Far Cry 2 Storage

[Jan Dominquez]

TheStorage interface of the Web Storage API provides access to a particular domain's session or local storage. It allows, for example, the addition, modification, or deletion of stored data items.

Here we access a Storage object by calling localStorage. We first test whether the local storage contains data items using !localStorage.getItem('bgcolor'). If it does, we run a function called setStyles() that grabs the data items using Storage.getItem() and uses those values to update page styles. If it doesn't, we run another function, populateStorage(), which uses Storage.setItem() to set the item values, then runs setStyles().

Amazon Simple Storage Service (Amazon S3) is an object storage service offering industry-leading scalability, data availability, security, and performance. Millions of customers of all sizes and industries store, manage, analyze, and protect any amount of data for virtually any use case, such as data lakes, cloud-native applications, and mobile apps. With cost-effective storage classes and easy-to-use management features, you can optimize costs, organize and analyze data, and configure fine-tuned access controls to meet specific business and compliance requirements.

A data lake is a centralized repository that allows you to store all your structured and unstructured data at any scale. You can run data analytics, artificial intelligence (AI), machine learning (ML), and high-performance computing (HPC) applications to unlock the value of your data.

Meet your recovery time objective (RTO), recovery point objective (RPO), and compliance requirements with S3's robust replication functionality, data protection with AWS Backup, and various AWS Partner Network solutions.

Because Amazon S3 stores more than 350 trillion objects (exabytes of data) for virtually any use case and averages over 100 million requests per second, it may be the starting point of your generative AI journey.

Amazon S3 stores data as objects within buckets. An object is a file and any metadata that describes the file. A bucket is a container for objects. To store your data in Amazon S3, you first create a bucket and specify a bucket name and AWS Region. Then, you upload your data to that bucket as objects in Amazon S3. Each object has a key (or key name), which is the unique identifier for the object within the bucket.

S3 provides features that you can configure to support your specific use case. For example, you can use S3 Versioning to keep multiple versions of an object in the same bucket, which allows you to restore objects that are accidentally deleted or overwritten. Buckets and the objects in them are private and can only be accessed with explicitly granted access permissions. You can use bucket policies, AWS Identity and Access Management (IAM) policies, S3 Access Points, and access control lists (ACLs) to manage access.

The second section has an illustration of an empty bucket. The second section is titled "Amazon S3." The second section says, "Object storage built to store and retrieve any amount of data from anywhere."

The second section has more text under the heading "Store data." The text says, "Create bucket, specify the Region, access controls, and management options. Upload any amount of data." A nearby illustration shows a bucket that contains a square, a circle, and a triangle.

The second section also has icons that show Amazon S3 features. The features are "Control access to data," "Optimize cost with storage classes," "Replicate data to any Region," "Access from on-premises or VPC," "Protect and secure your data," and "Gain visibility into your storage."

The third section is titled "Analyze data." The third section says, "Use AWS and third-party services to analyze your data to gain insights." Nearby icons show ways of analyzing data: "artificial intelligence (AI)," "advanced analytics," and "machine learning (ML)."

Minimize your total cost of ownership (TCO) with managed services that eliminate infrastructure maintenance. Optimize your storage costs based on how frequently and quickly you need to access your data.

Safeguard your data with unmatched security and storage designed for 99.999999999 (11 9s) of durability and multi Availability Zone resilience. Keep data available to your applications with data resilience provided through options from granular restore to recovery across AWS and on-premises environments.

Choose from a variety of tools to get more from your data and accelerate new product and service delivery. Run big data analytics, artificial intelligence (AI), machine learning (ML), high-performance computing (HPC), and media processing applications on all your cloud data.

Nasdaq used Amazon S3 to build a data lake, allowing them to ingest 70 billion records per day. Nasdaq now loads financial market data five hours faster and runs Amazon Redshift queries 32 percent faster.

Find out how Nasdaq does it

Discover Financial used Amazon EFS to build its data science collaboration platform and archives long-term data to Amazon S3 Glacier. Discover cut storage costs by 50 percent and reduced access times for analytics tools from weeks to hours.

Find out how Discover does it

Equifax moved its Oracle application suite to Amazon Elastic Compute Cloud (EC2) and Amazon EBS, increasing processing performance by 300 percent. AWS gave Equifax the flexibility and scalability to conduct business globally, ensuring reliable application delivery in multiple countries across the globe.

Find out how Equifax does it

Even the first computer designs, Charles Babbage's Analytical Engine and Percy Ludgate's Analytical Machine, clearly distinguished between processing and memory (Babbage stored numbers as rotations of gears, while Ludgate stored numbers as displacements of rods in shuttles). This distinction was extended in the Von Neumann architecture, where the CPU consists of two main parts: The control unit and the arithmetic logic unit (ALU). The former controls the flow of data between the CPU and memory, while the latter performs arithmetic and logical operations on data.

A modern digital computer represents data using the binary numeral system. Text, numbers, pictures, audio, and nearly any other form of information can be converted into a string of bits, or binary digits, each of which has a value of 0 or 1. The most common unit of storage is the byte, equal to 8 bits. A piece of information can be handled by any computer or device whose storage space is large enough to accommodate the binary representation of the piece of information, or simply data. For example, the complete works of Shakespeare, about 1250 pages in print, can be stored in about five megabytes (40 million bits) with one byte per character.

Data are encoded by assigning a bit pattern to each character, digit, or multimedia object. Many standards exist for encoding (e.g. character encodings like ASCII, image encodings like JPEG, and video encodings like MPEG-4).

By adding bits to each encoded unit, redundancy allows the computer to detect errors in coded data and correct them based on mathematical algorithms. Errors generally occur in low probabilities due to random bit value flipping, or "physical bit fatigue", loss of the physical bit in the storage of its ability to maintain a distinguishable value (0 or 1), or due to errors in inter or intra-computer communication. A random bit flip (e.g. due to random radiation) is typically corrected upon detection. A bit or a group of malfunctioning physical bits (the specific defective bit is not always known; group definition depends on the specific storage device) is typically automatically fenced out, taken out of use by the device, and replaced with another functioning equivalent group in the device, where the corrected bit values are restored (if possible). The cyclic redundancy check (CRC) method is typically used in communications and storage for error detection. A detected error is then retried.

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