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Optimising BigQuery for astronomy datasets using HealPix Index

This article covers how to enhance query performance on an astronomy dataset employing clustering of the records by HEALPix index. Although this article specifically refers to astronomy data, the techniques could be useful for any user of the BigQuery GIS platform.


Introduction to HEALPix

Briefly, HEALPix is a hierarchical equal-area pixelisation scheme for the sphere. It is a widely used tool for representing and analysing data on the celestial sphere. Clustering the records by Geopositioning fields and by adding a HEALPix index field for each record in the collection of tables involved in the dataset can improve the performance of queries that involve multiple tables because HEALPIX Index keep close together on the server nodes records within the region depicted by the HEALPix Index. This way BigQuery can optimise the query response and even filter out records from other clusters that are not required for processing the query.


Image 1: HEALPix grid resolutions. Reference: https://healpix.jpl.nasa.gov/index.shtml


Key concepts

First there are some key definitions for the rest of this article:


  • Clustered tables are tables that have a user-defined column sort order. The columns defined create storage blocks for the records based on the values of these fields in the records.

  • Materialised views are precomputed views that periodically cache the results of a query for increased performance.

  • HEALPix (Hierarchical Equal Area isoLatitude Pixelation) of a sphere. It is a subdivision of the spherical surface in which each pixel covers the same surface area as every other pixel. We use this as a column to cluster tables for the astronomy datasets as the majority of the query searches are for objects and their neighbors.


Reading this article you will, learn how to:


  • Create and measure the performance of clustered tables and materialised views

  • Transfer astronomy datasets to clustered tables efficiently


Bringing astronomy datasets to BigQuery

Many astronomy datasets are publicly available in different platforms and media and in some cases you may want to consolidate these datasets into BigQuery to have a central consolidated place with all the information needed to serve astronomers in their research. Frequently, transformations may be needed to harmonise and enhance records from the source datasets, such as adding the HEALPix Index to all records with Geoposition.


There are multiple options to migrate large datasets including astronomy datasets to BigQuery. To choose the best option, there are some areas to consider:


  • Size of the datasets: astronomy datasets can be very large, in some cases multiple PBs.

  • Data structure: astronomy datasets often have complex data structures with wide-size records that contain multiple positioning and characterists attributes.

  • Data validations: astronomy datasets often have complex data validations, for example the truth match validations.

  • Combining data from different tables or multiple observatories sources: combining data from different tables or multiple observatories sources can be complex and frequently involves a large number of records to be processed.

  • Storage Performance: the target storage for these datasets must be highly scalable to handle the high volumes and optimized for analytics to handle the queries and joins against the final tables where the different pieces of data will be stored.


It is important to have a clear understanding of the data, the target storage, and the migration process before beginning the migration.


The datasets used for this article contain a large amount of data and to speed up the migration with the necessary transformations to add HEALPix into Clustered Target tables and avoid duplication of data at the target, we used Google DataFlow (ApacheBeam SDK). 


As Cloud Dataflow is a fully managed service for data processing, it provides a simple and familiar way to define data pipelines, then applies optimisation and executes the pipelines using parallel processing on the Google Compute Engine resources. Also, during the execution users get visibility into the processing jobs and resources using cloud console monitoring. 


Cloud Dataflow accepts Python and Java for programming the pipelines and for our transformation we used Python to leverage important libraries for this project such as HEALPY: the out-of-the-box library to perform HEALPix Index determination. 


How does BigQuery help?

There are several benefits of using BigQuery as a platform for astronomy datasets:


BigQuery is a highly scalable and performant data warehouse that can handle large volumes of data, such as those generated by astronomical observatories.


It’s optimised for analytics, which means that it can quickly and easily answer complex queries on astronomy catalogues.


It offers a variety of features that are useful for astronomy, such as the ability to store and analyse geospatial data with out-of-the-box geopositioning data types and operations.


The analogy between geospatial data and astronomical data is well discussed in this article, Querying the Stars with BigQuery GIS.


BigQuery is a cloud-based service, which means that it can be accessed from anywhere in the world.


It’s a cost-effective solution for storing and analysing datasets.


BigQuery offers clustered tables to speed up queries. Clustering is a good first option for improving query performance because it:


  • Accelerates queries that filter on particular columns by only scanning the blocks that match the filter.

  • Accelerates queries that filter on columns with many distinct values by providing BigQuery with detailed metadata for where to get input data.

  • Enables the table's underlying storage blocks to be adaptively sized based on the size of the table.


 It is important to create the tables clustered before loading the data.


