Process

/ˈdʒɜrnəlɪŋ/

noun — "tracks changes to protect data integrity."

Journaling is a technique used in modern file systems and databases to maintain data integrity by recording changes in a sequential log, called a journal, before applying them to the primary storage structures. This ensures that in the event of a system crash, power failure, or software error, the system can replay or roll back incomplete operations to restore consistency. Journaling reduces the risk of corruption and speeds up recovery by avoiding full scans of the storage medium after an unexpected shutdown.

Technically, a journaling system records metadata or full data changes in a dedicated log area. File systems such as NTFS, ext3, ext4, HFS+, and XFS implement journaling to varying degrees. Metadata journaling records only changes to the file system structure, like directory updates, file creation, or allocation table modifications, while full data journaling writes both metadata and the actual file contents to the journal before committing. The journal is often circular and sequential, which optimizes write performance and ensures ordered recovery.

In workflow terms, consider creating a new file on a journaling file system. The system first writes the intended changes—allocation of blocks, directory entry, file size, timestamps—to the journal. Once these journal entries are safely committed to storage, the actual file data is written to its designated location. If a crash occurs during the write, the system can read the journal and apply any incomplete operations or discard them, preserving the file system’s consistency without manual intervention.

A simplified example illustrating journaling behavior conceptually:

// Pseudocode for metadata journaling
journal.log("Create file /docs/report.txt")
allocateBlocks("/docs/report.txt")
updateDirectory("/docs", "report.txt")
journal.commit()

Journaling can be further categorized into several modes: write-back, write-through, and ordered journaling. Write-back prioritizes speed by writing data asynchronously while metadata is committed first; write-through ensures data and metadata are both journaled before completion; ordered journaling guarantees that data blocks are written to disk in a defined order relative to the metadata updates. These strategies balance performance, reliability, and crash recovery needs depending on the workload and criticality of the data.

Conceptually, journaling is like keeping a detailed ledger of all planned changes before making physical edits to a ledger book. If an error occurs midway, the ledger can be consulted to either complete or undo the changes, ensuring no corruption or lost entries.

See FileSystem, NTFS, Transaction.

/trænˈzækʃən/

noun — "atomic unit of work in computing."

Transaction is a sequence of operations performed as a single, indivisible unit in computing or database systems. A transaction either completes entirely or has no effect at all, ensuring system consistency. It encapsulates multiple read, write, or update actions that must succeed together, maintaining data integrity even under concurrent access or system failures.

Technically, transactions are defined by the ACID properties: Atomicity, Consistency, Isolation, and Durability. Atomicity ensures all operations within the transaction are applied fully or not at all. Consistency guarantees that the system remains in a valid state after the transaction. Isolation ensures that concurrent transactions do not interfere with each other, and Durability preserves the committed changes permanently. Database management systems implement transactions through mechanisms like write-ahead logs, locks, or multi-version concurrency control (MVCC).

In workflow terms, a typical example is a bank transfer. A transaction debits Account A and credits Account B. Both actions must succeed together; otherwise, the transaction is rolled back, leaving both accounts unchanged. Similarly, in e-commerce, an order placement may update inventory, process payment, and send a confirmation email—all encapsulated within a single transaction to ensure consistency.

Transactions are also used in distributed systems. Distributed transactions coordinate multiple nodes or services to maintain consistency across a network, often using protocols like two-phase commit or consensus algorithms to guarantee ACID properties across disparate systems.

Conceptually, a transaction is like a sealed envelope containing multiple instructions: it either delivers everything inside or nothing at all, ensuring no partial execution can corrupt the system.

See ACID, Atomicity, Consistency, Isolation, Durability.

/ˈbʌfərɪŋ/

noun — "temporary storage to smooth data flow."

Buffering is the process of temporarily storing data in memory or on disk to compensate for differences in processing rates between a producer and a consumer. It ensures that data can be consumed at a steady pace even if the producer’s output or the network delivery rate fluctuates. Buffering is a critical mechanism in streaming, multimedia playback, networking, and data processing systems.

Technically, a buffer is a reserved memory region where incoming data segments are held before being processed. In video or audio streaming, incoming data packets are temporarily stored in the buffer to prevent interruptions caused by network jitter, latency, or transient bandwidth drops. Once the buffer accumulates enough data, the consumer can read sequentially without pause, maintaining smooth playback.

