Query Class — Query
¶
Query
¶
-
class
aerospike.
Query
¶ The Query object created by calling
aerospike.Client.query()
is used for executing queries over a secondary index of a specified set (which can be omitted orNone
). For queries, theNone
set contains those records which are not part of any named set.The Query can (optionally) be assigned one of the
predicates
(between()
orequals()
) usingwhere()
. A query without a predicate will match all the records in the given set, similar to aScan
.The query is invoked using either
foreach()
orresults()
. The bins returned can be filtered by usingselect()
.Finally, a stream UDF may be applied with
apply()
. It will aggregate results out of the records streaming back from the query.See also
Queries and Managing Queries.
-
select
(bin1[, bin2[, bin3..]])¶ Set a filter on the record bins resulting from
results()
orforeach()
. If a selected bin does not exist in a record it will not appear in the bins portion of that record tuple.
-
where
(predicate)¶ Set a where predicate for the query, without which the query will behave similar to
aerospike.Scan
. The predicate is produced by one of theaerospike.predicates
methodsequals()
andbetween()
.Parameters: predicate (tuple) – the tuple()
produced by one of theaerospike.predicates
methods.Note
Currently, you can assign at most one predicate to the query.
-
results
([,policy [, options]]) -> list of (key, meta, bins)¶ Buffer the records resulting from the query, and return them as a
list
of records.Parameters: - policy (dict) – optional Query Policies.
- options (dict) – optional Query Options.
Returns: a
list
of Record Tuple.import aerospike from aerospike import predicates as p import pprint config = { 'hosts': [ ('127.0.0.1', 3000)]} client = aerospike.client(config).connect() pp = pprint.PrettyPrinter(indent=2) query = client.query('test', 'demo') query.select('name', 'age') # matched records return with the values of these bins # assuming there is a secondary index on the 'age' bin of test.demo query.where(p.equals('age', 40)) records = query.results( {'total_timeout':2000}) pp.pprint(records) client.close()
Note
Queries require a secondary index to exist on the bin being queried.
-
foreach
(callback[, policy[, options]])¶ Invoke the callback function for each of the records streaming back from the query.
Parameters: - callback (callable) – the function to invoke for each record.
- policy (dict) – optional Query Policies.
- options (dict) – optional Query Options.
Note
A Record Tuple is passed as the argument to the callback function.
import aerospike from aerospike import predicates as p import pprint config = { 'hosts': [ ('127.0.0.1', 3000)]} client = aerospike.client(config).connect() pp = pprint.PrettyPrinter(indent=2) query = client.query('test', 'demo') query.select('name', 'age') # matched records return with the values of these bins # assuming there is a secondary index on the 'age' bin of test.demo query.where(p.between('age', 20, 30)) names = [] def matched_names((key, metadata, bins)): pp.pprint(bins) names.append(bins['name']) query.foreach(matched_names, {'total_timeout':2000}) pp.pprint(names) client.close()
Note
To stop the stream return
False
from the callback function.from __future__ import print_function import aerospike from aerospike import predicates as p config = { 'hosts': [ ('127.0.0.1',3000)]} client = aerospike.client(config).connect() def limit(lim, result): c = [0] # integers are immutable so a list (mutable) is used for the counter def key_add((key, metadata, bins)): if c[0] < lim: result.append(key) c[0] = c[0] + 1 else: return False return key_add query = client.query('test','user') query.where(p.between('age', 20, 30)) keys = [] query.foreach(limit(100, keys)) print(len(keys)) # this will be 100 if the number of matching records > 100 client.close()
-
apply
(module, function[, arguments])¶ Aggregate the
results()
using a stream UDF. If no predicate is attached to theQuery
the stream UDF will aggregate over all the records in the specified set.Parameters: - module (str) – the name of the Lua module.
- function (str) – the name of the Lua function within the module.
- arguments (list) – optional arguments to pass to the function. NOTE: these arguments must be types supported by Aerospike See: supported data types. If you need to use an unsuported type, (e.g. set or tuple) you can use a serializer like pickle first.
