Basic queries

Overview

Teaching: 65 min
Exercises: 25 min

Questions

How can we select and download the data we want from the Gaia server?

Objectives

Compose a basic query in ADQL/SQL.

Use queries to explore a database and its tables.

Use queries to download data.

Develop, test, and debug a query incrementally.

1. Queries

This is the first in a series of lessons about working with astronomical data.

As a running example, we will replicate parts of the analysis in a recent paper, “Off the beaten path: Gaia reveals GD-1 stars outside of the main stream” by Adrian Price-Whelan and Ana Bonaca.

Outline

This lesson demonstrates the steps for selecting and downloading data from the Gaia Database:

First we’ll make a connection to the Gaia server,

We will explore information about the database and the tables it contains,

We will write a query and send it to the server, and finally

We will download the response from the server.

Query Language

In order to select data from a database, you have to compose a query, which is a program written in a “query language”. The query language we’ll use is ADQL, which stands for “Astronomical Data Query Language”.

ADQL is a dialect of SQL (Structured Query Language), which is by far the most commonly used query language. Almost everything you will learn about ADQL also works in SQL.

The reference manual for ADQL is here. But you might find it easier to learn from this ADQL Cookbook.

Using Jupyter

If you have not worked with Jupyter notebooks before, you might start with the tutorial on from Jupyter.org called “Try Classic Notebook”, or this tutorial from DataQuest.

There are two environments you can use to write and run notebooks:

“Jupyter Notebook” is the original, and
“Jupyter Lab” is a newer environment with more features.

For these lessons, you can use either one.

If you are too impatient for the tutorials, here are the most important things to know:

Notebooks are made up of code cells and text cells (and a few other less common kinds). Code cells contain code; text cells, like this one, contain explanatory text written in Markdown.
To run a code cell, click the cell to select it and press Shift-Enter. The output of the code should appear below the cell.
In general, notebooks only run correctly if you run every code cell in order from top to bottom. If you run cells out of order, you are likely to get errors.
You can modify existing cells, but then you have to run them again to see the effect.
You can add new cells, but again, you have to be careful about the order you run them in.
If you have added or modified cells and the behavior of the notebook seems strange, you can restart the “kernel”, which clears all of the variables and functions you have defined, and run the cells again from the beginning.

If you are using Jupyter notebook, open the Kernel menu and select “Restart and Run All”.
In Jupyter Lab, open the Kernel menu and select “Restart Kernel and Run All Cells”
In Colab, open the Runtime menu and select “Restart and run all”

Before you go on, you might want to explore the other menus and the toolbar to see what else you can do.

Connecting to Gaia

The library we’ll use to get Gaia data is Astroquery. Astroquery provides Gaia, which is an object that represents a connection to the Gaia database.

We can connect to the Gaia database like this:

from astroquery.gaia import Gaia

Created TAP+ (v1.2.1) - Connection:
	Host: gea.esac.esa.int
	Use HTTPS: True
	Port: 443
	SSL Port: 443
Created TAP+ (v1.2.1) - Connection:
	Host: geadata.esac.esa.int
	Use HTTPS: True
	Port: 443
	SSL Port: 443

This import statement creates a TAP+ connection; TAP stands for “Table Access Protocol”, which is a network protocol for sending queries to the database and getting back the results.

Databases and Tables

What is a database, anyway? Most generally, it can be any collection of data, but when we are talking about ADQL or SQL:

A database is a collection of one or more named tables.
Each table is a 2-D array with one or more named columns of data.

We can use Gaia.load_tables to get the names of the tables in the Gaia database. With the option only_names=True, it loads information about the tables, called “metadata”, not the data itself.

tables = Gaia.load_tables(only_names=True)

INFO: Retrieving tables... [astroquery.utils.tap.core]
INFO: Parsing tables... [astroquery.utils.tap.core]
INFO: Done. [astroquery.utils.tap.core]

The following for loop prints the names of the tables.

for table in tables:
    print(table.name)

external.apassdr9
external.gaiadr2_geometric_distance
external.gaiaedr3_distance
external.galex_ais
external.ravedr5_com
external.ravedr5_dr5
external.ravedr5_gra
external.ravedr5_on
external.sdssdr13_photoprimary
external.skymapperdr1_master
external.skymapperdr2_master
[Output truncated]

So that’s a lot of tables. The ones we’ll use are:

gaiadr2.gaia_source, which contains Gaia data from data release 2,
gaiadr2.panstarrs1_original_valid, which contains the photometry data we’ll use from PanSTARRS, and
gaiadr2.panstarrs1_best_neighbour, which we’ll use to cross-match each star observed by Gaia with the same star observed by PanSTARRS.