In networking, buffering manages the mismatch between transmission and reception speeds. For example, if a sender transmits data faster than the receiver can process, the buffer prevents immediate packet loss by holding the surplus data until the receiver is ready. Similarly, if network conditions slow down transmission, the buffer allows the receiver to continue consuming previously stored data, reducing perceived latency or glitches.

Buffering strategies vary depending on system goals. Fixed-size buffers hold a predetermined amount of data, while dynamic buffers can grow or shrink according to demand. Circular buffers are often used in real-time systems, overwriting the oldest data when full, while FIFO (first-in, first-out) buffers preserve ordering and integrity. Proper buffer sizing balances memory usage, latency, and smooth data flow.

In multimedia workflows, buffering is closely coupled with adaptive streaming. Clients monitor buffer levels to dynamically adjust playback quality or request rate. If the buffer drops below a threshold, the client may lower video resolution to prevent stalling; if the buffer is full, it can increase resolution for higher quality. This approach ensures a continuous and adaptive user experience.

Conceptually, buffering can be viewed as a shock absorber in a data pipeline. It absorbs the irregularities of production or transmission, allowing downstream consumers to operate at a consistent rate. This principle applies equally to HTTP downloads, CPU I/O operations, or hardware DMA transfers.

A typical workflow: A video streaming service delivers content over the internet. The client device receives incoming packets and stores them in a buffer. Playback begins once the buffer has sufficient data to maintain smooth rendering. During playback, the buffer is continuously refilled, compensating for fluctuations in network speed or temporary interruptions.

Buffering is essential for system reliability, smooth user experiences, and efficient data handling across varied domains. By decoupling producer and consumer speeds, it allows systems to tolerate variability in throughput without interruption.

See Streaming, HTTP, DMA.

/ˈstriːmɪŋ/

noun — "continuous delivery of data as it is consumed."

Streaming is a method of data transmission in which information is delivered and processed incrementally, allowing consumption to begin before the complete dataset has been transferred. Rather than waiting for a full file or payload to arrive, a receiving system handles incoming data in sequence as it becomes available. This model reduces startup latency and supports continuous use while transmission is still in progress.

From a systems perspective, streaming depends on dividing data into ordered segments that can be independently transported, buffered, and reassembled. A producer emits these segments sequentially, while a consumer processes them in the same order. Temporary storage, known as buffering, absorbs short-term variations in delivery rate and protects the consumer from brief interruptions. The goal is not zero delay, but predictable continuity.

Most modern streaming systems operate over standard network protocols layered on HTTP. Data is made available as a sequence of retrievable chunks, and clients request these chunks progressively. Clients measure network conditions such as throughput and latency and adapt their request strategy accordingly. This adaptive behavior allows systems to remain usable across fluctuating network environments.

Encoding and compression are central to practical streaming. Data is transformed into compact representations that reduce transmission cost while preserving functional quality. In audiovisual systems, encoded streams are decoded incrementally so playback can proceed without full reconstruction. Hardware acceleration, commonly provided by a GPU, is often used to reduce decoding latency and computational load.

Streaming extends beyond media delivery. In distributed computing, streams are used to represent ongoing sequences of events, measurements, or state changes. Consumers read from these streams in order and update internal state as new elements arrive. This approach supports real-time analytics, monitoring, and control systems where delayed batch processing would be ineffective.

Architecturally, streaming systems emphasize sustained throughput, ordering guarantees, and fault tolerance. Producers and consumers are frequently decoupled by intermediaries that manage sequencing, buffering, and retransmission. This separation allows independent scaling and recovery from transient failures without halting the overall flow of data.

A typical streaming workflow involves a source generating data continuously, such as video frames, sensor readings, or log entries. The data is segmented and transmitted as it is produced. The receiver buffers and processes each segment in order, discarding it after use. At no point is the entire dataset required to be present locally.

In user-facing applications, streaming improves responsiveness by reducing perceived wait time. Playback can begin almost immediately, live feeds can be observed as they are generated, and ongoing data can be inspected in near real time. The defining advantage is incremental availability rather than completeness.

Within computing as a whole, streaming reflects a shift from static, file-oriented data handling toward flow-oriented design. Data is treated as something that moves continuously through systems, aligning naturally with distributed architectures, real-time workloads, and modern networked environments.

See Buffering, HTTP, Video Codec.