BigQuery also offers materialised views to speed up complex and large table joins because materialised views are precomputed in the background when the base tables change. Any incremental data changes from the base tables are automatically added to the materialised views, with no user action required. Also it has smart tuning. If any part of a query against the base table can be resolved by querying the materialised view, then BigQuery reroutes the query to use the materialised view for better performance and efficiency.


Overall, BigQuery is a powerful and versatile tool that can be used to store, analyse, and share astronomy datasets. It is a good choice for astronomers who need to store and analyse large amounts of data, or who need to share their data with others.


The dataset we used

Google Maps Platform’s dataset is from Vera C. Rubin Observatory. Data Preview 0 (DP0) is the first data preview to test Legacy Survey of Space and Time (LSST) Science Pipelines and the Rubin Science Platform (RSP). DP0 will provide a limited number of astronomers and students with synthetic preview data, allowing them to prepare for science with the LSST. This dataset contains 5 tables with photometry, object details, position, and truth match records. These tables vary in size from 150 million to 800 million records (100 Gb to 300 Gb) in BigQuery. 


When they uploaded the dataset into BigQuery Clustered tables by HEALPix Id, GeoPoint (latitude, longitude), and Object Identification, they used the following optimisations:


They applied the HEALPIx NESTED index to all records. This method labels cells in a specific sequence to preserve multiple levels and we used 512 cells per pixel.


They created a GEOPOINT type object column to leverage the Geoposition features from BigQuery and reduce the calculations of Geographical operations during query processing.


They also created materialised views for preprocessing large table joins.


Query improvements and metrics

Now that Google Maps Platform has their data harmonised, loaded into the clustered tables, and the necessary materialised views created. They identified scenarios that required further optimisations on the query processing. They compared query executions in BigQuery on tables without clustering and the same query executions on tables with clusters. The data records used are the same in both tables.


Scenario 1: Clustered tables

Query Name

BIG Query non-Clustered(seconds)

BIG Query Clustered(seconds)

Single Record

Table Volume: 147 million rows (148 GB)

1.9

0.8

Magnitude Between Values

Table Volume: 147 million rows (148 GB)

2.0

1.0

Truth match join

Table1 Volume: 147 million rows (148 GB)Table2 Volume: 765 million rows(183 GB)

29.6

20

Table 1: Queries runtime database comparison by query type.


They saw a significant improvement on the performance of the BigQuery with clustered tables compared with the BigQuery without clustered tables for scenarios where the queries are executed with simple filters, range or values, or direct joins and filters.


Scenario 2: Materialised views

There are more complex scenarios involving geopositioning queries that require additional optimisation. The use of materialised views preprocess and cache large table joins which records can be clustered together, providing further optimisation.


Take for example the following query that selects an object within a circle in the sky and with limited magnitude intensity:


SELECT * FROM object AS obj JOIN truth_match AS truth ON truth.match_objectId = obj.objectId WHERE CONTAINS(POINT('ICRS', obj.ra, obj.dec), CIRCLE('ICRS', 61.863, -35.79, 0.05555555555555555))=1 AND (obj.mag_g <25 AND obj.mag_i <24)


These 2 tables are very large (147 million rows and 765 million rows) and the query optimiSer would not be able to push down filters completely to process this query.


Therefore, they created a materialised view with all required non-duplicated columns from the 2 tables and clustered the materialized view by HEALPix, object id, object geoposition from object table.


The following figure shows the query execution on the materialised view and the filters being pushed down to the database which is used for processing a subset of the data and not the totality of the records.


Image 2: Processing steps for the query using clustered materialised view.


Detailed runtime durations and consumption of BigQuery resources are described in the following image:


Image 3: Processing runtime details per step of querying materialised views.


The comparison results below shows a significant improvement results of the query with addition of clustered materialised view for processing:


Query Name

BigQuery Clustered(seconds)

BigQuery Materialised View(seconds)

Magnitude cut in a cone on JOIN of object and truth-match (same query but with Materialised View)

13

1

Table 2: Queries runtime database comparison with materialized view.


Closing

This article has discussed the benefits of migrating astronomy datasets into clustered tables in BigQuery and use cases for materialised views. It has also provided an approach on how to migrate astronomy datasets and improve query performance. This article results show that out-of-the-box BigQuery geopositioning functions, table clustering, and materialised views can significantly improve the performance of queries on large datasets. Customers around the globe may extend the application of these optimisation approaches in large datasets in different scenarios.

Get in touch today to find out how your business can benefit from technologies like these. 




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