Materialized | A Material Design theme for wordpress | Content Management System library
kandi X-RAY | Materialized Summary
kandi X-RAY | Materialized Summary
A Material Design theme for wordpress!. #It's under construction! This is an work in progress. Not even have something really working right now. I'm developping it in my free time, so if you want help me, let me know.
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QUESTION
I have an Aurora Serverless instance which has data loaded across 3 tables (mixture of standard and jsonb data types). We currently use traditional views where some of the deeply nested elements are surfaced along with other columns for aggregations and such.
We have two materialized views that we'd like to send to Redshift. Both the Aurora Postgres and Redshift are in Glue Catalog and while I can see Postgres views as a selectable table, the crawler does not pick up the materialized views.
Currently exploring two options to get the data to redshift.
- Output to parquet and use copy to load
- Point the Materialized view to jdbc sink specifying redshift.
Wanted recommendations on what might be most efficient approach if anyone has done a similar use case.
Questions:
- In option 1, would I be able to handle incremental loads?
- Is bookmarking supported for JDBC (Aurora Postgres) to JDBC (Redshift) transactions even if through Glue?
- Is there a better way (other than the options I am considering) to move the data from Aurora Postgres Serverless (10.14) to Redshift.
Thanks in advance for any guidance provided.
...ANSWER
Answered 2021-Jun-15 at 13:51Went with option 2. The Redshift Copy/Load process writes csv with manifest to S3 in any case so duplicating that is pointless.
Regarding the Questions:
N/A
Job Bookmarking does work. There is some gotchas though - ensure Connections both to RDS and Redshift are present in Glue Pyspark job, IAM self ref rules are in place and to identify a row that is unique [I chose the primary key of underlying table as an additional column in my materialized view] to use as the bookmark.
Using the primary key of core table may buy efficiencies in pruning materialized views during maintenance cycles. Just retrieve latest bookmark from cli using
aws glue get-job-bookmark --job-name yourjobname
and then just that in the where clause of the mv aswhere id >= idinbookmark
conn = glueContext.extract_jdbc_conf("yourGlueCatalogdBConnection")
connection_options_source = { "url": conn['url'] + "/yourdB", "dbtable": "table in dB", "user": conn['user'], "password": conn['password'], "jobBookmarkKeys":["unique identifier from source table"], "jobBookmarkKeysSortOrder":"asc"}
datasource0 = glueContext.create_dynamic_frame.from_options(connection_type="postgresql", connection_options=connection_options_source, transformation_ctx="datasource0")
That's all, folks
QUESTION
transform file/directory structure into 'tree' in vue json
I have an array of objects that looks like this:
...ANSWER
Answered 2021-Jun-11 at 09:55EDIT
Here is the full implementation, based upon my initial answer. I changed the forEach() into map() as it is more suitable in this case.
QUESTION
Using below code I'm attempting to use an actor as a source and send messages of type Double to be processed via a sliding window.
The sliding windows is defined as sliding(2, 2)
to calculate each sequence of twp values sent.
Sending the message:
...ANSWER
Answered 2021-Jun-14 at 11:39The short answer is that your source
is a recipe of sorts for materializing a Source
and each materialization ends up being a different source.
In your code, source.to(Sink.foreach(System.out::println)).run(system)
is one stream with the materialized actorRef
being only connected to this stream, and
QUESTION
- I would have expected the queryOutput to be materialized?
- Why is there the invalid attempt to call fieldcount when it alreadyhas the IEnumerable?
ANSWER
Answered 2021-Jun-12 at 20:53Dapper's supposed to make your life easier, and that code looks complicated.
If you use a Tuple with named fields, you can just use Dapper's auto mapping to materialize. EG:
QUESTION
When reading about CQRS it is often mentioned that the write model should not depend on any read model (assuming there is one write model and up to N read models). This makes a lot of sense, especially since read models usually only become eventually consistent with the write model. Also, we should be able to change or replace read models without breaking the write model.
However, read models might contain valuable information that is aggregated across many entities of the write model. These aggregations might even contain non-trivial business rules. One can easily imagine a business policy that evaluates a piece of information that a read model possesses, and in reaction to that changes one or many entities via the write model. But where should this policy be located/implemented? Isn't this critical business logic that tightly couples information coming from one particular read model with the write model?
