What is the DynamoDB equivalent of
SELECT MAX(RANGE_KEY) FROM MYTABLE WHERE PRIMARYKEY = "value"
The best I can come up with is
from boto.dynamodb2.table import Table as awsTable
tb = awsTable("MYTABLE")
rs = list(tb.query_2(PRIMARYKEY__eq="value", reverse=True, limit=1))
MAXVALUE = rs[0][RANGE_KEY]
Is there a better way to do this?
That's the correct way.
Because the records matched by the Hash Key are sorted by the Range Key, getting the first one by the descendant order will give you the record with the maximum range key.
Query results are always sorted by the range key. If the data type of
the range key is Number, the results are returned in numeric order;
otherwise, the results are returned in order of ASCII character code
values. By default, the sort order is ascending. To reverse the order
use the ScanIndexForward parameter set to false.
Query and Scan Operations - Amazon DynamoDB : http://docs.aws.amazon.com/amazondynamodb/latest/developerguide/QueryAndScan.html
NOTE: Setting the reverse parameter to true via boto API is equivalent to setting ScanIndexForward to false via the native AWS API.
If someone looking how to do it with Java:
QuerySpec querySpec = new QuerySpec();
querySpec.withKeyConditionExpression("PRIMARYKEY = :key")
.withValueMap(new ValueMap()
.withString(":key", primaryKeyValue));
querySpec.withScanIndexForward(true);
querySpec.withMaxResultSize(1);
In boto3 you can do it this way:
import boto3
from boto3.dynamodb.conditions import Key, Attr
kce = Key('table_id').eq(tableId) & Key('range').between(start, end)
output = table.query(KeyConditionExpression = kce, ScanIndexForward = False, Limit = 1)
output contains the row associated with the Max value for the range between start and end. For the Min value change the ScanIndexForward to True
Related
Is there a way to sort data and drop duplicates using pure pyarrow tables? My goal is to retrieve the latest version of each ID based on the maximum update timestamp.
Some extra details: my datasets are normally structured into at least two versions:
historical
final
The historical dataset would include all updated items from a source so it is possible to have duplicates for a single ID for each change that happened to it (picture a Zendesk or ServiceNow ticket, for example, where a ticket can be updated many times)
I then read the historical dataset using filters, convert it into a pandas DF, sort the data, and then drop duplicates on some unique constraint columns.
dataset = ds.dataset(history, filesystem, partitioning)
table = dataset.to_table(filter=filter_expression, columns=columns)
df = table.to_pandas().sort_values(sort_columns, ascending=True).drop_duplicates(unique_constraint, keep="last")
table = pa.Table.from_pandas(df=df, schema=table.schema, preserve_index=False)
# ds.write_dataset(final, filesystem, partitioning)
# I tend to write the final dataset using the legacy dataset so I can make use of the partition_filename_cb - that way I can have one file per date_id. Our visualization tool connects to these files directly
# container/dataset/date_id=20210127/20210127.parquet
pq.write_to_dataset(final, filesystem, partition_cols=["date_id"], use_legacy_dataset=True, partition_filename_cb=lambda x: str(x[-1]).split(".")[0] + ".parquet")
It would be nice to cut out that conversion to pandas and then back to a table, if possible.
Edit March 2022: PyArrow is adding more functionalities, though this one isn't here yet. My approach now would be:
def drop_duplicates(table: pa.Table, column_name: str) -> pa.Table:
unique_values = pc.unique(table[column_name])
unique_indices = [pc.index(table[column_name], value).as_py() for value in unique_values]
mask = np.full((len(table)), False)
mask[unique_indices] = True
return table.filter(mask=mask)
//end edit
I saw your question because I had a similar one, and I solved it for my work (due to IP issues I can't post the whole code but I'll try to answer as well as I can. I've never done this before)
import pyarrow.compute as pc
import pyarrow as pa
import numpy as np
array = table.column(column_name)
dicts = {dct['values']: dct['counts'] for dct in pc.value_counts(array).to_pylist()}
for key, value in dicts.items():
# do stuff
I used the 'value_counts' to find the unique values and how many of them there are (https://arrow.apache.org/docs/python/generated/pyarrow.compute.value_counts.html). Then I iterated over those values. If the value was 1, I selected the row by using
mask = pa.array(np.array(array) == key)
row = table.filter(mask)
and if the count was more then 1 I selected either the first or last one by using numpy boolean arrays as a mask again.
After iterating it was just as simple as pa.concat_tables(tables)
warning: this is a slow process. If you need something quick&dirty, try the "Unique" option (also in the same link I provided).
edit/extra:: you can make it a bit faster/less memory intensive by keeping up a numpy array of boolean masks while iterating over the dictionary. then in the end you return a "table.filter(mask=boolean_mask)".
