Querying datasets¶
Datasets in a butler-managed data repository are identified by the combination of their dataset type and data ID within a collection.
The Registry
class’s query methods (queryDatasetTypes
, queryCollections
, queryDataIds
, and queryDatasets
) allow these to be specified either fully or partially in various ways.
DatasetType expressions¶
Arguments that specify one or more dataset types can generally take any of the following:
DatasetType
instances;str
values (corresponding toDatasetType.name
);re.Pattern
values (matched toDatasetType.name
strings, viafullmatch
);- iterables of any of the above;
- the special value “
...
”, which matches all dataset types.
Some of these are not allowed in certain contexts (as documented there).
Collection expressions¶
Arguments that specify one or more collections are similar to those for dataset types; they can take:
Collection expressions are processed by the CollectionQuery
class.
User code will rarely need to interact with these directly, but they can be passed to Registry
instead of the expression objects themselves, and hence may be useful as a way to transform an expression that may include single-pass iterators into an equivalent form that can be reused.
Ordered collection searches¶
An ordered collection expression is required in contexts where we want to search collections only until a dataset with a particular dataset type and data ID is found.
These include all direct Butler
operations, the definitions of CHAINED
collections, Registry.findDataset
, and the findFirst=True
mode of Registry.queryDatasets
.
In these contexts, regular expressions and “...
” are not allowed for collection names, because they make it impossible to unambiguously define the order in which to search.
Ordered collection searches are processed by the CollectionSearch
class.
Dimension expressions¶
Constraints on the data IDs returned by a query can take two forms:
- an explicit data ID value can be provided (as a
dict
orDataCoordinate
instance) to directly constrain the dimensions in the data ID and indirectly constrain any related dimensions (see Dimensions Overview);- a string expression resembling a SQL WHERE clause can be provided to constrain dimension values in a much more general way.
In most cases, the two can be provided together, requiring that returned data IDs match both constraints. The rest of this section describes the latter in detail.
The language grammar is defined in the exprParser.parserYacc
module, which is responsible for transforming a string with the user expression into a syntax tree with nodes represented by various classes defined in the exprParser.exprTree
module.
Modules in the exprParser
package are considered butler/registry implementation details and are not exposed at the butler package level.
The grammar is based on standard SQL; it is a subset of SQL expression language that can appear in WHERE clause of standard SELECT statement with some extensions, such as range support for the IN
operator and time literals.
Expression structure¶
The expression is passed as a string via the where
arguments of queryDataIds
and queryDatasets
.
The string contains a single boolean expression which evaluates to true or
false (if it is a valid expression). Expression can contain a bunch of
standard logical operators, comparisons, literals, and identifiers which are
references to registry objects.
A few words in expression grammar are reserved: AND
, OR
, NOT
,
IN
, and OVERLAPS
. Reserved words are not case sensitive and can appear
in either upper or lower case, or a mixture of both.
Language operator precedence rules are the same as for the other languages like C++ or Python. When in doubt use grouping operators (parentheses) for sub-expressions.
General note — the parser itself does not evaluate any expressions even if they consist of literals only, all evaluation happens in the SQL engine when registry runs the resulting SQL query.
Following sections describe each of the parts in detail.
Literals¶
The language supports these types of literals:
- Strings
- This is just a sequence of characters enclosed in single quotation marks. The parser itself fully supports Unicode, but some tools such as database drivers may have limited support for it, depending on environment or encoding chosen.
- Numbers
- Integer numbers are series of decimal numbers optionally preceded by minus sign. Parser does not support octal/hexadecimal numbers. Floating point numbers use standard notation with decimal point and/or exponent. For numbers parser passes a string representation of a number to downstream registry code to avoid possible rounding issues.
- Time literals
- Timestamps in a query are defined using special syntax which consists of
a capital letter “T” followed by quoted string:
T'time-string'
. Time string contains time information together with optional time format and time scale. For detailed description of supported time specification check section Time literals. - Range literals
- This sort of literal is allowed inside
IN
expressions only. It consists of two integer literals separated by double dots and optionally followed by a colon and one more integer literal. Two integers define start and stop values for the range; both are inclusive values. The optional third integer defines stride value, which defaults to 1; it cannot be negative. Ranges are equivalent to a sequence of integers (but not to intervals of floats).
