I currently have this code which loops through every row in my mongoDB collection table, and either inserts a row or updates a row with new data (depending on whether todays date and studentID is there or not):
for rowindex, row in InsertionData.iterrows():
record = row.to_dict()
record['date'] = datetime.datetime.combine(datetime.date.today(), datetime.time(0, 0))
collection.replace_one({'student': row.get('student'), 'date': record['date']}, record, upsert=True)
However, I was wondering whether there was a way to do a bulk upsert?
My idea is that it would loop through all of the records to be updated, create the update script and add it to an operations array. Then once it either runs out of records or it reaches 5000, it runs a bulkWrite function to Mongo, which basically sends one request to the mongo server rather than 5000 single requests.
This would make it much faster, as the biggest slow down is communication between server's rather than the actual update function itself.
Does anyone have any idea how to do this in python/pymongo?
Use Bulk Write Operations.
Create a list and add each updates as a ReplaceOne, e.g.
operations = []
for rowindex, row in InsertionData.iterrows():
record = row.to_dict()
record['date'] = datetime.datetime.combine(datetime.date.today(), datetime.time(0, 0))
operations.append(ReplaceOne({'student': row.get('student'), 'date': record['date']}, record, upsert=True))
collection.bulk_write(operations)
Related
We have a script that should be running daily at 12 am on GCP cloud function and scheduler that sends data to a table in bigquery.
The cron job unfortunately used to send the data every minute at 12 am, that means that the file would be uploaded 60 times instead of only one time
The cron timer was * * 3 * * * instead of 00 3 * * *
How can we fix the table?
Noting that the transferred data is now deleted from the source, so far we depend on getting the unique values, but the table is getting too large
Any help would be much appreciated
I have two options for you, plus a comment on how to avoid this in future. I recommend reading and comparing both options before proceeding.
Option One
If this is a one-off fix, I recommend you simply
navigate to the table (your_dataset.your_table) in the UI
click 'snapshot' and create a snapshot in case you make a mistake in the next part
run SELECT DISTINCT * FROM your_dataset.your_table in the UI
click 'save results' and select 'bigquery table' then save as a new table (e.g. your_dataset.your_table_deduplicated)
navigate back to the old table and click the 'delete' button, then authorise the deletion
navigate to the new table and click the 'copy' button, then save it in the location the old table was in before (i.e. call the copy your_dataset.your_table)
delete your_dataset.your_table_deduplicated
This procedure will result in your replacing the current table with another with the same schema but without duplicated records. You should check that it looks as you expect before you discard your snapshot.
Option Two
A quicker approach, if you're comfortable with it, would be using the Data Manipulation Language (DML).
There is a DELETE statement, but you'd have to construct an appropriate WHERE clause to only delete the duplicate rows.
There is a simpler approach, which is equivalent to option one and just requires you to run this query:
CREATE OR REPLACE TABLE your_dataset.your_table AS
SELECT DISTINCT * FROM your_dataset.your_table
Again, you may wish to take a snapshot before running this.
The Future
If you have a cloud function that sends data to BigQuery on a schedule, then best-practice would be for this function to be idempotent (i.e. doesn't matter how many times you run it, if the input is the same the output is the same).
A typical pattern would be to add a stage to your function to pre-filter the new records.
Depending on your requirements, this stage could
prepare the new records you want to insert, which should have some unique, immutable ID field
SELECT some_unique_id FROM your_dataset.your_table -> old_record_ids
filter the new records, e.g. in python new_records = [record for record in prepared_records if record["id"] not in old_record_ids]
upload only the records that don't exist yet
This will prevent the sort of issues you have encountered here.
I have a single database table on a relational database. Data will be loaded into it. I then want to have multiple servers processing that data concurrently (I don't want to have only one server running at a time). E.g. each server will:
Query for a fixed number of rows
Do some work for each row retrieved
Update each row to show it has been processed
How do I ensure that each row is only processed once? Note I don't want to pre-assign a row of data to a server; i'm designing for high availability so the solution should keep running if one or more servers goes down.
