Efficient Indexing Strategies for Time-Series Workloads Quiz

Explore key concepts in efficient indexing for time-series workloads with this quiz designed to improve understanding of optimal data retrieval, indexing methods, and storage patterns. Gain insights into handling large-scale, chronological datasets while maintaining fast query performance and scalability.

1. Timestamp Order Importance

   Why is maintaining the order of timestamps crucial when indexing time-series data such as temperature readings every minute?

   1. To save disk space regardless of query type
   2. To ensure chronological queries are fast and accurate
   3. So data can be compressed using lossy compression
   4. Because random order reduces data security

   Explanation: Maintaining timestamp order supports efficient queries that retrieve data over time ranges, which are common in time-series workloads. Disk space savings do not directly result from ordering, and random order does not compromise security. Lossy compression is less relevant here, as order primarily facilitates accurate and fast time-based lookup.

2. Index Choice for Range Queries

   Which type of index is typically preferred for efficiently retrieving all records in a specific time range from time-series data?

   1. Bitmap index
   2. Hashed index
   3. Suffix tree index
   4. B-tree index

   Explanation: A B-tree index efficiently supports range queries, allowing rapid retrieval of records between two timestamps. Hashed indexes are optimized for exact matches, not ranges. Bitmap indexes work well with low-cardinality columns, and suffix tree indexes are mainly used for string pattern matching, making these less suitable for time-range lookups.

3. Index Granularity

   What is one benefit of using coarse-grained indexes, such as indexing by day instead of by minute, on time-series datasets?

   1. Reduced index size and maintenance overhead
   2. Faster updates on every new data point
   3. Increased query precision for small intervals
   4. Greater susceptibility to duplicate entries

   Explanation: Coarse-grained indexes, such as by day, use less space and are easier to maintain, though they may decrease query precision. Fine-grained indexing is better for small intervals, but can be more costly. Updates are not necessarily faster or slower, and coarse-grained indexing does not typically increase duplicates.

4. Write Patterns in Time-Series

   In time-series workloads, what pattern do data writes most commonly follow?

   1. Appends in increasing timestamp order
   2. Random insertions throughout the dataset
   3. Frequent updates to old data
   4. Mostly deletions and little new data

   Explanation: Time-series data is generally appended with new entries as time progresses, preserving temporal order. Random insertions or frequent updates are uncommon since past events aren't usually changed. Deletions do occur for retention reasons, but consistent appends are the primary pattern.

5. Impact of High Cardinality

   What challenge does high cardinality—a large number of unique time-series, such as by device ID—pose for indexing?

   1. Data can no longer be stored in a database
   2. Timestamps must be stored as strings
   3. Indexes will require lossy compression
   4. Indexes may become very large and less efficient

   Explanation: High cardinality can lead to larger index structures, slowing down lookups and increasing overhead. It does not prevent database storage or force storing timestamps as strings. While compression might occasionally be used to reduce size, lossy compression is generally not applied to indexes.

6. Retention Policy Relevance

   Why are data retention policies, such as deleting records older than one year, important in time-series indexing?

   1. They are necessary to prevent timestamp collisions
   2. They eliminate the need for index maintenance
   3. They make all queries return only the most recent day
   4. They limit the growth of indexes and keep queries efficient

   Explanation: Retention policies help control data and index growth, ensuring queries remain fast over time. They do not restrict all queries to only the last day, do not address timestamp collisions (rare in time-series), and do not remove the need for maintenance, as remaining indexes still require upkeep.

7. Downsampling for Queries

   How does downsampling, such as storing hourly averages instead of minute-by-minute data, affect indexing for time-series workloads?

   1. It reduces the volume of data and index entries
   2. It requires every data point to be indexed individually
   3. It eliminates the need for time columns
   4. It causes index fragmentation due to missing data

   Explanation: Downsampling aggregates data into larger intervals, leading to fewer data points and index entries, which can speed up queries. It does not cause fragmentation, nor does it remove the necessity for a time column. Downsampling actually reduces, not increases, the need to index every individual data point.

8. Efficiency of Composite Indexes

   When indexing by both device ID and timestamp, what is the main benefit of using a composite (multi-column) index in time-series workloads?

   1. Prevents the creation of duplicate records
   2. Forces all writes to be sequential
   3. Enables automatic data compression
   4. Allows efficient retrieval of data for a device over a range of times

   Explanation: A composite index facilitates quick lookups filtering by both device ID and time range, fitting common time-series query needs. It does not force writes to be strictly sequential, nor does it prevent duplicates at the data level. Compression is independent from indexing in this context.

9. Importance of Index Maintenance

   Why is regular index maintenance, like rebuilding or reorganizing indexes, necessary in time-series systems handling logs or sensor data?

   1. So that new data is always added to the start of the index
   2. To keep query performance high as new data is continuously added
   3. Because indexes never change when new data arrives
   4. To avoid converting timestamps into other formats

   Explanation: Over time, indexes may become fragmented or inefficient as new data is appended, so maintenance helps sustain fast queries. Saying indexes never change is incorrect—they must adapt to new data. New data is usually appended, not added to the start, and index maintenance is unrelated to timestamp format conversions.

10. Sparse vs. Dense Indexes

    In time-series indexing, what is a primary advantage of using a sparse index over a dense index for large datasets?

    1. Saves all data redundantly at each index entry
    2. Lower memory usage and faster index scans
    3. Allows duplicate time entries by default
    4. Forces data to be stored out of chronological order

    Explanation: Sparse indexes point to data blocks, not every row, reducing memory needs and improving scanning speed. They do not encourage redundant storage or duplicates. Sparse indexing does not require storing data out of order; the chronological order is usually preserved.