def _validate_query(self, cfg, query):
validator = cfg.get("query_validator")
if validator is None:
return True
filters = app.config.get("QUERY_FILTERS", {})
validator_path = filters.get(validator)
fn = plugin.load_function(validator_path)
if fn is None:
msg = "Unable to load query validator for {x}".format(x=validator)
raise exceptions.ConfigurationException(msg)
return fn(query)
def _post_filter_search_results(self, cfg, res, unpacked=False):
filters = app.config.get("QUERY_FILTERS", {})
result_filter_names = cfg.get("result_filters", [])
for result_filter_name in result_filter_names:
fn = plugin.load_function(filters.get(result_filter_name))
if fn is None:
msg = "Unable to load result filter for {x}".format(x=result_filter_name)
raise exceptions.ConfigurationException(msg)
# apply the result filter
res = fn(res, unpacked=unpacked)
return res
def _validate_query(self, cfg, query):
validator = cfg.get("query_validator")
if validator is None:
return True
filters = app.config.get("QUERY_FILTERS", {})
validator_path = filters.get(validator)
fn = plugin.load_function(validator_path)
if fn is None:
msg = "Unable to load query validator for {x}".format(x=validator)
raise exceptions.ConfigurationException(msg)
return fn(query)
def _post_filter_search_results(self, cfg, res, unpacked=False):
filters = app.config.get("QUERY_FILTERS", {})
result_filter_names = cfg.get("result_filters", [])
for result_filter_name in result_filter_names:
fn = plugin.load_function(filters.get(result_filter_name))
if fn is None:
msg = "Unable to load result filter for {x}".format(
x=result_filter_name)
raise exceptions.ConfigurationException(msg)
# apply the result filter
res = fn(res, unpacked=unpacked)
return res
def _pre_filter_search_query(self, cfg, query):
# now run the query through the filters
filters = app.config.get("QUERY_FILTERS", {})
filter_names = cfg.get("query_filters", [])
for filter_name in filter_names:
# because of back-compat, we have to do a few tricky things here...
# filter may be the name of a filter in the list of query filters
fn = plugin.load_function(filters.get(filter_name))
if fn is None:
msg = "Unable to load query filter for {x}".format(x=filter_name)
raise exceptions.ConfigurationException(msg)
# run the filter
fn(query)
return query
def _pre_filter_search_query(self, cfg, query):
# now run the query through the filters
filters = app.config.get("QUERY_FILTERS", {})
filter_names = cfg.get("query_filters", [])
for filter_name in filter_names:
# because of back-compat, we have to do a few tricky things here...
# filter may be the name of a filter in the list of query filters
fn = plugin.load_function(filters.get(filter_name))
if fn is None:
msg = "Unable to load query filter for {x}".format(
x=filter_name)
raise exceptions.ConfigurationException(msg)
# run the filter
fn(query)
return query
def do_upgrade(definition, verbose):
# get the source and target es definitions
source = definition.get("source")
target = definition.get("target")
if source is None:
source = {
"host": app.config.get("ELASTIC_SEARCH_HOST"),
"index": app.config.get("ELASTIC_SEARCH_DB")
}
if target is None:
target = {
"host": app.config.get("ELASTIC_SEARCH_HOST"),
"index": app.config.get("ELASTIC_SEARCH_DB"),
"mappings": False
}
sconn = esprit.raw.Connection(source.get("host"), source.get("index"))
tconn = esprit.raw.Connection(target.get("host"), target.get("index"))
if verbose:
print("Source", source)
print("Target", target)
# get the defined batch size
batch_size = definition.get("batch", 500)
for tdef in definition.get("types", []):
print("Upgrading", tdef.get("type"))
batch = []
total = 0
first_page = esprit.raw.search(sconn, tdef.get("type"))
max = first_page.json().get("hits", {}).get("total", 0)
type_start = datetime.now()
default_query={
"query": {"match_all": {}}
}
try:
for result in esprit.tasks.scroll(sconn, tdef.get("type"), q=tdef.get("query",default_query), keepalive=tdef.get("keepalive", "1m"), page_size=tdef.get("scroll_size", 1000), scan=True):
# learn what kind of model we've got
model_class = MODELS.get(tdef.get("type"))
original = deepcopy(result)
if tdef.get("init_with_model", True):
# instantiate an object with the data
try:
result = model_class(**result)
except DataStructureException as e:
print("Could not create model for {0}, Error: {1}".format(result['id'], str(e)))
continue
for function_path in tdef.get("functions", []):
fn = plugin.load_function(function_path)
result = fn(result)
data = result
_id = result.get("id", "id not specified")
if isinstance(result, model_class):
# run the tasks specified with this object type
tasks = tdef.get("tasks", None)
if tasks:
for func_call, kwargs in tasks.items():
getattr(result, func_call)(**kwargs)
# run the prep routine for the record
try:
result.prep()
except AttributeError:
if verbose:
print(tdef.get("type"), result.id, "has no prep method - no, pre-save preparation being done")
pass
data = result.data
_id = result.id
