For example, to select objects for which the BLENDED flag is set in PhotoTag, you would use a query like:

SELECT top 10 objid, flags FROM PhotoTag
WHERE flags & dbo.fPhotoFlags('BLENDED') > 0

and to select only objects for which the flag is NOT set, use

SELECT top 10 objid, flags FROM PhotoTag
WHERE flags & dbo.fPhotoFlags('BLENDED') = 0 

Checking multiple flags    

To select objects for which all of several flags are set, generate the combined bitmask by adding the individual flag bitmasks, then compare the result of ANDing the combined bitmask with the flag column with the combined bitmask itself, e.g.,

SELECT top 10 objid, flags FROM PhotoTag
WHERE 
    ( flags & (dbo.fPhotoFlags('NODEBLEND')
               + dbo.fPhotoFlags('BINNED1')
	       + dbo.fPhotoFlags('BINNED2')) ) 
	= ( dbo.fPhotoFlags('NODEBLEND')
            + dbo.fPhotoFlags('BINNED1')
	    + dbo.fPhotoFlags('BINNED2') )

To select objects for which at least one of several flags is set, you just need to check that ANDing the combined bitmask with the flag column returns a non-zero result, e.g.,

SELECT top 10 objid, flags FROM PhotoTag
WHERE 
    ( flags & (dbo.fPhotoFlags('NODEBLEND')
               + dbo.fPhotoFlags('BINNED1')
               + dbo.fPhotoFlags('BINNED2')) ) > 0

To select objects for which none of several flags is set, the result of ANDing the flag column with the combined bitmask must be 0, e.g.,

SELECT top 10 objid, flags FROM PhotoTag
WHERE 
    ( flags & (dbo.fPhotoFlags('NODEBLEND')
               + dbo.fPhotoFlags('BINNED1')
               + dbo.fPhotoFlags('BINNED2')) ) = 0

As mentioned above, if you are running a query that is expected to match a large number of rows (millions), it is better to first obtain the binary bitmask resulting from the multiple flag arithmetic and using that single bitmask instead of repeated function calls to the flag functions, as described in the Using dbo functions in your query subsection of the Optimizing Queries section below.

Clean Photometry    

The SDSS photo pipeline sets a number of flags that indicate the quality of the photometry for a given object in the catalog. If you desire objects with only clean photometry for science, you should be aware that you need to filter out unwanted objects yourself in your query. This is not done automatically for you (e.g. with a view of the PhotoObjAll table). The main reason is that the flag constraints that are required for this filtering often impose a significant performance penalty on your query .

There is a single up or down flag that is available in the PhotoObjAll table (and its views) called "clean" that is set to 1 if the photometry meets our definition of good photometry, and the use of this shorthand flag is illustrated in the Clean Photometry sample query. This is meant to provide a simple way to select objects with clean photometry. However, if you do not trust this or you want to be more specific and use the individual photo flags to select objects that meet your criteria for "clean photometry", this is illustrated in two other sample queries: the Clean photometry with flags - Stars, and the Clean photometry with flags - Galaxies sample queries.

Excluding Invalid Data Values    

As mentioned in the EDR Paper, the database designates quantities that are not calculated for a particular object in a table with special values, as follows:

• The value of a quantity that has not been calculated is set to -9999.
• The value of an error that has not been calculated is set to -1000.

SELECT ra,dec,u,err_u FROM PhotoObj
WHERE 
	ra BETWEEN 180 AND 181
	AND dec BETWEEN -0.5 AND 0.5
	AND u BETWEEN -9999 AND 20.0 	-- or "u > -9999 AND u < 20.0", 
					-- instead of just "u < 20.0"
	AND err_u BETWEEN -1000 AND 0.1	-- or err_u > -1000 AND err_u < 0.1, 
					-- instead of just "err_u < 0.1"

Changing Precision of Query Output    

Use the STR(column,n,d) SQL construct (where n is the total number of digits and d is the number of decimal places) to set the precision of the column that your query requests. The SkyServer returns values with a default precision that is set for each data type, and this may not be enough for columns like ra, dec etc. See the Selected neighbors in run or the Uniform Quasar Sample sample queries for examples of how to use STR.