I don't know how to calculate the speed though...
edit2:
(sorry for the many edits. I've been doing a lot of refactoring and trying to get it to work faster.)
You can also try something like:
def drop_duplicates(table: pa.Table, col_name: str) ->pa.Table:
column_array = table.column(col_name)
mask_x = np.full((table.shape[0]), False)
_, mask_indices = np.unique(np.array(column_array), return_index=True)
mask_x[mask_indices] = True
return table.filter(mask=mask_x)
The following gives a good performance. About 2mins for a table with half billion rows. The reason I don't do combine_chunks(): there is a bug, arrow seems can not combine chunk arrays if there size are too large. See details: https://issues.apache.org/jira/browse/ARROW-10172?src=confmacro
a = [len(tb3['ID'].chunk(i)) for i in range(len(tb3['ID'].chunks))]
c = np.array([np.arange(x) for x in a])
a = ([0]+a)[:-1]
c = pa.chunked_array(c+np.cumsum(a))
tb3= tb3.set_column(tb3.shape[1], 'index', c)
selector = tb3.group_by(['ID']).aggregate([("index", "min")])
tb3 = tb3.filter(pc.is_in(tb3['index'], value_set=selector['index_min']))
I found duckdb can give better performance on group by. Change the last 2 lines above into the following will give 2X speedup:
import duckdb
duck = duckdb.connect()
sql = "select first(index) as idx from tb3 group by ID"
duck_res = duck.execute(sql).fetch_arrow_table()
tb3 = tb3.filter(pc.is_in(tb3['index'], value_set=duck_res['idx']))
I'm pulling data from the NHTSA API, using a JSON format. I'm then creating a named tuple from this data and a few other sources and using this as a record to insert into a MySQL database.
The NHTSA API uses '' to designate a null value which is not an accepted value in for this particular column in database. The column only allows a float datatype.
When creating my named tuple, is there a way to substitute None if a specific value is returned? I.e. if API call returns '', use None instead?
Error returned is
Failed inserting object into MySQL table Error while executing statement: Data truncated for column 'weight' at row 1
Tuples are immutable, hence you need to create a new tuple
Here's an example:
old = (1,2,'ABC','','','','text')
new = tuple(None if x == '' else x for x in old)
Output:
Now new contains:
(1, 2, 'ABC', None, None, None, 'text')
Refer this thread for more information
To replace one specific field value in namedtuple / NamedTuple in an easier way you can use _replace() method.
Point = namedtuple('Point', 'x,y')
p = Point(x=11, y=22)
p = p._replace(x=33)
print(p)
It will print:
Point(x=33, y=22)
_replace() substitutes a field specified with keyword argument with its value, and returns a new namedtuple with that value and the rest of values copied from an old namedtuple.
I have a work order in Maximo 7.6.1.1:
The WO has LatitudeY and LongitudeX coordinates in the Service Address tab.
The WO has a custom zone field.
And there is a feature class (polygons) in a separate GIS database.
I want to do spatial query to return an attribute from the polygon record that the WO intersects and use it to populate zone in the WO.
How can I do this?
Related keyword: Maximo Spatial
To do this live in Maximo using an automation script is possible or by writing custom code into Spatial (more challenging). You want to use the /MapServer/identify tool and post the geometry xy, coordinate system, and the layer you want to query. identify window
You will have to format the geometry object correctly and test your post from the window. I usually grab the post from the network section of developer tools once I get it to work and change the output format to json and use it in my code.
You may actually not need to touch your Maximo environment at all. How about just using a trigger on your work orders table ? That trigger can then automatically fill the zone ID from a simple select statement that matches x and y with the zones in the zones table. Here is how that could look like.
This assumes that your work orders are in a table like this:
create table work_orders (
wo_id number primary key,
x number,
y number,
zone_id number
);
and the zones in a table like this
create table zones (
zone_id number primary key,
shape st_geometry
)
Then the trigger would be like this
create or replace trigger work_orders_fill_zone
before insert or update of x,y on work_orders
for each row
begin
select zone_id
into :new.zone_id
from zones
where sde.st_contains (zone_shape, sde.st_point (:new.x, :new.y, 4326) ) = 1;
end;
/
Some assumptions:
The x and y columns contain coordinates in WGS84 longitude/latitude (not in some projection or some other long/lat coordinate system)
Zones don't overlap: a work order point is always therefore in one and only one zone. If not, then the query may return multiple results, which you then need to handle.
Zones fully cover the territory your work orders can take place in. If a work order location can be outside all your zones, then you also need to handle that (the query would return no result).
