as demand forecasting, inventory management, procurement, transportation, and warehouse operations. Powered by technologies like machine learning, NLP, computer vision, and robotic process automation, these agents deliver significant benefits including cost reduction, improved efficiency, greater visibility, and enhanced adaptability to market changes. While practical use cases show measurable gains in areas like dynamic routing and real-time inventory tracking, successful implementation requires careful integration with existing systems, quality data, and strategic scaling. Despite challenges such as data integration and change management, AI agents offer a strong competitive edge, with widespread industry adoption expected by 2025.