Examples of range literals:
1..5
– equivalent to1,2,3,4,5
1..10:3
– equivalent to1,4,7,10
-10..-1:2
– equivalent to-10,-8,-6,-4,-2
Identifiers¶
Identifiers represent values external to a parser, such as values stored in a database. The parser itself cannot define identifiers or their values; it is the responsibility of translation layer (registry) to map identifiers into something sensible. Like in most programming languages, an identifier starts with a letter or underscore followed by zero or more letters, underscores, or digits. Parser also supports dotted identifiers consisting of two simple identifiers separated by a dot. Identifiers are case-sensitive on parser side but individual database back-ends may have special rules about case sensitivity.
In current implementation simple identifiers are used by registry to represent
dimensions, e.g. visit
identifier is used to represent a value of
visit
dimension in registry database. Dotted identifiers are mapped to
tables and columns in registry database, e.g. detector.raft
can be used
for accessing raft name (obviously dotted names need knowledge of database
schema and how SQL query is built). A simple identifier with a name
ingest_date
is used to reference dataset ingest time, which can be used to
filter query results based on that property of datasets.
Unary arithmetic operators¶
Two unary operators +
(plus) and -
(minus) can be used in the
expressions in front of (numeric) literals, identifiers, or other
expressions which should evaluate to a numeric value.
Binary arithmetic operators¶
Language supports five arithmetic operators: +
(add), -
(subtract),
*
(multiply), /
(divide), and %
(modulo). Usual precedence rules
apply to these operators. Operands for them can be anything that evaluates to
a numeric value.
Comparison operators¶
Language supports set of regular comparison operators: =
, !=
, <
,
<=
, >
, >=
. This can be used on operands that evaluate to a numeric
values or timestamps, for (in)equality operators operands can also be boolean
expressions.
Note
The equality comparison operator is a single =
like in SQL, not
double ==
like in Python or C++.
IN operator¶
The IN
operator (and NOT IN
) are an expanded version of a regular SQL
IN operator. Its general syntax looks like:
<expression> IN ( <item1>[, <item2>, ... ])
<expression> NOT IN ( <item1>[, <item2>, ... ])
where each item in the right hand side list is one of the supported literals or identifiers. Unlike regular SQL IN operator the list cannot contain expressions, only literals or identifiers. The extension to regular SQL IN is that literals can be range literals as defined above. The query language allows mixing of different types of literals and ranges but it may not make sense to mix them when expressions is translated to SQL.
Regular use of IN
operator is for checking whether an integer number is in
set of numbers. For that case the list on right side can be a mixture of
integer literals, identifiers that represent integers, and range literals.
For an example of this type of usage, these two expressions are equivalent:
visit IN (100, 110, 130..145:5)
visit in (100, 110, 130, 135, 140, 145)
as are these:
visit NOT IN (100, 110, 130..145:5)
visit Not In (100, 110, 130, 135, 140, 145)
Another usage of IN
operator is for checking whether a timestamp or a time
range is contained wholly in other time range. Time range in this case can be
specified as a tuple of two time literals or identifers each representing a
timestamp, or as a single identifier representing a time range. In case a
single identifier appears on the right side of IN
it has to be enclosed
in parentheses.
Here are few examples for checking containment in a time range:
-- using literals for both timestamp and time range
T'2020-01-01' IN (T'2019-01-01', '2020-01-01')
(T'2020-01-01', T'2020-02-01') NOT IN (T'2019-01-01', '2020-01-01')
-- using identifiers for each timestamp in a time range
T'2020-01-01' IN (interval.begin, interval.end)
T'2020-01-01' NOT IN (interval_id)
-- identifier on left side can represent either a timestamp or time range
timestamp_id IN (interval.begin, interval.end)
range_id NOT IN (interval_id)
The same IN
operator can be used for checking containment of a point or
region inside other region. Presently there are no special literal type for
regions, so this can only be done with regions represented by identifiers. Few
examples of region containment:
POINT(ra, dec) IN (region1)
region2 NOT IN (region1)
OVERLAPS operator¶
The OVERLAPS
operator checks for overlapping time ranges or regions, its
argument have to have consistent types. Like with IN
operator time ranges
can be represented with a tuple of two timestamps (literals or identifiers) or
with a single identifier. Regions can only be used as identifiers.