The x and y columns are always filled. If they are optional, then you also need to handle that case (set zone_id to NULL if either x or y is NULL)
After that, each time a new work order is inserted in the work_orders table, the zone_id column will be automatically updated.
You can initialize zone_id in your existing work orders with a simple update:
update work_orders set x=x, y=y;
This will make the trigger run for each row in the table ... It may take some time to complete if the table is large.
Adapt the code in the Library Scripts section of Maximo 76 Scripting Features (pdf):
#What the script does:
# 1. Takes the X&Y coordinates of a work order in Maximo
# 2. Generates a URL from the coordinates
# 3. Executes the URL via a separate script/library (LIB_HTTPCLIENT)
# 4. Performs a spatial query in an ESRI REST feature service (a separate GIS system)
# 5. Returns JSON text to Maximo with the attributes of the zone that the work
# order intersected
# 6. Parses the zone number from the JSON text
# 7. Inserts the zone number into the work order record
from psdi.mbo import MboConstants
from java.util import HashMap
from com.ibm.json.java import JSONObject
field_to_update = "ZONE"
gis_field_name = "ROADS_ZONE"
def get_coords():
"""
Get the y and x coordinates(UTM projection) from the WOSERVICEADDRESS table
via the SERVICEADDRESS system relationship.
The datatype of the LatitdeY and LongitudeX fields is decimal.
"""
laty = mbo.getDouble("SERVICEADDRESS.LatitudeY")
longx = mbo.getDouble("SERVICEADDRESS.LongitudeX")
#Test values
#laty = 4444444.7001941890
#longx = 666666.0312127020
return laty, longx
def is_latlong_valid(laty, longx):
#Verify if the numbers are legitimate UTM coordinates
return (4000000 <= laty <= 5000000 and
600000 <= longx <= 700000)
def make_url(laty, longx, gis_field_name):
"""
Assembles the URL (including the longx and the laty).
Note: The coordinates are flipped in the url.
"""
url = (
"http://hostname.port"
"/arcgis/rest/services/Example"
"/Zones/MapServer/15/query?"
"geometry={0}%2C{1}&"
"geometryType=esriGeometryPoint&"
"spatialRel=esriSpatialRelIntersects&"
"outFields={2}&"
"returnGeometry=false&"
"f=pjson"
).format(longx, laty, gis_field_name)
return url
def fetch_zone(url):
# Get the JSON text from the feature service (the JSON text contains the zone value).
ctx = HashMap()
ctx.put("url", url)
service.invokeScript("LIBHTTPCLIENT", ctx)
json_text = str(ctx.get("response"))
# Parse the zone value from the JSON text
obj = JSONObject.parse(json_text)
parsed_val = obj.get("features")[0].get("attributes").get(gis_field_name)
return parsed_val
try:
laty, longx = get_coords()
if not is_latlong_valid(laty, longx):
service.log('Invalid coordinates')
else:
url = make_url(laty, longx, gis_field_name)
zone = fetch_zone(url)
#Insert the zone value into the zone field in the work order
mbo.setValue(field_to_update, zone, MboConstants.NOACCESSCHECK)
service.log(zone)
except:
#If the script fails, then set the field value to null.
mbo.setValue(field_to_update, None, MboConstants.NOACCESSCHECK)
service.log("An exception occurred")
LIBHTTPCLIENT: (a reusable Jython library script)
from psdi.iface.router import HTTPHandler
from java.util import HashMap
from java.lang import String
handler = HTTPHandler()
map = HashMap()
map.put("URL", url)
map.put("HTTPMETHOD", "GET")
responseBytes = handler.invoke(map, None)
response = String(responseBytes, "utf-8")
I'd like to update a table with Django - something like this in raw SQL:
update tbl_name set name = 'foo' where name = 'bar'
My first result is something like this - but that's nasty, isn't it?
list = ModelClass.objects.filter(name = 'bar')
for obj in list:
obj.name = 'foo'
obj.save()
Is there a more elegant way?
Update:
Django 2.2 version now has a bulk_update.
Old answer:
Refer to the following django documentation section
Updating multiple objects at once
In short you should be able to use:
ModelClass.objects.filter(name='bar').update(name="foo")
You can also use F objects to do things like incrementing rows:
from django.db.models import F
Entry.objects.all().update(n_pingbacks=F('n_pingbacks') + 1)
See the documentation.
However, note that:
This won't use ModelClass.save method (so if you have some logic inside it won't be triggered).
No django signals will be emitted.
You can't perform an .update() on a sliced QuerySet, it must be on an original QuerySet so you'll need to lean on the .filter() and .exclude() methods.
Consider using django-bulk-update found here on GitHub.