The solution I've gone for so far is as follows:
The table has three columns: LOCKED_BY (VARCHAR), LOCKED_AT (TIMESTAMP) and PROCESSED (CHAR)
Each server starts by attempting to "pseudo-lock" some rows by doing:
UPDATE THE_TABLE
SET LOCKED_BY= $servername,
LOCKED_AT = CURRENT_TIMESTAMP,
WHERE (LOCKED_BY = null OR (CURRENT_TIMESTAMP- LOCKED_AT > $timeout)
AND PROCSSED = 'N'
i.e. try to "pseudo-lock" rows that aren't locked already or where the pseudo-lock as expired. Only do this for unprocessed rows.
More than one server may have attempted this at the same time. The current server needs to query to find out if it was successful in the "pseudo-lock":
SELECT * FROM THE_TABLE
WHERE LOCKED_BY = $server_name
AND PROCESSED = 'N'
If any rows are returned the server can process them.
Once the processing has been done the row is updated
UPDATE THE_TABLE SET PROCESSED = 'Y' WHERE PRIMARYKEYCOL = $pk
Note: the update statement should ideally limit the number of rows updated.
If you are open to changing platform then I would suggest moving to a modern, cloud-based solution like Snowflake. This will do what you want but in the background and by default - so you don't need to know what it's doing or how it's doing it (unless you want to).
This may come across as patronising, which is not my intention, but what you are attempting (in the way you are attempting it) is very complex; so if you don't already know how to do it then someone telling you how to do it is not going to give you the skills/experience you need to be able to implement it successfully
I am working on my cron system which gathers informaiton via an API call. For most, it has been fairly straight forward, but now I am faced with multiple difficulties, as the API call is dependant on who is making the API request. It runs through each users API Key and certain information will be visible/hidden to them and visaversa to the public.
There are teams, and users are part of teams. A user can stealth their move, however all information will be showed to them and their team, however this will not be visible to their oponent, however both teams share the same id and have access tothe same informaiton, just one can see more of it than the other.
Defendants Point Of View
"attacks": {
"12345`": {
"timestamp": 1645345234,
"attacker_id": "",
"attacker_team_id": "",
"defender_id": 321,
"defender_team_id": 1,
"stealthed": 1
}
}
Attackers Point Of View
"attacks": {
"12345`": {
"timestamp": 1645345234,
"attacker_id": 123,
"attacker_team_id": 2
"defender_id": 321,
"defender_team_id": 1,
"stealthed": 1,
"boosters": {
"fair_fight": 3,
"retaliation": 1,
"group_attack": 1
}
}
}
So, if the defendant's API key is first used, id 12345 will already be in the team_attacks table but will not include the attacker_id and attacker_team_id. For each insert there after, I need to check to see whether the new insert's ID already exist and has any additional information to add to the row.
Here is the part of my code that loops through the API and obtains the data, it loops through all the attacks per API Key;
else if ($category === "attacks") {
$database = new Database();
foreach($data as $attack_id => $info) {
$database->query('INSERT INTO team_attacks (attack_id, attacker_id, attacker_team_id, defender_id, defender_team_id) VALUES (:attack_id, :attacker_id, :attacker_team_id, :defender_id, :defender_team_id)');
$database->bind(':attack_id', $attack_id);
$database->bind(':attacker_id', $info["attacker_id"]);
$database->bind(':attacker_team_id', $info["attacker_team_id"]);
$database->bind(':defender_id', $info["defender_id"]);
$database->bind(':defender_team_id', $info["defender_team_id"]);
$database->execute();
}
}
I have also been submitting to the news table, and typically I have simply been submitting X new entries have been added or whatnot, however I haven't a clue if there is a way to check during the above if any new entries and any updated entries to produce two news feeds:
2 attacks have bee updated.
49 new attack information added.
For this part, I was simply counting how many is in the array, but this only works for the first ever upload, I know I cannot simply count the array length on future inserts which require additional checks.
If The attack_id Does NOT Already Exist I also need to submit the boosters into another table, for this I was adding them to an array during the above loop and then looping through them to submit those, but this also depends on the above, not simply attempting to upload for each one without any checks. Boosters will share the attack_id.
With over 1,000 teams who will potentially have at least one members join my site, I need to be as efficient as this as possible. The API will give the last 100 attacks per call and I want this to be within my cron which collects any new data every 30 seconds, so I need to sort through potentially 100,000.
In SQL, you can check conditions when inserting new data using merge:
https://en.wikipedia.org/wiki/Merge_(SQL)
Depending on the database you are using, the name and syntax of the command might be different. Common names for the command are also upsert and replace.