We can use load_table (not load_tables) to get the metadata for a single table. The name of this function is misleading, because it only downloads metadata, not the contents of the table.

meta = Gaia.load_table('gaiadr2.gaia_source')
meta

Retrieving table 'gaiadr2.gaia_source'
Parsing table 'gaiadr2.gaia_source'...
Done.

<astroquery.utils.tap.model.taptable.TapTableMeta at 0x7f50edd2aeb0>

Jupyter shows that the result is an object of type TapTableMeta, but it does not display the contents.

To see the metadata, we have to print the object.

print(meta)

TAP Table name: gaiadr2.gaiadr2.gaia_source
Description: This table has an entry for every Gaia observed source as listed in the
Main Database accumulating catalogue version from which the catalogue
release has been generated. It contains the basic source parameters,
that is only final data (no epoch data) and no spectra (neither final
nor epoch).
Num. columns: 96

Columns

The following loop prints the names of the columns in the table.

for column in meta.columns:
    print(column.name)

solution_id
designation
source_id
random_index
ref_epoch
ra
ra_error
dec
dec_error
parallax
parallax_error
[Output truncated]

You can probably infer what many of these columns are by looking at the names, but you should resist the temptation to guess. To find out what the columns mean, read the documentation.

If you want to know what can go wrong when you don’t read the documentation, you might like this article.

Exercise (2 minutes)

One of the other tables we’ll use is gaiadr2.panstarrs1_original_valid. Use load_table to get the metadata for this table. How many columns are there and what are their names?
Solution
meta2 = Gaia.load_table('gaiadr2.panstarrs1_original_valid')
print(meta2)

for column in meta2.columns:
    print(column.name)

Writing queries

By now you might be wondering how we download these tables. With tables this big, you generally don’t. Instead, you use queries to select only the data you want.

A query is a string written in a query language like SQL; for the Gaia database, the query language is a dialect of SQL called ADQL.

Here’s an example of an ADQL query.

query1 = """SELECT 
TOP 10
source_id, ra, dec, parallax 
FROM gaiadr2.gaia_source
"""

Python note

We use a triple-quoted string here so we can include line breaks in the query, which makes it easier to read.

The words in uppercase are ADQL keywords:

SELECT indicates that we are selecting data (as opposed to adding or modifying data).
TOP indicates that we only want the first 10 rows of the table, which is useful for testing a query before asking for all of the data.
FROM specifies which table we want data from.

The third line is a list of column names, indicating which columns we want.

In this example, the keywords are capitalized and the column names are lowercase. This is a common style, but it is not required. ADQL and SQL are not case-sensitive.

Also, the query is broken into multiple lines to make it more readable. This is a common style, but not required. Line breaks don’t affect the behavior of the query.

To run this query, we use the Gaia object, which represents our connection to the Gaia database, and invoke launch_job:

job = Gaia.launch_job(query1)
job

<astroquery.utils.tap.model.job.Job at 0x7f50edd2adc0>

The result is an object that represents the job running on a Gaia server.

If you print it, it displays metadata for the forthcoming results.

print(job)

<Table length=10>
   name    dtype  unit                            description                             n_bad
--------- ------- ---- ------------------------------------------------------------------ -----
source_id   int64      Unique source identifier (unique within a particular Data Release)     0
       ra float64  deg                                                    Right ascension     0
      dec float64  deg                                                        Declination     0
 parallax float64  mas                                                           Parallax     2
Jobid: None
Phase: COMPLETED
Owner: None
Output file: sync_20210315090602.xml.gz
[Output truncated]

Don’t worry about Results: None. That does not actually mean there are no results.

