I need to filter rows from an Azure Table Store that are less than 10 minutes old. I'm using a Azure Function App integration to query the table, so a coded solution is not viable in this case.
I'm aware of the datetime type, but for this I have to specify an explicit datetime, for example -
Timestamp gt datetime'2018-07-10T12:00:00.1234567Z'
However, this is insufficient as I need the query to run on a timer every 10 minutes.
According to the OData docs, there are built in functions such as totaloffsetminutes() and now(), but using these causes the function to fail.
[Error] Exception while executing function: Functions.FailedEventsCount. Microsoft.WindowsAzure.Storage: The remote server returned an error: (400) Bad Request.
Is there a way to query a Table Store dynamically in this way?
Turns out that this was easier than expected.
I added the following query filter to the Azure Table Store input integration -
Timestamp gt datetime'{afterDateTime}'
In conjunction with a parameter in the Function trigger route, and Bob's your uncle -
FailedEventsCount/after/{afterDateTime}
Appreciate for other use cases it may not be viable to pass in the datatime, but for me that is perfectly acceptable.
Related
I have an Event Hub that sends data to Time Series Insights, with the following message format:
{
"deviceId" : "Device1",
"time" : "2022-03-30T21:27:29Z"
}
I want to calculate the difference in seconds between the Event Hub EnqueuedTimeUtc property and time property.
I created a Time Series Insights with an Event Source without specifying the Timestamp property name, in that way in Time Series Insights our Timestamp ($ts) property will be the EnqueuedTimeUtc property of the Event.
Now with those two properties, using TSX (Time Series Expression Language), I want to do something like this:
$event.$ts - $event.time.DateTime
The problem I'm facing is that the result of that operation returns a DateTime, but in Time Series Expression there isn't a function to convert DateTime to Seconds, or to Unix Timestamp. Time Series Expresion Doc
Is there a way of achieving this using Time Series Insights and TSX (Time Series Expression)?
Thanks!
TSI is an depreciated service in Azure and there are not much features (inbuilt functions) available in it to explore data. Therefore, I suggest you to use Azure Data Explorer to work with the Event Hub Data.
Azure Data Explorer provides inbuild datetime_diff function which allows to calculate the period in many supported formats based on your requirement using simple Kusto Query Language.
datetime_diff(): Calculates calendarian difference between two datetime values.
Syntax:
datetime_diff(period,datetime_1,datetime_2)
Example:
second = datetime_diff('second',datetime(2017-10-30 23:00:10.100),datetime(2017-10-30 23:00:00.900))
I have a tracking SQL table which has following schema:
CREATE TABLE [dbo].[TEST_TABLE](
[id] [int] IDENTITY(1,1) NOT NULL,
[value] [nvarchar](50) NULL,
[status] [nvarchar](50) NULL,
[source] [nvarchar](50) NULL,
[timestamp] [datetime] NULL
)
My application code will automatically maintain the table by inserting record and updating the field status.
My target is to trigger an ADF pipeline based on the result of following query:
SELECT COUNT(1) AS cnt FROM [dbo].[TEST_TABLE] WHERE [status] = 'active'
If the result is >0, then trigger an ADF pipeline.
Current status:
My current work:
set up an Stored procedure SP_TEST to return 1 if condition is filled; otherwise 0
setup an pipeline like below:
the result of SP is parsed and used for routing to trigger later stages (which will mark the SQL table status to 'inactive' to avoid duplicate processing)
3. associate the pipeline with a scheduling trigger every 5 minutes.
My current work is "working", in the sense that it can detect for whether there is DB change every 5 minutes and execute subsequent processing.
Problem:
However, the scheuling trigger may be too frequent and cost activity run unit on every execution, which could be costly. Is there any trigger like "SQL table change trigger"?
what I have tried:
A quick google points me to this link, but seems no answer yet.
I am also aware of storage event trigger and custom events trigger. Unfortunately, we are not permitted to create other Azure resource. Only the existing ADF and SQL server is provided to us.
Appreciate any insights/directions in advance.
Polling using ADF can be expensive, we want to avoid that. Instead have the polling take place within an Azure Logic App, it's much cheaper. Here are the steps to listen to a SQL Server DB (Azure included) then trigger an ADF pipeline if a table change is found.
Here is the pricing for Azure Logic App:
I believe this means that every trigger is using a standard connector, so it will be 12.5 cents (USD) per 1000 firings of the app, and 2.5 cents (USD) per 1000 actions triggered.
For ADF it is $1 (USD) per 1000 activities, so it's much more expensive for ADF
Please let me know if you have any issues at all!
AFAIK there is no REST API providing this functionality directly. So, I am using restore for this (there are other ways but those don’t guarantee transactional consistency and are more complicated) via Create request.
Since it is not possible to turn off short time backup (retention has to be at least 1 day) it should be reliable. I am using current time for ‘properties.restorePointInTime’ property in request. This works fine for most databases. But one db returns me this error (from async operation request):
"error": {
"code": "BackupSetNotFound",
"message": "No backups were found to restore the database to the point in time 6/14/2021 8:20:00 PM (UTC). Please contact support to restore the database."