OVERLAPS
syntax is similar to IN
but it does not require parentheses
on right hand side when there is a single identifier representing a time range
or a region.
Few examples of the syntax:
(T'2020-01-01', T'2022-01-01') OVERLAPS (T'2019-01-01', '2021-01-01')
(interval.begin, interval.end) OVERLAPS interval_2
interval_1 OVERLAPS interval_2
NOT (region_1 OVERLAPS region_2)
Boolean operators¶
NOT
is the standard unary boolean negation operator.
AND
and OR
are binary logical and/or operators.
All boolean operators can work on expressions which return boolean values.
Grouping operator¶
Parentheses should be used to change evaluation order (precedence) of sub-expressions in the full expression.
Function call¶
Function call syntax is similar to other languages, expression for call
consists of an identifier followed by zero or more comma-separated arguments
enclosed in parentheses (e.g. func(1, 2, 3)
). An argument to a function
can be any expression.
Presently there only one construct that uses this syntax, POINT(ra, dec)
is function which declares (or returns) sky coordinates similarly to ADQL
syntax. Name of the POINT
function is not case-sensitive.
Time literals¶
Timestamps in a query language are specified using syntax T'time-string'
.
The content of the time-string
specifies a time point in one of the
supported time formats. For internal time representation Registry uses
astropy.time.Time class and parser converts time string into an instance
of that class. For string-based time formats such as ISO the conversion
of a time string to an object is done by the Time
constructor. The syntax
of the string could be anything that is supported by astropy
, for details
see astropy.time reference. For numeric time formats such as MJD the parser
converts string to a floating point number and passes that number to Time
constructor.
Parser guesses time format from the content of the time string:
- If time string is a floating point number then parser assumes that time is in “mjd” format.
- If string matches ISO format then parser assumes “iso” or “isot” format depending on presence of “T” separator in a string.
- If string starts with “+” sign followed by ISO string then parser assumes “fits” format.
- If string matches
year:day:time
format then “yday” is used.
The format can be specified explicitly by prefixing time string with a format
name and slash, e.g. T'mjd/58938.515'
. Any of the formats supported by
astropy
can be specified explicitly.
Time scale that parser passes to Time
constructor depends on time format,
by default parser uses:
- “utc” scale for “iso”, “isot”, “fits”, “yday”, and “unix” formats,
- “tt” scale for “cxcsec” format,
- “tai” scale for anything else.
Default scale can be overridden by adding a suffix to time string consisting
of a slash and time scale name, e.g. T'58938.515/tai'
. Any combination of
explicit time format and time scale can be given at the same time, e.g.
T'58938.515'
, T'mjd/58938.515'
, T'58938.515/tai'
, and
T'mjd/58938.515/tai'
all mean the same thing.
Note that astropy.time.Time class imposes few restrictions on the format of the string that it accepts for iso/isot/fits/yday formats, in particular:
- time zone specification is not supported
- hour-only time is not supported, at least minutes have to be specified for time (but time can be omitted entirely)
Examples¶
Few examples of valid expressions using some of the constructs:
visit > 100 AND visit < 200
visit IN (100..200) AND tract = 500
visit IN (100..200) AND visit NOT IN (159, 191) AND band = 'i'
(visit = 100 OR visit = 101) AND exposure % 2 = 1
visit.datetime_begin > T'2020-03-30 12:20:33'
exposure.datetime_begin > T'58938.515'
visit.datetime_end < T'mjd/58938.515/tai'
ingest_date < T'2020-11-06 21:10:00'