Is there a way, to read and process data (e.g. lookup) in a UDF?
I've seen only samples for StoredProcedures.
In a UDF, I get an error with the getContext() function.
No. You can only operate on what is passed in. If you can embed the lookup in the UDF code that is a possibility. If you need to get the lookup data from the collection, then a stored procedure is your best option.
Related
Basic Question: Does Google Data Studio allow calling a SQL User Defined Function (UDF) within a Custom Query data source?
My UDF works great in BigQuery. The exact same code (SQL call to a UDF) fails when creating a report to leverage this data source/code.
The following error is encountered:
Data Set Configuration Error Data Studio cannot connect to your data set.
Failed to fetch data from the underlying data set
Error ID: 53979666
+1 to the question, looking for a better solution, too.
My current walkaround is far from ideal, but perhaps could be good enough for someone:
scheduled query that executes and materialises data
datastudio on top of table created in step 1
Though you can't create a user-defined function in a Data Studio custom query, you can define a UDF in BigQuery and use it in the custom query.
Source: https://www.anvilinsights.com/video/user-defined-functions-udf-in-custom-query
What do developers commonly use as the key and value to cache the result from a SQL query into Redis? For example, if I have a Users table, and I want to cache the results from the query:
SELECT name, age FROM Users
1) Which Redis data structure should I use? Should I just have a single Key for the query and store the entire object returned by the database as the Value as such:
{ key: { object returned by database } }
Or should I use Redis' List data structure and loop through the rows individually and push them into the List as such:
{ key: [ ... ]}
Wouldn't this add computation time of O(N)? How is this more effective than just simply storing the object returned by the database?
Or should I use Redis' Hash Map data structure and loop through the rows individually and set a unique Key for each row with its corresponding attributes as such:
{ key1: {name: 'Bob', age: 25} }, { key2: {name: 'Sally', age: 15} }, ...
2) What would be a good rule of thumb with regards to the Key? From my understanding, some people just use the SQL query as the Key? But if you do so, does that mean you would have to store the entire object returned by the database as the Value (as per question 1)? Is this the best way to do it? If you are using an ORM, do you still use the SQL query as the key?
This is nicely analyzed in the Database Caching Strategies Using Redis whitepaper, by AWS.
Here the options discussed in the document. What is best is really a design decision based on tradeoffs you have to make for your specific use-case.
Cache the Database SQL ResultSet
Cache a serialized ResultSet object that contains the fetched database
row.
Pro: When data retrieval logic is abstracted (e.g., as in a Data Access Object or DAO layer), the consuming code expects only a
ResultSet object and does not need to be made aware of its
origination. A ResultSet object can be iterated over, regardless of
whether it originated from the database or was deserialized from the
cache, which greatly reduces integration logic. This pattern can be
applied to any relational database.
Con: Data retrieval still requires extracting values from the ResultSet object cursor and does not further simplify data access; it
only reduces data retrieval latency.
Cache Select Fields and Values in a Custom Format
Cache a subset of a fetched database row into a custom structure that
can be consumed by your applications.
Pro: This approach is easy to implement. You essentially store specific retrieved fields and values into a structure such as JSON or
XML and then SET that structure into a Redis string. The format you
choose should be something that conforms to your application’s data
access pattern.
Con: Your application is using different types of objects when querying for particular data (e.g., Redis string and database
results). In addition, you are required to parse through the entire
structure to retrieve the individual attributes associated with it.
Cache Select Fields and Values into an Aggregate Redis Data Structure
Cache the fetched database row into a specific data structure that can
simplify the application’s data access.
Pro: When converting the ResultSet object into a format that simplifies access, such as a Redis Hash, your application is able to
use that data more effectively. This technique simplifies your data
access pattern by reducing the need to iterate over a ResultSet object
or by parsing a structure like a JSON object stored in a string. In
addition, working with aggregate data structures, such as Redis Lists,
Sets, and Hashes provide various attribute level commands associated
with setting and getting data, eliminating the overhead associated
with processing the data before being able to leverage it.
Con: Your application is using different types of objects when querying for particular data (e.g., Redis Hash and database results).
Cache Serialized Application Object Entities
Cache a subset of a fetched database row into a custom structure that
can be consumed by your applications.
Pro: Use application objects in their native application state with simple serializing and deserializing techniques. This can
rapidly accelerate application performance by minimizing data
transformation logic.
Con: Advanced application development use case
Regarding 2)
What would be a good rule of thumb with regards to the Key?
Using the SQL query as the Key is OK for as long as you are sure it is unique. Add prefixes if there is a risk of not-uniqueness. You may have other databases with the same table names, leading to the same queries. Also make them invariant: all lower case or upper case. Redis keys are case-sensitive.