When I want to implement said policy without coupling the write model to the read model, I can imagine the following strategy: Include a materialized view in the write model that gets updated synchronously whenever a relevant part of the involved entities changes (when using DDD, this could be done via domain events). However, this denormalizes the write model, and is effectively a special read model embedded in the write model itself.
I can imagine that DDD purists would say that such a policy should not exist, because it represents a business invariant/rule that encompasses multiple entities (a.k.a. aggregates). I could probably agree in theory, but in practice, I often encounter such requirements anyway.
Finally, my question is simply: How do you deal with requirements that change data in reaction to certain conditions whose evaluation requires a read model?
...ANSWER
Answered 2021-Jun-07 at 01:20First, any write model which validates commands is a read model (because at some point validating a command requires a read), albeit one that is optimized for the purpose of validating commands. So I'm not sure where you're seeing that a write model shouldn't depend on a read model.
Second, a domain event is implicitly a command to the consumers of the event: "process/consider/incorporate this event", in which case a write model processor can subscribe to the events arising from a different write model: from the perspective of the subscribing write model, these are just commands.
QUESTION
ANSWER
Answered 2021-Jun-05 at 14:21QUESTION
I encounter an error whenever I refresh my Materialized view, so I created a PIPELINE function and left join it to my Main table, the creation of Materialized view runs smoothly, but when I tried to do a refresh, then the error message appeared.
ORA-00942: table or view does not exist
Please, need help on this.
...ANSWER
Answered 2021-Jun-02 at 01:57So, It works! I just added this additional join and it works like magic.
QUESTION
I have created a database project using the target as Azure SQL Datawarehouse in Visual Studio 2019 by importing the database. When I click on build it throws me an error for Materialized views
Error: SQL71640: COUNT_BIG(a) is required when using this tool to create a materialized view that has SUM(a) in the SELECT list.
Since this is already present in the Datawarehouse it should not create an issue while creating a dacpac file and I have COUNT_BIG(*) in my script. Can you let me know if anyone has faced similar issue?
...ANSWER
Answered 2021-Jun-01 at 16:59I have faced a similar issue and this is currently a feature of VS2019 and have received the following errors:
Severity Code Description Project File Line Suppression State Error SQL71640: Cannot create a materialized view in this tool with COUNT(a). Replace it with COUNT_BIG(a). yourProjectName yourViewName.sql
Severity Code Description Project File Line Suppression State Error SQL71640: COUNT_BIG(a) is required when using this tool to create a materialized view that has SUM(a) in the SELECT list. yourProjectName yourViewName.sql
I've just updated to version 16.10.0 today and it's still an issue. The simple workaround is to as the error suggests and convert any COUNT
to COUNT_BIG
as the error suggests. The tool may get updated in the future so keep an eye out for updates.
As an alternative you could start to manage your materialized views in the post-deployment scripts (untested) but then you would lose the nice dependency features of SSDT.
If you feel strongly about it you could raise a feedback item here and get some upvotes for it:
https://feedback.azure.com/forums/307516-azure-synapse-analytics
QUESTION
I'm trying to create a mview in Oracle. It would be for a report I run everyday, so I would just need it updated before the execution, on demand.
...ANSWER
Answered 2021-Jun-01 at 00:24You should really be using the dbms_mview
package rather than the old dbms_snapshot
. They do the same thing in this case but Oracle doesn't even bother documenting the dbms_snapshot
package any longer.
The second parameter of dbms_mview.refresh
is the method
. You're specifying a method
of 'f' which means that you're asking for a fast (incremental) refresh. If you want an incremental refresh, you'd need to create a materialized view log on the remote database (something you almost certainly cannot do over the database link). Alternately, you can ask for a complete refresh instead at the cost of sending every row over the network every time
QUESTION
I have several Tables and materialized views which haven't been created with To [db]
statement and have an inner tables with these names:
ANSWER
Answered 2021-May-26 at 16:45To resolve uuid name use this query:
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