However, Phase: COMPLETED indicates that the job is complete, so we can get the results like this:

results = job.get_results()
type(results)

astropy.table.table.Table

The type function indicates that the result is an Astropy Table.

Repetition

Why is table repeated three times? The first is the name of the module, the second is the name of the submodule, and the third is the name of the class. Most of the time we only care about the last one. It’s like the Linnean name for gorilla, which is Gorilla gorilla gorilla.

An Astropy Table is similar to a table in an SQL database except:

SQL databases are stored on disk drives, so they are persistent; that is, they “survive” even if you turn off the computer. An Astropy Table is stored in memory; it disappears when you turn off the computer (or shut down this Jupyter notebook).
SQL databases are designed to process queries. An Astropy Table can perform some query-like operations, like selecting columns and rows. But these operations use Python syntax, not SQL.

Jupyter knows how to display the contents of a Table.

results

<Table length=10>
     source_id              ra                 dec               parallax      
                           deg                 deg                 mas         
       int64             float64             float64             float64       
------------------- ------------------ ------------------- --------------------
5887983246081387776   227.978818386372  -53.64996962450103   1.0493172163332998
5887971250213117952 228.32280834041364  -53.66270726203726  0.29455652682279093
5887991866047288704  228.1582047014091 -53.454724911639794  -0.5789179941669236
5887968673232040832 228.07420888099884   -53.8064612895961  0.41030970779603076
5887979844465854720 228.42547805195946  -53.48882284470035 -0.23379683441525864
5887978607515442688 228.23831627636855  -53.56328249482688  -0.9252161956789068
[Output truncated]

Each column has a name, units, and a data type.

For example, the units of ra and dec are degrees, and their data type is float64, which is a 64-bit floating-point number, used to store measurements with a fraction part.

This information comes from the Gaia database, and has been stored in the Astropy Table by Astroquery.

Exercise (3 minutes)

Read the documentation of this table and choose a column that looks interesting to you. Add the column name to the query and run it again. What are the units of the column you selected? What is its data type?
Solution

Let’s add radial_velocity : Radial velocity (double, Velocity[km/s] )

Spectroscopic radial velocity in the solar barycentric reference frame.

The radial velocity provided is the median value of the radial velocity measurements at all epochs.
query = """SELECT 
TOP 10
source_id, ra, dec, parallax, radial_velocity
FROM gaiadr2.gaia_source
"""

Asynchronous queries

launch_job asks the server to run the job “synchronously”, which normally means it runs immediately. But synchronous jobs are limited to 2000 rows. For queries that return more rows, you should run “asynchronously”, which mean they might take longer to get started.

If you are not sure how many rows a query will return, you can use the SQL command COUNT to find out how many rows are in the result without actually returning them. We’ll see an example in the next lesson.

The results of an asynchronous query are stored in a file on the server, so you can start a query and come back later to get the results. For anonymous users, files are kept for three days.

As an example, let’s try a query that’s similar to query1, with these changes:

It selects the first 3000 rows, so it is bigger than we should run synchronously.
It selects two additional columns, pmra and pmdec, which are proper motions along the axes of ra and dec.
It uses a new keyword, WHERE.

query2 = """SELECT 
TOP 3000
source_id, ra, dec, pmra, pmdec, parallax
FROM gaiadr2.gaia_source
WHERE parallax < 1
"""

A WHERE clause indicates which rows we want; in this case, the query selects only rows “where” parallax is less than 1. This has the effect of selecting stars with relatively low parallax, which are farther away. We’ll use this clause to exclude nearby stars that are unlikely to be part of GD-1.

WHERE is one of the most common clauses in ADQL/SQL, and one of the most useful, because it allows us to download only the rows we need from the database.