But if you do so, does that mean you would have to store the entire object returned by the database as the Value (as per question 1)?
Not necessarily, it comes down to what processing you are doing with the query. Chances are some are best stored as raw entire object for processing, some as JSON-stringified object to return quickly to the client, some as rows, etc. The best is to adapt accordingly.
Is this the best way to do it?
Not necessarily.
If you are using an ORM, do you still use the SQL query as the key?
You may if your ORM easily exposes the SQL Query programmatically, and it is consistent.
I wouldn't get fixed on the idea of using the SQL Query as key, use something you can be sure it is consistent, it will optimize your processing, and you'll have clear rules to invalidate. It could be the method call with parameters, the web API call, etc.
I have a requirement to send couple of List<> objects to the Oracle store procedure. I can do it using ODP.
But I just want to confirm that can it be possible with the System.Data.OracleClient.
System.Data.OracleClient does not accept Lists as parameters to stored procedures. In fact, it doesn't allow standard Arrays either. Really the only thing you can do is serialize the data encapsulated in the List and pass it into the stored procedure as a String (Varchar2) and then split it within the procedure. But that really depends on what sort of data your Lists contain, and how dynamic it is.
I need to write a generic code which can call any given stored procedure and handle the parameters type automatically using meta data and that's the reason I am using SimpleJdbcCall with MapSqlParameterSource.
The issue is with that the data I am getting back is a List of Maps, but the result set can be huge, I need to have access to the resultset directly or any other way in which I won't be having the entire List of Maps in memory but flushing the records to a stream as soon as the resultset is moved to next record.
The method SimpleJdbcCall.returningResultSet() allows you to register a RowMapper to handle ResultSets one row at a time.
I have a HashMap that I am serializing and deserializing to an Oracle db, in a BLOB data type field.
I want to perform a query, using this field.
Example, the application will make a new HashMap, and have some key-value pairs.
I want to query the db to see if a HashMap with this data already exists in the db.
I do not know how to do this, it seems strange if i have to go to every record in the db, deserialize it, then compare, Does SQL handle comparing BLOBs, so i could have...select * from PROCESSES where foo = ?....and foo is a BLOB type, and the ? is an instance of the new HashMap?
Thanks
Here's an article for you to read: Pounding a Nail: Old Shoe or Glass Bottle
I haven't heard much about your application's underlying architecture, but I can tell you immediately that there is never a reason why you should need to use a HashMap in this way. Its a bad technique, plain and simple.
The answer to your question is not a clever Oracle query, its a redesign of your application's architecture.
For a start, you should not serialize a HashMap to a database (more generally, you shouldn't serialize anything that you need to query against). Its much easier to create a table to represent hashmaps in your application as follows:
HashMaps
--------
MapID (pk int)
Key (pk varchar)
Value
Once you have the content of your hashmaps in your database, its trivial to query the database to see if the data already exists or produce any other kind of aggregate data:
SELECT Count(*) FROM HashMaps where MapID = ? AND Key = ?
Storing serialized objects in a database is almost always a bad idea, unless you know ahead of time that you don't need to query against them.
How are you serializing the HashMap? There are lots of ways to serialize data and an object like a HashMap. Comparing two maps, especially in serialized form, is not trivial, unless your serialization technique guarantees that two equivalent maps always serialize the same way.
One way you can get around this mess is to use XML serialization for some objects that rarely need to be queried. For example, where I work we have a log table where a certain log message is stored as an XML file in a CLOB field. This xml data represents a serialized Java object. Normally we query against other columns in the record, and only read/write the blob in single atomic steps. However once or twice it was necessary to do some deep inspection of the blob, and using XML allowed this to happen (Oracle supports querying XML in varchar2 or CLOB fields as well as native XML objects). It's a useful technique if used sparingly.
Look into dbms_crypto.hash to make a hash of your blob. Store the hash alongside the blob and it will give you something to narrow down the search to something manageable. I'm not recommending storing the hash map, but this is a general technique for searching for an exact match between blobs.
See also SQL - How do you compare a CLOB
i cannot disagree, but i'm being told to do so.
i appreciate your solution, and that's sort of what i had previously.
thanks
I haven't had the need to compare BLOBs, but it appears that it's supported through the dbms_lob package.
See dbms_lob.compare() at http://www.psoug.org/reference/dbms_lob.html
Oracle can have new data types defined with Java (or .net on windows) you could define a data type for your serialized object and define how queries work on it.
Good lack if you try this...
If you serialize your data to xml, and store the data in an xml you can then use xpaths within your sql query. (Sorry as I am more of a SqlServer person, I don’t know the details of how to do this in Oracle.)
If you EVERY need to update only part of the serialized data don’t do this.
Likewise if any of the data is pointed to by other data or points to other data don’t do this.