Scaling Time-Series Workloads on Postgres
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Scaling Time-Series Workloads on Postgres

C1AdvancedSeptember 22, 202648 min
  • Interview
  • Technology

Many real-world processes produce data as a continuous stream rather than as isolated records. Sensor readings, financial markets, and application telemetry all generate data this way. This kind of time-series data has a distinctive shape. It’s written far more often than it is updated, it accumulates continuously, and it is usually queried across ranges of time. Time-series databases are built specifically for this kind of workload. TimescaleDB is an open source database from Tiger Data that adds time-series capabilities to PostgreSQL. It’s implemented as a Postgres extension, so it introduces new functionality while preserving standard Post...

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to PostgresQL.

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standard Postgres behavior and SQL.

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Many real-world processes produce data as a continuous stream rather than as isolated records. Sensor readings, financial markets, and application telemetry all generate data this way. This kind of time series data has a distinctive shape. It's written far more often than it is updated, it accumulates continuously,

0:21

and it is usually queried across ranges of time. Time series databases are built specifically for this kind of workload. TimescaleDB is an open source database from Tiger Data that adds time series capabilities to PostgresQL.

0:37

It's implemented as a Postgres extension, so it introduces new functionality while preserving standard Postgres behavior and SQL. This lets a single Postgres-based system handle both transactional and analytical workloads without splitting data across multiple tools.

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