Returns: one of the supported types,
int
,str
,float
(double),list
,dict
(map),bytearray
(bytes).See also
Note
Assume we registered the following Lua module with the cluster as stream_udf.lua using
aerospike.Client.udf_put()
.local function having_ge_threshold(bin_having, ge_threshold) return function(rec) debug("group_count::thresh_filter: %s > %s ?", tostring(rec[bin_having]), tostring(ge_threshold)) if rec[bin_having] < ge_threshold then return false end return true end end local function count(group_by_bin) return function(group, rec) if rec[group_by_bin] then local bin_name = rec[group_by_bin] group[bin_name] = (group[bin_name] or 0) + 1 debug("group_count::count: bin %s has value %s which has the count of %s", tostring(bin_name), tostring(group[bin_name])) end return group end end local function add_values(val1, val2) return val1 + val2 end local function reduce_groups(a, b) return map.merge(a, b, add_values) end function group_count(stream, group_by_bin, bin_having, ge_threshold) if bin_having and ge_threshold then local myfilter = having_ge_threshold(bin_having, ge_threshold) return stream : filter(myfilter) : aggregate(map{}, count(group_by_bin)) : reduce(reduce_groups) else return stream : aggregate(map{}, count(group_by_bin)) : reduce(reduce_groups) end end
Find the first name distribution of users in their twenties using a query aggregation:
import aerospike from aerospike import predicates as p import pprint config = {'hosts': [('127.0.0.1', 3000)], 'lua': {'system_path':'/usr/local/aerospike/lua/', 'user_path':'/usr/local/aerospike/usr-lua/'}} client = aerospike.client(config).connect() pp = pprint.PrettyPrinter(indent=2) query = client.query('test', 'users') query.where(p.between('age', 20, 29)) query.apply('stream_udf', 'group_count', [ 'first_name' ]) names = query.results() # we expect a dict (map) whose keys are names, each with a count value pp.pprint(names) client.close()
With stream UDFs, the final reduce steps (which ties the results from the reducers of the cluster nodes) executes on the client-side. Explicitly setting the Lua
user_path
in the config helps the client find the local copy of the module containing the stream UDF. Thesystem_path
is constructed when the Python package is installed, and contains system modules such asaerospike.lua
,as.lua
, andstream_ops.lua
. The default value for the Luasystem_path
is/usr/local/aerospike/lua
.
-
predexp
(predicates)¶ Set the predicate expression filters to be used by this query.
Parameters: predicates – list A list of predicates generated by the aerospike.predexp — Query Predicate Expressions functions import aerospike from aerospike import predexp as predexp query = client.query('test', 'demo') predexps = [ predexp.rec_device_size(), predexp.integer_value(65 * 1024), predexp.integer_greater() ] query.predexp(predexps) big_records = query.results() client.close()
-
execute_background
([policy])¶ Execute a record UDF on records found by the query in the background. This method returns before the query has completed. A UDF must have been added to the query with
Query.apply()
.Parameters: policy (dict) – optional Write Policies. Returns: a job ID that can be used with aerospike.Client.job_info()
to track the status of theaerospike.JOB_QUERY
, as it runs in the background.import aerospike query = client.query('test', 'demo') query.apply('myudfs', 'myfunction', ['a', 1]) # This id can be used to monitor the progress of the background query query_id = query.execute_background()
-
Query Policies¶
-
policy
A
dict
of optional query policies which are applicable toQuery.results()
andQuery.foreach()
. See Policies.- max_retries
- An
int
. Maximum number of retries before aborting the current transaction. The initial attempt is not counted as a retry.If max_retries is exceeded, the transaction will return errorAEROSPIKE_ERR_TIMEOUT
.WARNING: Database writes that are not idempotent (such as “add”) should not be retried because the write operation may be performed multiple timesif the client timed out previous transaction attempts. It’s important to use a distinct write policy for non-idempotent writes which sets max_retries = 0;Default:0
- sleep_between_retries
- An
int
. Milliseconds to sleep between retries. Enter zero to skip sleep. Default:0
- socket_timeout
- An
int
. Socket idle timeout in milliseconds when processing a database command.If socket_timeout is not zero and the socket has been idle for at least socket_timeout, both max_retries and total_timeout are checked. If max_retries and total_timeout are not exceeded, the transaction is retried.If bothsocket_timeout
andtotal_timeout
are non-zero andsocket_timeout
>total_timeout
, thensocket_timeout
will be set tototal_timeout
. Ifsocket_timeout
is zero, there will be no socket idle limit.Default:30000
.
- total_timeout
- An
int
. Total transaction timeout in milliseconds.The total_timeout is tracked on the client and sent to the server along with the transaction in the wire protocol. The client will most likely timeout first, but the server also has the capability to timeout the transaction.Iftotal_timeout
is not zero andtotal_timeout
is reached before the transaction completes, the transaction will return errorAEROSPIKE_ERR_TIMEOUT
. Iftotal_timeout
is zero, there will be no total time limit.Default:0
- deserialize
bool
Should raw bytes representing a list or map be deserialized to a list or dictionary.Set to False for backup programs that just need access to raw bytes.Default:True
- fail_on_cluster_change
bool
Terminate query if cluster is in migration state. DefaultFalse
Query Options¶
-
options
A
dict
of optional scan options which are applicable toQuery.foreach()
andQuery.results()
.- nobins
bool
whether to return the bins portion of the Record Tuple. DefaultFalse
.
New in version 3.0.0.
- nobins