We use launch_job_async to submit an asynchronous query.

job = Gaia.launch_job_async(query2)
job

INFO: Query finished. [astroquery.utils.tap.core]

<astroquery.utils.tap.model.job.Job at 0x7f50edd40f40>

And here are the results.

results = job.get_results()
results

<Table length=10>
     source_id              ra         ...       parallax       radial_velocity
                           deg         ...         mas               km / s    
       int64             float64       ...       float64            float64    
------------------- ------------------ ... -------------------- ---------------
5895270396817359872 213.08433715252883 ...    2.041947005434917              --
5895272561481374080  213.2606587905109 ...  0.15693467895110133              --
5895247410183786368 213.38479712976664 ... -0.19017525742552605              --
5895249226912448000 213.41587389088238 ...                   --              --
5895261875598904576  213.5508930114549 ... -0.29471722363529257              --
5895258302187834624 213.87631129557286 ...   0.6468437015289753              --
[Output truncated]

You might notice that some values of parallax are negative. As this FAQ explains, “Negative parallaxes are caused by errors in the observations.” They have “no physical meaning,” but they can be a “useful diagnostic on the quality of the astrometric solution.”

Exercise (5 minutes)

The clauses in a query have to be in the right order. Go back and change the order of the clauses in query2 and run it again. The modified query should fail, but notice that you don’t get much useful debugging information.

For this reason, developing and debugging ADQL queries can be really hard. A few suggestions that might help:

Whenever possible, start with a working query, either an example you find online or a query you have used in the past.

Make small changes and test each change before you continue.

While you are debugging, use TOP to limit the number of rows in the result. That will make each test run faster, which reduces your development time.

Launching test queries synchronously might make them start faster, too.
Solution

In this example, the WHERE clause is in the wrong place.
query = """SELECT 
TOP 3000
WHERE parallax < 1
source_id, ref_epoch, ra, dec, parallax
FROM gaiadr2.gaia_source
"""

Operators

In a WHERE clause, you can use any of the SQL comparison operators; here are the most common ones:

Symbol	Operation
`>`	greater than
`<`	less than
`>=`	greater than or equal
`<=`	less than or equal
`=`	equal
`!=` or `<>`	not equal

Most of these are the same as Python, but some are not. In particular, notice that the equality operator is =, not ==. Be careful to keep your Python out of your ADQL!

You can combine comparisons using the logical operators:

AND: true if both comparisons are true
OR: true if either or both comparisons are true

Finally, you can use NOT to invert the result of a comparison.

Exercise (5 minutes)

Read about SQL operators here and then modify the previous query to select rows where bp_rp is between -0.75 and 2.
Solution

Here’s a solution using > and < operators
query = """SELECT 
TOP 10
source_id, ref_epoch, ra, dec, parallax
FROM gaiadr2.gaia_source
WHERE parallax < 1 
  AND bp_rp > -0.75 AND bp_rp < 2
"""
And here’s a solution using the BETWEEN operator
query = """SELECT 
TOP 10
source_id, ref_epoch, ra, dec, parallax
FROM gaiadr2.gaia_source
WHERE parallax < 1 
  AND bp_rp BETWEEN -0.75 AND 2
"""

bp_rp contains BP-RP color, which is the difference between two other columns, phot_bp_mean_mag and phot_rp_mean_mag. You can read about this variable here.

This Hertzsprung-Russell diagram shows the BP-RP color and luminosity of stars in the Gaia catalog (Copyright: ESA/Gaia/DPAC, CC BY-SA 3.0 IGO).

Selecting stars with bp-rp less than 2 excludes many class M dwarf stars, which are low temperature, low luminosity. A star like that at GD-1’s distance would be hard to detect, so if it is detected, it it more likely to be in the foreground.

Formatting queries

The queries we have written so far are string “literals”, meaning that the entire string is part of the program. But writing queries yourself can be slow, repetitive, and error-prone.

It is often better to write Python code that assembles a query for you. One useful tool for that is the string format method.

As an example, we’ll divide the previous query into two parts; a list of column names and a “base” for the query that contains everything except the column names.

Here’s the list of columns we’ll select.

columns = 'source_id, ra, dec, pmra, pmdec, parallax'

And here’s the base; it’s a string that contains at least one format specifier in curly brackets (braces).

query3_base = """SELECT 
TOP 10 
{columns}
FROM gaiadr2.gaia_source
WHERE parallax < 1
  AND bp_rp BETWEEN -0.75 AND 2
"""

This base query contains one format specifier, {columns}, which is a placeholder for the list of column names we will provide.

To assemble the query, we invoke format on the base string and provide a keyword argument that assigns a value to columns.

query3 = query3_base.format(columns=columns)

In this example, the variable that contains the column names and the variable in the format specifier have the same name. That’s not required, but it is a common style.