Joins: Querying With Multiple Tables    

You may wish to obtain quantities from multiple tables, or place constraints on quantities in one table while obtaining measurements from another. For instance, you may want magnitudes (from PhotoObj) from all objects spectroscopically identified (SpecObj) as galaxies. To perform these types of queries, you must use a join. You can join any two (or more) tables in the databases as long as they have some quantity in common (typically an object or field ID). To actually perform the join, you must have a JOIN subclause in the FROM clause of your query that specifies the common quantity to be equal in the two tables. Here is an example, getting the g magnitudes for stars in fields where the PSF fitting worked well:

SELECT TOP 10 s.psfMag_g
FROM Star s JOIN Field f ON s.fieldID = f.fieldID
WHERE
  s.psfMag_g < 20 
  AND  f.pspStatus = 2

Notice how we define abbreviations for the table names in the FROM clause; this is not necessary but makes for a lot less typing. Also, you do not have to ask for quantities to be returned from all the tables. You must specify all the tables on which you place constraints (including the join) in the FROM clause, but you can use any subset of these tables in the SELECT. If you use more than two tables, they do not all need to be joined on the same quantity. For instance, this three way join is perfectly acceptable:

SELECT TOP 10 p.objID,f.field,z.z
FROM PhotoObj p 
   JOIN Field f ON f.fieldid = p.fieldid
   JOIN photoz z ON p.objid = z.objid
WHERE
   f.psfWidth_r > 1.2
   AND  p.colc > 400.0

The type of joins shown above are called inner joins. In the above examples, we only return those objects which are matched between the multiple tables. If we want to include all rows of one of the tables, regardless of whether or not they are matched to another table, we must perform an outer join. One example is to get photometric data for all objects, while getting the spectroscopic data for those objects that have spectroscopy.

In the example below, we perform a left outer join, which means that we will get all entries (regardless of matching) from the table on the left side of the join. In the example below, the join is on P.objID = s.BestObjID; therefore, we will get all photometric (P) objects, with data from the spectroscopy if it exists. If there is no spectroscopic data for an object, we'll still get the photometric measurements but have nulls for the corresponding cpectroscopy.

select top 100 P.objID, P.ra, P.dec, S.SpecObjId, S.ra, S.dec
from PhotoObj as P left outer join SpecObjAll as S
on P.objID = s.BestObjID

When using table valued functions, you must do the join explicitly (rather than using "="). To do this, we use the syntax
SELECT quantities
FROM table1
JOIN table2 on table1.quantity = table2.quantity
WHERE constraints

For instance, in the example below, we use the function dbo.fGetNearbyObjEq to get all objects within a given radius (in this case, 1') of a specified coordinate. This is a table-valued, so it returns a table, containing the ObjIDs and distances of nearby objects. We want to get further photometric parameters on the returned objects, so we must join the output table with PhotoObj.:

SELECT G.objID, GN.distance
FROM Galaxy as G
JOIN dbo.fGetNearbyObjEq(115.,32.5, 1.0) AS GN
  ON G.objID = GN.objID 
WHERE (G.flags & dbo.fPhotoFlags('saturated')) = 0

Manipulating Query Output    

SQL provides a number of ways to reorder, group, or otherwise arrange the output of your queries. Some of these options are:

• count: Just tell me how many objects would be returned by my query. You can specify a column name as the argument to the count function or just "*" to mean all columns. It doesn't really matter (unless you are including a DISTINCT qualifier, see below) since it will count all rows that match your query anyway. Example:

  SELECT count(*) FROM Galaxy WHERE ra between 180.1 and 180.5

• distinct: Return only the unique values of the quantities requested in the SELECT statement. Example:

  SELECT distinct run FROM Field

  Here a COUNT would return different counts depending on the column you selected, e.g.

  SELECT count(distinct run) FROM Field

  would return a different number in general from:

  SELECT count(distinct field) FROM Field

• top: Return only the first n rows of the query results. We've already been using this above. Note that the rows selected in the TOP x are not deterministic, i.e., there is no order implied in the rows selected. This is true of all database queries in general. You have to explicitly enforce an order in your query if you want, and it is an expensive option in terms of query execution time usually (see ORDER BY clause below). Example:

  SELECT top 10 r FROM Star

• order by: Order the output by the specified quantities. Default is ascending order, but you can specify descending as well. You can also order by multiple columns. Example:

  SELECT top 10 u,g,r FROM Star order by g,r desc

  Note how repeatedly executing this query returns the same 100 rows. This is not true of the previous query, especially if you run it at different times so the cache does not come into play..

