Efficient Querying of Historical Data in a Database

Introduction

In this article, we will explore the process of querying historical data stored in a database table in #postgresql and compare two different approaches. We will examine the benefits and trade-offs of each method and discuss their impact on query performance. By understanding these techniques, database developers and analysts can make informed decisions to optimize their queries and improve overall efficiency.

Understanding Historical Data and Table Structure

Historical data refers to information that captures the evolution and changes of data over time. In the context of our analysis, the table "dim_projects_hist" is a #historicaltable that stores data related to projects available in "dim_projects". In addition to all the fields available in the table "dim_projects", the historical table contains three additional fields such as:

• "start_date", which represents the date when a project record was modified or became active
• "end_date", indicating the date when a modification occurred or the record became inactive
• and "delete", a boolean flag that specifies whether the record has been deleted or not.

By leveraging these columns, we can track the lifecycle of project data and retrieve the relevant information based on specific dates or periods of interest.

Approach 1: LATERAL JOIN with ROW_NUMBER()

The #LATERALJOIN is a powerful #SQL construct that enables us to perform correlated subqueries, where the subquery can reference columns from preceding tables in the query. In the context of querying historical data, the LATERAL JOIN proves beneficial: we can effectively retrieve the latest or most relevant project information based on the specified date or period. By incorporating the "ROW_NUMBER" function within the subquery, we can assign a row number to each record partitioned by project ID and start date, allowing us to extract the latest record using the "rn = 1" condition.

SELECT DISTINCT ON (phm.project_id, c.date_id)
? ? ?c.date_id
? ? , phm.*
FROM calendar AS c
? ? LEFT JOIN LATERAL
? ? ? (
? ? ? ? SELECT
? ? ? ? ? dph.project_id
? ? ? ? , dph.category
? ? ? ? , dph.project_type
? ? ? ? , dph.provider_id
? ? ? ? , dph.start_date
? ? ? ? , ROW_NUMBER() OVER (
                         PARTITION BY dph.project_id, dph.start_date::date
                         ORDER BY dph.start_date DESC
                            ) AS rn
? ? FROM dwh.dim_projects_hist AS dph
? ? WHERE 1 = 1
? ? ? ? AND dph.start_date::date <= c.date_id
? ? ? ? AND c.date_id <= COALESCE(dph.end_date::date, '2100-01-01'::date)
? ? ? ) AS phm ON phm.rn = 1
ORDER BY
? ? ? phm.project_id ASC
? ? , c.date_id DESC
? ? , phm.start_date DESC        

Approach 2: INNER JOIN with Date Comparison

In this approach, we perform a standard #INNERJOIN operation between the "calendar" table and the "dim_projects_hist" table, using a comparison between the date columns to determine the relevant records. By specifying conditions that ensure the "date_id" falls within the start and end dates of each project historical record, we can retrieve the appropriate data for the given date or period.

SELECT DISTINCT ON (dph.project_id, c.date_id)
? ? ? c.date_id
? ? , dph.project_id
? ? , dph.category
? ? , dph.project_type
? ? , dph.provider_id
FROM calendar AS c
? ? JOIN dwh.dim_projects_hist AS dph ON
                (
                     c.date_id >= dph.start_date::date
                 AND c.date_id <= COALESCE(dph.end_date::date, '2100-01-01'::date)
                )
ORDER BY
? ? ? dph.project_id ASC
? ? , c.date_id DESC
? ? , dph.start_date DESC        

This approach does not involve subqueries or the LATERAL JOIN construct, simplifying the query structure. However, it is important to note that the efficiency and accuracy of this approach depend on appropriate indexing and optimized data retrieval based on date ranges. The INNER JOIN with date comparison approach provides an effective way to query historical data and can be a suitable alternative depending on the specific use case and performance requirements.

Performance Comparison and Analysis

To assess the performance of the two approaches, we conducted a comprehensive analysis of query cost and execution time.

The results indicate that the LATERAL JOIN approach generally outperformed the INNER JOIN with date comparison approach in terms of query execution time. The LATERAL JOIN approach exhibited lower execution times across multiple runs, suggesting a higher efficiency in retrieving historical data.

However, it is important to note that the query cost for the INNER JOIN with date comparison approach was relatively lower. The disparity in query execution times can be attributed to the inherent differences in the underlying query plans generated by the database optimizer for each approach. Factors such as indexing, table statistics, and the volume of data can also influence performance.

While the LATERAL JOIN approach showed better execution times, organizations should consider the trade-offs and choose the approach that best aligns with their specific requirements, taking into account the size of the dataset and the complexity of the queries being performed.

Choosing the Right Approach

When selecting between the LATERAL JOIN and JOIN with date comparison approaches for querying historical data, several factors need to be considered.

Firstly, the size of the dataset and the complexity of the queries play a significant role. If the dataset is large and the queries involve multiple joins or complex conditions, the LATERAL JOIN approach may offer better performance due to its ability to eliminate redundant data early in the query execution. On the other hand, if the dataset is relatively smaller and the queries are straightforward, the INNER JOIN with date comparison approach can be a simpler and more efficient choice.

Additionally, it is crucial to evaluate the available database resources, indexing strategies, and query optimization techniques to ensure optimal performance. It is recommended to conduct thorough testing and benchmarking to compare the performance of both approaches under realistic workloads and select the approach that strikes the right balance between query performance, maintainability, and scalability for the specific use case at hand.

Final thoughts

In conclusion, efficient querying of historical data plays a crucial role in improving business outcomes. As demonstrated in the analysis, selecting the appropriate approach, such as the LATERAL JOIN or INNER JOIN with date comparison, can significantly impact query performance.

By optimizing queries, companies like ClimateTrade? can retrieve relevant data more efficiently, leading to better decision-making and resource allocation. This not only enhances business operations but also minimizes environmental impact by promoting effective resource utilization. Investing in query optimization aligns with ClimateTrade's commitment to ecological responsibility, as it enables our organization to streamline processes, reduce energy consumption, and make informed decisions that support a sustainable future.