Adding multiple jobs for a number of zCentric agents (all JSR352 jobs). Is there a way to set defaults for Connections attributes in IDWC (Eg, hostname, username, password), to reduce effort to add jobs.
I think the best way is to specify the values in variable tables and use variables in the job you define in DWC.
At the following link you can find the official documentation about using variable from DWC in job type with advanced option (that includes also the JSR352)
https://www.ibm.com/support/knowledgecenter/en/SSRULV_9.3.0/com.ibm.tivoli.itws.doc_9.3/zos/src_man/eqqr1addvarondwc.htm
Related
According to BigQuery docs, you can ensure data consistency providing an insertId (https://cloud.google.com/bigquery/streaming-data-into-bigquery#dataconsistency). If it's not provided, BQ will try to ensure consistency based on internals Ids and best-effort.
Using the BQ API you can do that with the row_ids param (https://google-cloud-python.readthedocs.io/en/latest/bigquery/generated/google.cloud.bigquery.client.Client.insert_rows_json.html#google.cloud.bigquery.client.Client.insert_rows_json) but I can't find the same for the Apache Beam Python SDK.
Looking into the SDK I have noticed that a 'unique_row_id' property exist, but I really don't know how to pass my param to WriteToBigQuery()
How can I write into BQ (streaming) providing a row Id for deduplication?
Update:
If you use WriteToBigQuery then it will automatically create and
insert a unique row id called insertId for you, which will be inserted to bigquery. It's handled for you, you don't need to worry about it. :)
WriteToBigQuery is a PTransform, and in it's expand method calls BigQueryWriteFn
BigQueryWriteFn is a DoFn, and in it's process method calls _flush_batch
_flush_batch is a method that then calls the BigQueryWrapper.insert_rows method
BigQueryWrspper.insert_rows creates a list of bigquery.TableDataInsertAllRequest.RowsValueListEntry objects which contain the insertId and the row data as a json object
The insertId is generated by calling the unique_row_id method which returns a value consisting of UUID4 concatenated with _ and with an auto-incremented number.
In the current 2.7.0 code, there is this happy comment; I've also verified it is true :)
https://github.com/apache/beam/blob/master/sdks/python/apache_beam/io/gcp/bigquery.py#L1182
# Prepare rows for insertion. Of special note is the row ID that we add to
# each row in order to help BigQuery avoid inserting a row multiple times.
# BigQuery will do a best-effort if unique IDs are provided. This situation
# can happen during retries on failures.
* Don't use BigQuerySink
At least, not in it's current form as it doesn't support streaming. I guess that might change.
Original (non)answer
Great question, I also looked and couldn't find a certain answer.
Apache Beam doesn't appear to use that google.cloud.bigquery client sdk you've linked to, it has some internal generated api client, but it appears to be up-to-date.
I looked at the source:
The insertall method is there https://github.com/apache/beam/blob/18d2168ee71a1b1b04976717f0f955199bb00961/sdks/python/apache_beam/io/gcp/internal/clients/bigquery/bigquery_v2_client.py#L476
I also found the insertid mentioned
https://github.com/apache/beam/blob/master/sdks/python/apache_beam/io/gcp/internal/clients/bigquery/bigquery_v2_messages.py#L1707
So if you can make an InsertAll call it will use a TableDataInsertAllRequest and pass a RowsValueListEntry
class TableDataInsertAllRequest(_messages.Message):
"""A TableDataInsertAllRequest object.
Messages:
RowsValueListEntry: A RowsValueListEntry object.
The RowsValueListEntry message is where the insertid is.
Here's the API docs for insert all
https://cloud.google.com/bigquery/docs/reference/rest/v2/tabledata/insertAll
I will look some more at this because I don't see the WriteToBigQuery() exposing this.
I suspect that the 'bigquery will remember this for at least one minute` is a pretty loose guarantee for de-duping. The docs suggest using datastore if you need transactions. Otherwise you might need to run SQL with window functions to de-dupe at runtime, or run some other de-duping jobs on bigquery.
Perhaps using batch_size parameter of WriteToBigQuery(), and running a combine (or at worst a GroupByKey) step in dataflow is a more stable way to de-dupe prior to writing.
I have read about DBSM_SESSION package, global user-defined application contexts and client identifiers. They are used to maintain database session with distributed application clients. But I don't understand what exactly is GLOBAL_CONTEXT
According to Oracle documentation
GLOBAL_CONTEXT displays the values of global context attributes, which are accessible for the current session, based on the CLIENT_IDENTIFIER value. This view is similar to SESSION_CONTEXT, which lists the values of session (or local) context attributes set under the current session.
How does one add and removes attributes of GLOBAL_CONTEXT according to client identifiers? Why GLOBAL_CONTEXT coexists with user defined global contexts?
