I want to data from RDBMS to NoSQL. I created the first graph, I've found end tables. I found end nodes, I want to add to the table they belong. But I couldn't so. I must found Primary Key and Foreign Key on the tables. Only I must join tables. What can how I do in Node.js?
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I have a databricks database and I want to subset all tables under this database for a specific set of values. Is there an easy way to do it instead of querying each table separately ?
We are using SAP ABAP oracle environment.I'm trying to implement Change Data capture for the SAP BSEG table in Azure datafactory using SAP table connector. In SAP table connector, I don't see an option to pass any join conditions. Based on what fields we can capture the CDC on BSEG table.
BSEG is a cluster table.
It dates back to R2 days on Mainframes.
See Se11 BSEG --> Menu option Database Object --> Database utility.
Run Check.
It will most likely say NOT ON DATABASE.
If you want to access the data via views see one of the numerous index tables.
BSxx description Accounting: Secondary Index for xxxxx
These so called Index tables are separate tables that behave like indexes
on bseg but arent true indexes as cluster tables can not have indexes.
The index tables are real tables you can access with joins/views.
The document number can be used read BSEG later should that still be necessary.
You may find FI_DOCUMENT_READ and BKPF useful too.
In theory the Index tables should be enough.
From the SAP Table connector help:
Currently SAP Table connector only supports one single table with the default function module. To get the joined data of multiple tables, you can leverage the customRfcReadTableFunctionModule property in the SAP Table connector following steps below
...
So no, table joins are not supported by default, you need to write in SAP backend a custom FM with the predefined interface. The interface to do is described in the help.
If you use Azure Data factory to Azure Data Explorer doing big tables like BSEG can be done with a work around.
Although BSEG is a cluster of tables in SAP, from the SAP Connector point of view it is a table with rows and columns which can be partitioned.
Here is an example for MSEG which is similar.
MSEG_Partitioned
Kind Regards
Gauchet
I have some tables in scheme of PostgreSQL. I need to find text in some tables at the same time. I need find the same phrases in different tables and also different phrases in different tables. In the end I need to join this tables and give id from the main table. Which solution is the best? P.S. Tables will update frequently.
I have a dataframes which have few rows among them some already exists in db. I want to update few columns of existing rows. How can we do that?
I see we have SaveModes:
append and override which might serve the purpose but there is a limitation in both the cases.
With append, I am getting primary key error, as this option tries to create a new row in db
With ovverride, I will loose values for the unchanged attributes in the tuple.
Can someone please suggest how can I update few attributes(Columns values) of a row(tuple).?
This can be handled in MySql level, The concept is known as upsert.
case when : primary key is new
The SQL will insert into MySQL DB as new row
Case when : primary key is existing
You can use
INSERT
ON DUPLICATE KEY UPDATE
Which will update the key with the new entries/changes.
Read More here and here.
The ideal way to such use case is, insert your data into a temporary table first in your MySQL DB and post that use a trigger in order to load that data into original table. Call that trigger from spark itself.
In spark, dataframes are immutable. So you cannot change a value in place. One way would be to read the complete table, make the modification and write back the complete table in overwrite mode. This will take time.
If your modifications are always for a particular group, say user id based or date based, then you can write the data based on that column using partitionBy(). Then you can read that partition using .filter() do the modifications and overwrite only that partition using insertInto() - from pyspark 2.3.0
Refer this answer for other versions for pyspark :Overwrite specific partitions in spark dataframe write method
For Example: I want to create 40 tables in one keyspace. In 40 tables I want to shard 3 tables. Is is it possible to shard specific tables without creating new keyspace.
I have seen How to shard only specific tables using vitess But for this we need to create new keyspace. I don't want to create new keyspace. I want sharded and unsharded tables in one keyspace is it possible?
This is currently not possible. A keyspace is categorized as sharded or unsharded. So, you have to migrate the tables you want to shard into a sharded keyspace and then reshard the keyspace.
Some people worked around this by assigning a "null primary vindex" to the unsharded tables, essentially forcing all rows to live in the first shard. But I don't know if this was experimental or was actually used in production.