ReadFilterung

ReadFilterung, often translated as read filtering, is a concept in information systems and data processing that describes mechanisms for reducing the amount of data retrieved from storage or a data stream by applying filters during the read operation. The goal is to lower I/O, network bandwidth, and processing costs while ensuring that the retrieved data satisfies predefined conditions or policies.

Filtering can occur at various layers. Predicate-based filters evaluate data against conditions as early as possible (predicate pushdown), and data can also be projected to a subset of columns (column pruning). Access controls and data masking can be applied at read time to prevent disclosure of sensitive fields. In streaming contexts, filters may be applied to continuous data flows or logs to limit what is forwarded downstream.

Contexts and techniques include databases, data warehouses, search engines, and various NoSQL systems. In relational databases, read filtering is commonly implemented via WHERE clauses and appropriate indexes to reduce scanned rows. Columnar stores emphasize column pruning to minimize data read from disk. In API design, read filtering can take the form of field filtering or query parameters that limit which data is returned. Implementation trade-offs often involve CPU overhead for pushed-down filters versus reduced I/O, and correctness must preserve data semantics and security.

Examples include a SQL query that selects only necessary columns and applies a WHERE condition to limit rows, a log analytics pipeline that drops non-critical events during read, and data masking that excludes or obfuscates sensitive fields for authorized users.

See also: predicate pushdown, data pruning, access control, data masking, row-level security, projection.