/ˈsɜːrkɪt dɪˈzaɪn/

noun … “Planning and creating electrical circuits.”

Circuit Design is the process of defining the components, connections, and layout of an electrical or electronic circuit to achieve a specific function. It involves selecting resistors, capacitors, inductors, transistors, integrated circuits, and other elements, arranging them logically, and ensuring proper operation under desired electrical conditions. Circuit design can be analog, digital, or mixed-signal and is central to developing devices ranging from microprocessors to power systems.

Key characteristics of Circuit Design include:

• Functional specification: defining the desired behavior of the circuit.
• Component selection: choosing suitable resistors, capacitors, ICs, and other elements.
• Topology and layout: arranging components and connections efficiently and safely.
• Simulation and verification: testing circuit behavior before physical implementation.
• Optimization: improving performance, reducing cost, size, or power consumption.

Applications of Circuit Design include designing CPUs, memory modules, power supplies, analog filters, communication devices, and embedded systems.

Workflow example: Designing a simple LED circuit:

voltage_source = 5    -- volts
led = LED(forward_voltage=2)
resistor = Resistor(value=(voltage_source - led.forward_voltage)/0.02)
circuit.connect(voltage_source, led, resistor)

Here, circuit design determines the resistor value to safely operate the LED at 20 mA.

Conceptually, Circuit Design is like drawing a roadmap for electricity: it defines paths, intersections, and rules so that current flows correctly and performs the intended function.

See Resistor, Capacitor, Inductor, Transistor, Power Supply, Signal Processing.

/kəˌmjuːnɪˈkeɪʃən/

noun … “Exchange of information between entities.”

Communication in computing refers to the transfer of data or signals between systems, devices, or components to achieve coordinated operation or information sharing. It encompasses both hardware and software mechanisms, protocols, and interfaces that enable reliable, timely, and accurate data exchange. Effective communication is essential for networking, distributed systems, and embedded control applications.

Key characteristics of Communication include:

• Medium: can be wired (e.g., Ethernet, USB) or wireless (e.g., Wi-Fi, radio, Bluetooth).
• Protocol: defines rules for data formatting, synchronization, error detection, and recovery.
• Directionality: simplex, half-duplex, or full-duplex communication.
• Reliability: mechanisms like ECC or acknowledgments ensure data integrity.
• Speed and latency: bandwidth and propagation delay affect performance of communication channels.

Workflow example: Simple message exchange over TCP/IP:

client_socket = socket.connect("server_address", port)
client_socket.send("Hello, Server!")
response = client_socket.receive()
print(response)
client_socket.close()

Here, the client and server exchange data over a network using a communication protocol that guarantees delivery and order.

Conceptually, Communication is like passing a note in class: the sender encodes a message, the medium carries it, and the receiver decodes and interprets it, ideally without errors or delays.

See Radio, Error-Correcting Code, Protocol, Network, Data Transmission.

/ˈdɔːrə/

n. “The four-step handshake that gets your device an IP address.”

DORA is an acronym that describes the sequence of steps in the DHCP (Dynamic Host Configuration Protocol) process, which allows a device to obtain an IP address and other network configuration parameters automatically. The four steps are: Discover, Offer, Request, and Acknowledge.

Key steps in DORA include:

• Discover: The client broadcasts a message on the network to find available DHCP servers.
• Offer: A DHCP server responds with an available IP address and network configuration options.
• Request: The client requests the offered IP address, signaling its intent to use it.
• Acknowledge: The server confirms the lease and finalizes the assignment, allowing the client to begin using the network.

A simplified visualization of the DORA sequence:

Client <--- Discover ---> Broadcast
Server <--- Offer ---> 192.168.1.25
Client <--- Request ---> 192.168.1.25
Server <--- Acknowledge ---> 192.168.1.25 ready

Key characteristics of DORA include:

• Automatic Address Assignment: Ensures devices receive valid IP addresses without manual configuration.
• Lease-Based: Assignments are temporary and can be renewed or changed.
• Foundation of DHCP: DORA defines the core communication protocol between clients and servers.

Conceptually, DORA acts like a quick four-step handshake: the device asks for a room, the network offers one, the device confirms, and the network formally acknowledges — then the device is ready to use the network.

In essence, DORA is the essential handshake that powers DHCP, enabling seamless, automated network configuration for every device that joins an IP network.