Time series

As explained in ScyllaDB in Action

Time-series data is data associated with specific points or intervals in time, often subject to time-based expiration.

Time-series data is associated with specific points in time and is often deleted after a defined period. In ScyllaDB, this lifecycle makes time-windowed compaction a natural fit. section 8.4.1

Time-series data influences compaction choices:

• Use TimeWindowCompactionStrategy (TWCS) when data is time-based or has a stable time to live. section 8.4.1
• TWCS sorts data into time windows, applies size-tiered compaction within each window, and compacts completed windows into separate SSTables. section 8.4.1
• Set the compaction strategy as a table property or change it with ALTER TABLE. section 8.4.1

Figure 8.18 TWCS is only used with time series data, dividing the data into windows and compacting it together when the window expires.

The figure shows how TWCS divides time-series data into windows and compacts each window when it expires. section 8.4.1

How ScyllaDB in Action covers it

How deep it goes

Explains time association, expiration, time windows, and table configuration, without detailing broader time-series modeling.

Examples

Uses expiring data windows and TWCS compaction diagrams as concrete illustrations.

What it assumes

Assumes familiarity with tables and compaction while defining time association and expiration directly.

What you can do after

Operate ScyllaDB tables by selecting TWCS and configuring compaction through table properties or ALTER TABLE.

How it connects

Connects time-series data with time-to-live, compaction strategies, SSTables, and table configuration.

liveBookmanning.com

Read ScyllaDB in Action on liveBook Get the book at manning.com

Also explained in

• 

  liveBookmanning.com
  The Well-Grounded Data AnalystA time series is a sequence of observations indexed by time, typically representing measurements collected across successive time points.
• 

  liveBookmanning.com
  Pandas Workout: 200 exercises to make you a stronger data analystA time series is a pandas data structure whose index contains timestamp values ordered or organized by time.
• 

  liveBookmanning.com
  Effective Data AnalysisA time series is a sequence of observations ordered or indexed by time points or intervals.
• 

  liveBookmanning.com
  Starting Data Analytics with Generative AI and PythonA time series is a sequence of observations indexed or ordered by time.
• 

  liveBookmanning.com
  Outlier Detection in PythonA time series is a sequence of observations indexed by or ordered according to time.
• 

  liveBookmanning.com
  MongoDB 8.0 in Action, Third EditionTime-series data is a sequence of observations whose values are associated with timestamps and analyzed through changes over time.
• 

  liveBookmanning.com
  Causal Inference for Data ScienceTime series data are observations indexed in temporal order, often collected at multiple time points.
• 

  liveBookmanning.com
  Elasticsearch in Action, Second EditionTime-series data consists of observations associated with timestamps or time intervals, usually ordered chronologically.

Compare how 9 books explain time series →

Related concepts

• data framekind of · A data frame is a two-dimensional tabular data structure with labeled rows and columns that can contain values of different types.
• Prometheusused by · Prometheus is an open-source monitoring and alerting system that collects, stores, and queries time-series metrics.
• timestamphas part · A timestamp is a value representing a specific point in time, typically associated with an event, observation, or data item.
• measurementhas part · Measurement is the systematic assessment of a property, behavior, or outcome that produces an observed value, score, or judgment.

This explainer was generated by Manning's knowledge graph from the text of ScyllaDB in Action. The section links point to the parts of the book it draws on.