Install: pip install django-bulk-update
Implement: (code taken directly from projects ReadMe file)
from bulk_update.helper import bulk_update
random_names = ['Walter', 'The Dude', 'Donny', 'Jesus']
people = Person.objects.all()
for person in people:
r = random.randrange(4)
person.name = random_names[r]
bulk_update(people) # updates all columns using the default db
Update: As Marc points out in the comments this is not suitable for updating thousands of rows at once. Though it is suitable for smaller batches 10's to 100's. The size of the batch that is right for you depends on your CPU and query complexity. This tool is more like a wheel barrow than a dump truck.
Django 2.2 version now has a bulk_update method (release notes).
https://docs.djangoproject.com/en/stable/ref/models/querysets/#bulk-update
Example:
# get a pk: record dictionary of existing records
updates = YourModel.objects.filter(...).in_bulk()
....
# do something with the updates dict
....
if hasattr(YourModel.objects, 'bulk_update') and updates:
# Use the new method
YourModel.objects.bulk_update(updates.values(), [list the fields to update], batch_size=100)
else:
# The old & slow way
with transaction.atomic():
for obj in updates.values():
obj.save(update_fields=[list the fields to update])
If you want to set the same value on a collection of rows, you can use the update() method combined with any query term to update all rows in one query:
some_list = ModelClass.objects.filter(some condition).values('id')
ModelClass.objects.filter(pk__in=some_list).update(foo=bar)
If you want to update a collection of rows with different values depending on some condition, you can in best case batch the updates according to values. Let's say you have 1000 rows where you want to set a column to one of X values, then you could prepare the batches beforehand and then only run X update-queries (each essentially having the form of the first example above) + the initial SELECT-query.
If every row requires a unique value there is no way to avoid one query per update. Perhaps look into other architectures like CQRS/Event sourcing if you need performance in this latter case.
Here is a useful content which i found in internet regarding the above question
https://www.sankalpjonna.com/learn-django/running-a-bulk-update-with-django
The inefficient way
model_qs= ModelClass.objects.filter(name = 'bar')
for obj in model_qs:
obj.name = 'foo'
obj.save()
The efficient way
ModelClass.objects.filter(name = 'bar').update(name="foo") # for single value 'foo' or add loop
Using bulk_update
update_list = []
model_qs= ModelClass.objects.filter(name = 'bar')
for model_obj in model_qs:
model_obj.name = "foo" # Or what ever the value is for simplicty im providing foo only
update_list.append(model_obj)
ModelClass.objects.bulk_update(update_list,['name'])
Using an atomic transaction
from django.db import transaction
with transaction.atomic():
model_qs = ModelClass.objects.filter(name = 'bar')
for obj in model_qs:
ModelClass.objects.filter(name = 'bar').update(name="foo")
Any Up Votes ? Thanks in advance : Thank you for keep an attention ;)
To update with same value we can simply use this
ModelClass.objects.filter(name = 'bar').update(name='foo')
To update with different values
ob_list = ModelClass.objects.filter(name = 'bar')
obj_to_be_update = []
for obj in obj_list:
obj.name = "Dear "+obj.name
obj_to_be_update.append(obj)
ModelClass.objects.bulk_update(obj_to_be_update, ['name'], batch_size=1000)
It won't trigger save signal every time instead we keep all the objects to be updated on the list and trigger update signal at once.
IT returns number of objects are updated in table.
update_counts = ModelClass.objects.filter(name='bar').update(name="foo")
You can refer this link to get more information on bulk update and create.
Bulk update and Create
It is possible to preserve insertion order when parsing a JSON struct with a
Poco::JSON::Parser( new Poco::JSON::ParseHandler( true ) ): the non-default ParseHandler parameter preserveObjectOrder = true is handed over to the Poco::JSON::Objects so that they keep an private list of keys sorted in insertion order.
An object can then be serialized via Object::stringify() to look just like the source JSON string. Fine.
What, however, is the official way to step through a Poco::JSON::Object and access its internals in insertion order? Object::getNames() and begin()/end() use the alphabetical order of keys, not insertion order -- is there another way to access the values, or do I have to patch Poco?
As you already said:
Poco::JSON::ParseHandler goes into the Poco::JSON::Parser-constructor.
Poco::JSON::Parser::parse() creates a Poco::Dynamic::Var.
From that you'll extract a Poco::JSON::Object::Ptr.
The Poco::JSON:Object has the method "getNames". Beginning with this commit it seems to preserve the order, if it was requested via the ParseHandler. (Poco::JSON:Object::getNames 1.8.1, Poco::JSON:Object::getNames 1.9.0)
So now it should work as expected to use:
for(auto const & name : object->getNames()){
auto const & value = object->get(name); // or one of the other get-methods
// ... do things ...
}