But: If you are seeking for high performance and almost-realtimeness, consider using a cache holding critical aggregated data instead of doing the aggregation 100'000 times per minute.
This may or may not be the "answer" you're looking for. The question(s) imply use of a single table for both teams. It's worth considering one table per team for writes to avoid write contention altogether. The two data sets could be combined at query time in order to return "team" results via the API. At scale, you could have another process calculating and storing combined team results in an API-specific cache table that serves the API request.
Is there are one or some reliable variants to solve easy task?
I've got a number of XML files which will be converting into 6 SQL tables (via SSIS).
Before the end of this process i need to add a new (in fact - common for all tables) column (or field) into each of them.
This column represents ID with assigning range and +1 incrementing step. Like (350000, 1)
Yes, i know how to solve it on SSMS SQL stage. But i need a solution at SSIS's pre-SQL converting lvl.
I'm sure there should be well-known pattern-solutions to deal with it.
I am going to take a stab at this. Just to be clear, I don't have a lot of information in your question to go on.
Most XML files that I have dealt with have a common element (let's call it a customer) with one to many attributes (this can be invoices, addresses, email, contacts, etc).
So your table structure will be somewhat star shaped around the customer.
So your XML will have a core customer information on a 1 to 1 basis that can be loaded into a single main table, and will have array information of invoices and an array of addresses etc. Those arrays would be their own tables referencing the customer as a key.
I think you are asking how to create that key.
Load the customer data first and return the identity column to be used as a foreign key when loading the other tables.
I find it easiest to do so in script component. I'm only going to explain how to get the key back. I personally would handle the whole process in C# (deserializing and all).
Add this to Using Block:
Using System.Data.OleDB;
Add this into your main or row processing depending on where the script task / component is:
string SQL = #"INSERT INTO Customer(CustName,field1, field2,...)
values(?,?,?,...); Select cast(scope_identity() as int);";
OleDBCommanad cmd = new OleDBCommand();
cmd.CommandType = System.Data.CommandType.Text;
cmd.CommandText = SQL;
cmd.Parameters.AddWithValue("#p1",[CustName]);
...
cmd.Connection.Open();
int CustomerKey = (int)cmd.ExecuteScalar(); //ExecuteScalar returns the value in first row / first column which in our case is scope_identity
cmd.Connection.Close();
Now you can use CustomerKey for all of the other tables.
I am working on an IBM DB, but I think the concepts are pretty much the same.
I want to fetch a single line, return these data (or save them into variables let's say) and update some fields of that specific row. There might be a number of instance who try to perform that query in the same time, and therefore we need the operation of fetching to be atomic.
Each row has a field called pending, init to FALSE. When one instance fetches this row, this is set to TRUE (that's the update I want to do). Reason that I need it too be atomic, is that my query fetches the first row of the table, where pending is FALSE.
In pseudocode I have something like this :
OPEN_DB(myDb, "DBNAME"); // opening the DB
BEGIN_TRANSACTION(myDb); // beginning transaction on my db
EXECUTE_QUERY(myDb,"SELECT * FROM tbname WHERE pending == 0 ORDER BY colid LIMIT 1");
... assign a cursor to my results
while (valid_data) {
// assign column fields to variable
// and here i want to do the update for this column
// I guess the problem is here
EXECUTE_QUERY(myDb,"UPDATE tbname SET pending = 1 WHERE colid=#colid")
}
COMMIT();
Don't worry about the syntax, they are macros and once executed on its own they work. I am guessing the problem is on the second query but why?
Should I commit first and then perform the second query?
If yes, then how can I ensure that there will be no other instance reading the same row before I update it?
I guess this doesn't have to do with the DB I am using.
If I comment the second one, code works. If I execute the second one stand alone, it works as well.
Option 1 - do a select for update for the curor which would be pessimistic locking
Option 2 - do an optimistic lock and handle the exception. The lock needs a column like last updated time or version illustrated below:
OPEN CURSOR
UPDATE table set col = :new_value
WHERE id = :id_from_cursor
last_updated = :last_updated_from_cursor;
CHECK if update row count == 1 COMMIT
IF not throw exception
Though I think Option 1 should work nicely unless you a significant time gap between the select & update