}
I know I am not out of range because if the restore time is before ‘earliestRestorePoint’ (this can be found in GET request on managed database) or in future I get ‘PitrPointInTimeInvalid’ error. Nevertheless, I found some information that I shouldn’t use current time but rather current time - 6 minutes at most. This is also true if done via Azure Portal (where it fails with the same error btw) which doesn’t allow to input time newer than current - 6 minutes. After few tries, I found out that current time - circa 40 minutes starts to work fine. But 40 minutes is a lot and I didn’t find any way to find out what time works before I try and wait for result of async operation.
My question is: Is there a way to find what is the latest time possible for restore?
Or is there a better way to do ‘copy’ of managed database which guarantees transactional consistency and is reasonably quick?
EDIT:
The issue I was describing was reported to MS. It was occuring when:
there is a custom time zone format e.g. UTC + 1 hour.
Backups are skipped for the source database at the desired point in time because the database is inactive (no active transactions).
This should be fixed as of now (25th of August 2021) and I were not able to reproduce it with current time - 10 minutes. Also I was told there should be new API which would allow to make copy without using PITR (no sooner than 1Q/22).
To answer your first question "Is there a way to find what is the latest time possible for restore?"
Yes. Via SQL. The only way to find this out is by using extended event (XEvent) sessions to monitor backup activity.
Process to start logging the backup_restore_progress_trace extended event and report on it is described here https://learn.microsoft.com/en-us/azure/azure-sql/managed-instance/backup-activity-monitor
Including the SQL here in case the link goes stale.
This is for storing in the ring buffer (max last 1000 records):
CREATE EVENT SESSION [Verbose backup trace] ON SERVER
ADD EVENT sqlserver.backup_restore_progress_trace(
WHERE (
[operation_type]=(0) AND (
[trace_message] like '%100 percent%' OR
[trace_message] like '%BACKUP DATABASE%' OR [trace_message] like '%BACKUP LOG%'))
)
ADD TARGET package0.ring_buffer
WITH (MAX_MEMORY=4096 KB,EVENT_RETENTION_MODE=ALLOW_SINGLE_EVENT_LOSS,
MAX_DISPATCH_LATENCY=30 SECONDS,MAX_EVENT_SIZE=0 KB,MEMORY_PARTITION_MODE=NONE,
TRACK_CAUSALITY=OFF,STARTUP_STATE=ON)
ALTER EVENT SESSION [Verbose backup trace] ON SERVER
STATE = start;
Then to see output of all backup events:
WITH
a AS (SELECT xed = CAST(xet.target_data AS xml)
FROM sys.dm_xe_session_targets AS xet
JOIN sys.dm_xe_sessions AS xe
ON (xe.address = xet.event_session_address)
WHERE xe.name = 'Verbose backup trace'),
b AS(SELECT
d.n.value('(#timestamp)[1]', 'datetime2') AS [timestamp],
ISNULL(db.name, d.n.value('(data[#name="database_name"]/value)[1]', 'varchar(200)')) AS database_name,
d.n.value('(data[#name="trace_message"]/value)[1]', 'varchar(4000)') AS trace_message
FROM a
CROSS APPLY xed.nodes('/RingBufferTarget/event') d(n)
LEFT JOIN master.sys.databases db
ON db.physical_database_name = d.n.value('(data[#name="database_name"]/value)[1]', 'varchar(200)'))
SELECT * FROM b
NOTE: This tip came to me via Microsoft support when I had the same issue of point in time restores failing what seemed like randomly. They do not give any SLA for log backups. I found that on a busy database the log backups seemed to happen every 5-10 minutes but on a quiet database hourly. Recovery of a database this way can be slow depending on number of transaction logs and amount of activity to replay etc. (https://learn.microsoft.com/en-us/azure/azure-sql/database/recovery-using-backups)
To answer your second question: "Or is there a better way to do ‘copy’ of managed database which guarantees transactional consistency and is reasonably quick?"
I'd have to agree with Thomas - if you're after guaranteed transactional consistency and speed you need to look at creating a failover group https://learn.microsoft.com/en-us/azure/azure-sql/database/auto-failover-group-overview?tabs=azure-powershell#best-practices-for-sql-managed-instance and https://learn.microsoft.com/en-us/azure/azure-sql/managed-instance/failover-group-add-instance-tutorial?tabs=azure-portal
A failover group for a managed instance will have a primary server and failover server with the same user databases on each kept in synch.
But yes, whether this suits your needs depends on the question Thomas asked of what is the purpose of the copy.
I have 3 node Spanner instance, and a single table that contains around 4 billion rows. The DDL looks like this:
CREATE TABLE predictions (
name STRING(MAX),
...,
model_version INT64,
) PRIMARY KEY (name, model_version)
I'd like to setup a job to periodically remove some old rows from this table using the Python Spanner client. The query I'd like to run is:
DELETE FROM predictions WHERE model_version <> ?