# add the data to the batch
data = _diff(original, data)
if "id" not in data:
data["id"] = _id
data = {"doc" : data}
batch.append(data)
if verbose:
print("added", tdef.get("type"), _id, "to batch update")
# When we have enough, do some writing
if len(batch) >= batch_size:
total += len(batch)
print(datetime.now(), "writing ", len(batch), "to", tdef.get("type"), ";", total, "of", max)
esprit.raw.bulk(tconn, batch, idkey="doc.id", type_=tdef.get("type"), bulk_type="update")
batch = []
# do some timing predictions
batch_tick = datetime.now()
time_so_far = batch_tick - type_start
seconds_so_far = time_so_far.total_seconds()
estimated_seconds_remaining = ((seconds_so_far * max) / total) - seconds_so_far
estimated_finish = batch_tick + timedelta(seconds=estimated_seconds_remaining)
print('Estimated finish time for this type {0}.'.format(estimated_finish))
except esprit.tasks.ScrollTimeoutException:
# Try to write the part-batch to index
if len(batch) > 0:
total += len(batch)
print(datetime.now(), "scroll timed out / writing ", len(batch), "to", tdef.get("type"), ";", total, "of", max)
esprit.raw.bulk(tconn, batch, idkey="doc.id", type_=tdef.get("type"), bulk_type="update")
batch = []
# Write the last part-batch to index
if len(batch) > 0:
total += len(batch)
print(datetime.now(), "final result set / writing ", len(batch), "to", tdef.get("type"), ";", total, "of", max)
esprit.raw.bulk(tconn, batch, idkey="doc.id", type_=tdef.get("type"), bulk_type="update")
def do_upgrade(definition, verbose):
# get the source and target es definitions
source = definition.get("source")
target = definition.get("target")
if source is None:
source = {
"host": app.config.get("ELASTIC_SEARCH_HOST"),
"index": app.config.get("ELASTIC_SEARCH_DB")
}
if target is None:
target = {
"host": app.config.get("ELASTIC_SEARCH_HOST"),
"index": app.config.get("ELASTIC_SEARCH_DB"),
"mappings": False
}
sconn = esprit.raw.Connection(source.get("host"), source.get("index"))
tconn = esprit.raw.Connection(target.get("host"), target.get("index"))
if verbose:
print "Source", source
print "Target", target
# get the defined batch size
batch_size = definition.get("batch", 500)
for tdef in definition.get("types", []):
print "Upgrading", tdef.get("type")
batch = []
total = 0
first_page = esprit.raw.search(sconn, tdef.get("type"))
max = first_page.json().get("hits", {}).get("total", 0)
type_start = datetime.now()
try:
for result in esprit.tasks.scroll(sconn, tdef.get("type"), keepalive=tdef.get("keepalive", "1m"), page_size=tdef.get("scroll_size", 1000), scan=True):
# learn what kind of model we've got
model_class = MODELS.get(tdef.get("type"))
original = deepcopy(result)
if tdef.get("init_with_model", True):
# instantiate an object with the data
try:
result = model_class(**result)
except DataStructureException as e:
print "Could not create model for {0}, Error: {1}".format(result['id'], e.message)
continue
for function_path in tdef.get("functions", []):
fn = plugin.load_function(function_path)
result = fn(result)
data = result
_id = result.get("id", "id not specified")
if isinstance(result, model_class):
# run the tasks specified with this object type
tasks = tdef.get("tasks", None)
if tasks:
for func_call, kwargs in tasks.iteritems():
getattr(result, func_call)(**kwargs)
# run the prep routine for the record
try:
result.prep()
except AttributeError:
if verbose:
print tdef.get("type"), result.id, "has no prep method - no, pre-save preparation being done"
pass
data = result.data
_id = result.id
# add the data to the batch
data = _diff(original, data)
if "id" not in data:
data["id"] = _id
data = {"doc" : data}
batch.append(data)
if verbose:
print "added", tdef.get("type"), _id, "to batch update"
# When we have enough, do some writing
if len(batch) >= batch_size:
total += len(batch)
print datetime.now(), "writing ", len(batch), "to", tdef.get("type"), ";", total, "of", max
esprit.raw.bulk(tconn, batch, idkey="doc.id", type_=tdef.get("type"), bulk_type="update")
batch = []
# do some timing predictions
batch_tick = datetime.now()
time_so_far = batch_tick - type_start
seconds_so_far = time_so_far.total_seconds()
estimated_seconds_remaining = ((seconds_so_far * max) / total) - seconds_so_far
estimated_finish = batch_tick + timedelta(seconds=estimated_seconds_remaining)
print 'Estimated finish time for this type {0}.'.format(estimated_finish)
except esprit.tasks.ScrollTimeoutException:
# Try to write the part-batch to index
if len(batch) > 0:
total += len(batch)
print datetime.now(), "scroll timed out / writing ", len(batch), "to", tdef.get("type"), ";", total, "of", max
esprit.raw.bulk(tconn, batch, idkey="doc.id", type_=tdef.get("type"), bulk_type="update")
batch = []
# Write the last part-batch to index
if len(batch) > 0:
total += len(batch)
print datetime.now(), "final result set / writing ", len(batch), "to", tdef.get("type"), ";", total, "of", max
esprit.raw.bulk(tconn, batch, idkey="doc.id", type_=tdef.get("type"), bulk_type="update")