The result is a string with line breaks. If you display it, the line breaks appear as \n.

query3

'SELECT \nTOP 10 \nsource_id, ra, dec, pmra, pmdec\nFROM gaiadr2.gaia_source\nWHERE parallax < 1\n  AND bp_rp BETWEEN -0.75 AND 2\n'

But if you print it, the line breaks appear as… line breaks.

print(query3)

SELECT 
TOP 10 
source_id, ra, dec, pmra, pmdec
FROM gaiadr2.gaia_source
WHERE parallax < 1
  AND bp_rp BETWEEN -0.75 AND 2

Notice that the format specifier has been replaced with the value of columns.

Let’s run it and see if it works:

job = Gaia.launch_job(query3)
print(job)

<Table length=10>
   name    dtype    unit                              description                            
--------- ------- -------- ------------------------------------------------------------------
source_id   int64          Unique source identifier (unique within a particular Data Release)
       ra float64      deg                                                    Right ascension
      dec float64      deg                                                        Declination
     pmra float64 mas / yr                         Proper motion in right ascension direction
    pmdec float64 mas / yr                             Proper motion in declination direction
Jobid: None
Phase: COMPLETED
Owner: None
[Output truncated]

results = job.get_results()
results

<Table length=10>
     source_id              ra         ...        pmdec        
                           deg         ...       mas / yr      
       int64             float64       ...       float64       
------------------- ------------------ ... --------------------
5895272561481374080  213.2606587905109 ...   1.2299266281737415
5895261875598904576  213.5508930114549 ...   -4.672602679543312
5895247444506644992 213.33215109206796 ...   -3.538080792097856
5895259470417635968 213.78815034206346 ...  -0.8163762113468646
5895265925746051584 213.17082359534547 ...  -4.8585444120179595
5895260913525974528 213.66936020541976 ...  -1.5566420086447643
[Output truncated]

Good so far.

Exercise (10 minutes)

This query always selects sources with parallax less than 1. But suppose you want to take that upper bound as an input.

Modify query3_base to replace 1 with a format specifier like {max_parallax}. Now, when you call format, add a keyword argument that assigns a value to max_parallax, and confirm that the format specifier gets replaced with the value you provide.
Solution
query_base = """SELECT 
TOP 10
{columns}
FROM gaiadr2.gaia_source
WHERE parallax < {max_parallax} AND 
bp_rp BETWEEN -0.75 AND 2
"""

query = query_base.format(columns=columns,
                          max_parallax=0.5)
print(query)

Summary

This notebook demonstrates the following steps:

Making a connection to the Gaia server,
Exploring information about the database and the tables it contains,
Writing a query and sending it to the server, and finally
Downloading the response from the server as an Astropy Table.

In the next lesson we will extend these queries to select a particular region of the sky.

Key Points

If you can’t download an entire dataset (or it’s not practical) use queries to select the data you need.

Read the metadata and the documentation to make sure you understand the tables, their columns, and what they mean.

Develop queries incrementally: start with something simple, test it, and add a little bit at a time.

Use ADQL features like TOP and COUNT to test before you run a query that might return a lot of data.

If you know your query will return fewer than 3000 rows, you can run it synchronously, which might complete faster (but it doesn’t seem to make much difference). If it might return more than 3000 rows, you should run it asynchronously.

ADQL and SQL are not case-sensitive, so you don’t have to capitalize the keywords, but you should.

ADQL and SQL don’t require you to break a query into multiple lines, but you should.

Make each section of the notebook self-contained. Try not to use the same variable name in more than one section.

Keep notebooks short. Look for places where you can break your analysis into phases with one notebook per phase.

lesson home

Foundations of Astronomical Data Science

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Basic queries

Overview

1. Queries

Outline

Query Language

Using Jupyter

Connecting to Gaia

Databases and Tables

Columns

Exercise (2 minutes)

Solution

Writing queries

Python note

Repetition

Exercise (3 minutes)

Solution

Asynchronous queries

Exercise (5 minutes)

Solution

Operators

Exercise (5 minutes)

Solution

Formatting queries

Exercise (10 minutes)

Solution

Summary

Key Points

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