How is this
SELECT * FROM GLOBAL_CONTEXT
different from
SELECT SYS_CONTEXT('GLOBAL_CONTEXT', 'attr_name') FROM DUAL
Or is the latter even valid in the case of GLOBAL_CONTEXT?
I'm trying to use HAProxy for rate-limiting.
I need to keep track of several endpoints and limit them individually.
So far I was using general purpose counters. However, there is only 3 of them, sc0 to sc2.
In the documentation, it mentions that all the operations on these counters take an optional table parameter, but it's not clear, then, if I can track different things on different tables, but using the same counters.
In other words: is the limit of 3 general purpose counters global, or per sticky table?
If, after proper table definition and track instructions, I do
sc1_inc_gpc0(table1)
(and, under different conditions)
sc1_inc_gpc0(table2)
And then have 2 acl rules like
acl X sc1_get_gpc0(table1) gt 1
acl Y sc1_get_gpc0(table2) gt 1
Will the two acls work indepentently, or they would to all effects track the same counter?
Thanks for all help!
(In case you are wondering: for a number of reasons, at the moment I could not use a different solution than HAProxy for rate-limiting)
Self answered after looking at the source code and testing.
Yes it is possible to use the same counter on different tables
Moreover, you can also increment the number of available counters at build time. The default is 3, but it can be set up to 10 for sure. Then generic versions of the functions like sc_gpc0_rate(<ctr>[,<table>]) can be used, passing the index of the new counter as first argument.
In my application I store users as user:n where n is a unique ID.
When a new user is created I increment a global variable such as user_count and use that ID as user:n.
But, I have an issue where I need to ensure an email is not already in use. I've done some reading around and the only way I can see how to do this is to:
1) Loop through the users. But, I am not keen on this solution as it could cause slower performance right?
2) Create a lookup that contains a list of email addresses used.
Both solutions seem a bit strange to me as I come from an SQL background.
Are these the only options available? I also have to do the same check for usernames too.
You could use Sets:
On registration: sadd taken_emails "john#example.com"
And testing with: sismember taken_emails "bob#exmaple.com"
Note that you have a possible race-condition where two users try to use the same email at the same time, both test and get "free" and then both register with it. You could use a lock to make sure they don't both get it, or make the registration operation atomic with either WATCH/MULTI/EXEC or with a lua script.
I've currently got a dataset which is something like:
channel1 = user1,user2,user3
channel2 = user4,user5,user6
(note- these are not actual names, the text is not a predictable sequence)
I would like to have the most optimized capability for the following:
1) Add user to a channel
2) Remove user from a channel
3) Get list of all users in several selected channels, maintaining knowledge of which channel they are in (in case it matters- this can also be simply checking whether a channel has any users or not without getting an actual list of them)
4) Detect if a specific user is in a channel (willing to forego this feature if necessary)
I'm a bit hungup on the fact that there are only two ways I can see of getting multiple keys at once:
A) Using regular keys and a mget key1, key2, key3
In this solution, each value would be a JSON string which can then be manipulated and queried clientside to add/remove/determine values. This itself has a couple problems- firstly that it's possible another client will change the data while it's being processed (i.e. this solution is not atomic) and it's not easy to detect right away if a channel contains a specific user even though it is easy to detect if a channel has any users (this is low priority, as stated above)
B) Using sets and sunion
I would really like to use sets for this solution somehow, the above solution just seems wrong... but I cannot see how to query multiple sets at once and maintain info about which set each member is from or if any of the sets in the union have 0 members (sunion only gives me a final set of all the combined members)
Any solutions which can implement the above points 1-4 in optimal time and atomic operations?
EDIT: One idea which might work in my specific case is to store the channel name as part of the username and then use sets. Still, it would be great to get a more generic answer
Short answer: use sets + pipelining + MULTI/EXEC, or sets + Lua.
1) Add user to a channel
SADD command
2) Remove user from a channel
SREM command
3) Get list of all users in several selected channels
There are several ways to do it.
If you don't need strict atomicity, you just have to pipeline several SMEMBERS commands to retrieve all the sets in one roundtrip. If you are just interested whether channels have users or not, you can replace SMEMBERS by SCARD.
If you need strict atomicity, you can pipeline a MULTI/EXEC block containing SMEMBERS or SCARD commands. The output of the EXEC command will contain all the results. This is the solution I would recommend.
An alternative (atomic) way is to call a server-side Lua script using the EVAL command. Lua script executions are always atomic. The script could take a number of channel as input parameters, and build a multi-layer bulk reply to return the output.
4) Detect if a specific user is in a channel
SISMEMBER command - pipeline them if you need to check for several users.