• group by: Group the output by the specified quantities. For instance, you could have all the stars in the output, followed by the galaxies. You could also perform operations on the grouped quantities. You could get the min and max magnitudes for stars and galaxies separately, as shown below:

  SELECT min(r),max(r) FROM PhotoPrimary group by type

  You can use this to count how many of each object type is loaded as primary photometric objects, for instance:

  SELECT count(r) FROM PhotoPrimary group by type

Optimizing Queries    

It is easy to construct very complex queries which can take a long time to execute. When writing queries, one can often rewrite them to run faster. This is called optimization.

The first, and most trivial, optimization trick is to use the minimal Table or View for your query. For instance, if all you care about are galaxies, use the Galaxy view in your FROM clause, instead of PhotoObj. We have also created a 'thin' version of PhotoObjAll, called PhotoTag. This vertical subset contains all the objects in PhotoObjAll, but only a subset of the measured quantities. Using the PhotoTag view to speed up the query only makes sense if you do NOT want parameters that are only available in the full PhotoObjAll.

It is extremely useful to think about how a database handles queries, rather than trying to write a plain, sequential list of constraints. NOT every query that is syntactically correct will necessarily be efficient; the built-in query optimizer is not perfect! Thus, writing queries such that they use the tricks below can produce significant speed improvements.

Using indices in your query    

Another simple way to make queries faster is to use indexed quantities to search on. There are two types of indices in the CAS, indices built into the database, and an external spatial index that we have added to make spatial searches much faster, called the Hierarchical Triangular Mesh.. The latter is explicitly invoked by using the built-in spatial search functions like fGetNearbyObjEq (does a radial search in equatorial coordinates), or fGetObjFromRectEq (searches in a rectangular area). Database indices are invoked automatically when you include columns in your search (in the WHERE clause) that have indices built on them. There are 3 types of database indices:

1. Primary Key (PK) indices - these are also called clustered indices because the data is physically arranged (clustered) on disk in the ascending order of this key. Each table has exactly one clustered or PK index, built on its primary search key. For most of the photometric data tables, this is the objID column, and for most of the spectroscopic data tables, this is the specObjID column.
2. Foreign Key (FK) indices - these are indices built on columns that define a relationship with other tables, e.g. objID in the SpecObjAll table is a foreign key on the PhotoObjAll table. Having FK indices helps to speed up queries that include a JOIN between the SpecObjAll table (and its associated views) and the PhotoObjAll table (and its views).
3. Covering Indices - these are indices created on (groups of) columns that are frequently used (together). Thus they "cover" the search space defined by those columns. They may be unique or non-unique (allow duplicates).

If you must search on non-indexed columns in addition to the indexed ones, you can still benefit by first performing a query using only the indexed quantities, and then select those parameters from the returned subset of objects. An indexed quantity is one where a look-up table has effectively been calculated, so that the database software does not have to do a time-consuming sequential search through all the objects in the table. For instance, sky coordinates cx,cy,cz are indexed using a Hierarchical Triangular Mesh (HTM). So, you can make a query faster by rewriting it such that it is nested; the inner query grabs the entire row for objects of interest based on the indexed quantities, while the outer query then gets the specific quantities desired.

Using dbo functions in your query    

SELECT ... 
FROM PhotoObj
WHERE
    flags & dbo.fPhotoFlags('BLENDED') > 0

In this case, it would be better to first do the pre-query:

SELECT dbo.fPhotoFlags('BLENDED')

to get the bitmask value for that flag, and then rewrite the above query as:

SELECT ... 
FROM PhotoObj
WHERE
    flags & 8 > 0

This will avoid the wastefully repeated function call for each and every photobj in the table.This is even more important when you are using multiple flags and you can reduce the comparison to a single bitmask using flag arithmetic. In the final example above in the Querying Bit Flags section, you can replace the original query:

SELECT top 10 objid, flags FROM PhotoTag
WHERE 
    ( flags & (dbo.fPhotoFlags('NODEBLEND')
               + dbo.fPhotoFlags('BINNED1')
               + dbo.fPhotoFlags('BINNED2')) ) = 0

SELECT (dbo.fPhotoFlags('NODEBLEND')
               + dbo.fPhotoFlags('BINNED1')
               + dbo.fPhotoFlags('BINNED2'))

SELECT top 10 objid, flags FROM PhotoTag
WHERE 
    ( flags & 805306432 ) = 0