According to the docs, it sounds like I would need to execute this as a Partitioned DML statement. I am using the Python Spanner client as follows, but am experiencing timeouts (504 Deadline Exceeded errors) due to the large number of rows in my table.
# this always throws a "504 Deadline Exceeded" error
database.execute_partitioned_dml(
"DELETE FROM predictions WHERE model_version <> #version",
params={"model_version": 104},
param_types={"model_version": Type(code=INT64)},
)
My first intuition was to see if there was some sort of timeout I could increase, but I don't see any timeout parameters in the source :/
I did notice there was a run_in_transaction method in the Spanner lib that contains a timeout parameter, so I decided to deviate from the partitioned DML approach to see if using this method worked. Here's what I ran:
def delete_old_rows(transaction, model_version):
delete_dml = "DELETE FROM predictions WHERE model_version <> {}".format(model_version),
dml_statements = [
delete_dml,
]
status, row_counts = transaction.batch_update(dml_statements)
database.run_in_transaction(delete_old_rows,
model_version=104,
timeout_secs=3600,
)
What's weird about this is the timeout_secs parameter appears to be ignored, because I still get a 504 Deadline Exceeded error within a minute or 2 of executing the above code, despite a timeout of one hour.
Anyways, I'm not too sure what to try next, or whether or not I'm missing something obvious that would allow me to run a delete query in a timely fashion on this huge Spanner table. The model_version column has pretty low cardinality (generally 2-3 unique model_version values in the entire table), so I'm not sure if that would factor into any recommendations. But if someone could offer some advice or suggestions, that would be awesome :) Thanks in advance
The reason that setting timeout_secs didn't help was because the argument is unfortunately not the timeout for the transaction. It's the retry timeout for the transaction so it's used to set the deadline after which the transaction will stop being retried.
We will update the docs for run_in_transaction to explain this better.
The root cause was that the total timeout for the Streaming RPC calls was set too low in the client libraries, being set to 120s for Streaming APIs (eg ExecuteStreamingSQL used by partitioned DML calls.)
This has been fixed in the client library source code, changing them to a 60 minute timout (which is the maximum), and will be part of the next client library release.
As a workaround, in Java, you can configure the timeouts as part of the SpannerOptions when you connect your database. (I do not know how to set custom timeouts in Python, sorry)
final RetrySettings retrySettings =
RetrySettings.newBuilder()
.setInitialRpcTimeout(Duration.ofMinutes(60L))
.setMaxRpcTimeout(Duration.ofMinutes(60L))
.setMaxAttempts(1)
.setTotalTimeout(Duration.ofMinutes(60L))
.build();
SpannerOptions.Builder builder =
SpannerOptions.newBuilder()
.setProjectId("[PROJECT]"));
builder
.getSpannerStubSettingsBuilder()
.applyToAllUnaryMethods(
new ApiFunction<UnaryCallSettings.Builder<?, ?>, Void>() {
#Override
public Void apply(Builder<?, ?> input) {
input.setRetrySettings(retrySettings);
return null;
}
});
builder
.getSpannerStubSettingsBuilder()
.executeStreamingSqlSettings()
.setRetrySettings(retrySettings);
builder
.getSpannerStubSettingsBuilder()
.streamingReadSettings()
.setRetrySettings(retrySettings);
Spanner spanner = builder.build().getService();
The first suggestion is to try gcloud instead.
https://cloud.google.com/spanner/docs/modify-gcloud#modifying_data_using_dml
Another suggestion is to pass the range of name as well so that limit the number of rows scanned. For example, you could add something like STARTS_WITH(name, 'a') to the WHERE clause so that make sure each transaction touches a small amount of rows but first, you will need to know about the domain of name column values.
Last suggestion is try to avoid using '<>' if possible as it is generally pretty expensive to evaluate.
I have a very basic setup, in which I never get any output if I use the TIMESTAMP BY statement.
I have a stream analytics job which is reading from Event Hub and writing to the table storage.
The query is the following:
SELECT
*
INTO
MyOutput
FROM
MyInput TIMESTAMP BY myDateTime;
If the query uses timestamp statement, I never get any output events. I do see incoming events in the monitoring, there are no errors neither in monitoring nor in the maintenance logs. I am pretty sure that the source data has the right column in the right format.
If I remove the timestamp statement, then everything is working fine. The reason why I need the timestamp statement in the first place is because I need to write a number of queries in the same job, writing various aggregations to different outputs. And if I use timestamp in one query, I am required to use it in all other queries itself.
Am I doing something wrong? Perhaps SELECT * does not play well with TIMESTAMP BY? I just did not find any documentation explaining that...
{"myDateTime":"2015-08-02T10:59:02.0000000Z", "EventEnqueuedUtcTime":"2015-08-07T10:59:07.6980000Z"}
Late tolerance window: 00.00:00:05
All of your events are considered late arriving because myDateTime is 5 days before EventEnqueuedUtcTime. Can you try sending new events where myDateTime is in UTC and is "now" so it matches within a couple of seconds?
Also, when you started the job, what did you pick as the job start date time? Can you make sure you pick a date before the myDateTime values? You might try this first.