How do I design the database to calculate the account balance?
1) Currently I calculate the account balance from the transaction table
In my transaction table I have "description" and "amount" etc..
I would then add up all "amount" values and that would work out the user's account balance.
I showed this to my friend and he said that is not a good solution, when my database grows its going to slow down???? He said I should create separate table to store the calculated account balance. If did this, I will have to maintain two tables, and its risky, the account balance table could go out of sync.
Any suggestion?
EDIT: OPTION 2: should I add an extra column to my transaction tables "Balance".
now I do not need to go through many rows of data to perform my calculation.
Example
John buys $100 credit, he debt $60, he then adds $200 credit.
Amount $100, Balance $100.
Amount -$60, Balance $40.
Amount $200, Balance $240.
An age-old problem that has never been elegantly resolved.
All the banking packages I've worked with store the balance with the account entity. Calculating it on the fly from movement history is unthinkable.
The right way is:
The movement table has an 'opening
balance' transaction for each and every account. You'll need
this in a few year's time when you
need to move old movements out of the
active movement table to a history
table.
The account entity has a balance
field
There is a trigger on the movement
table which updates the account
balances for the credited and debited accounts. Obviously, it has commitment
control. If you can't have a trigger, then there needs to be a unique module which writes movements under commitment control
You have a 'safety net' program you
can run offline, which re-calculates
all the balances and displays (and
optionally corrects) erroneous
balances. This is very useful for
testing.
Some systems store all movements as positive numbers, and express the credit/debit by inverting the from/to fields or with a flag. Personally, I prefer a credit field, a debit field and a signed amount, this makes reversals much easier to follow.
Notice that these methods applies both to cash and securities.
Securities transactions can be much trickier, especially for corporate actions, you will need to accommodate a single transaction that updates one or more buyer and seller cash balances, their security position balances and possibly the broker/depository.
You should store the current account balance and keep it up to date at all times. The transaction table is just a record of what has happened in the past and shouldn't be used at a high frequency just to fetch the current balance. Consider that many queries don't just want balances, they want to filter, sort and group by them, etc. The performance penalty of summing every transaction you've ever created in the middle of complex queries would cripple even a database of modest size.
All updates to this pair of tables should be in a transaction and should ensure that either everything remains in sync (and the account never overdraws past its limit) or the transaction rolls back. As an extra measure, you could run audit queries that check this periodically.
This is a database design I got with only one table for just storing a history of operations/transactions. Currently working as charm on many small projects.
This doesn't replace a specific design. This is a generic solution that could fit most of the apps.
id:int
standard row id
operation_type:int
operation type. pay, collect, interest, etc
source_type:int
from where the operation proceeds.
target table or category: user, bank, provider, etc
source_id:int
id of the source in the database
target_type:int
to what the operation is applied.
target table or category: user, bank, provider, etc
target_id:int
id of the target in the database
amount:decimal(19,2 signed)
price value positive or negative to by summed
account_balance:decimal(19,2 signed)
resulting balance
extra_value_a:decimal(19,2 signed) [this was the most versatile option without using string storage]
you can store an additional number: interest percentage, a discount, a reduction, etc.
created_at:timestamp
For the source_type and target_type it would be better to use an enum or tables appart.
If you want a particular balance you can just query the last operation sorted by created_at descending limit to 1. You can query by source, target, operation_type, etc.
For better performance it's recommended to store the current balance in the required target object.
Of course you need to store your current balance with each row, otherwise it is too slow. To simplify development, you can use constraints, so that you dont need triggers and periodic checks of data integrity. I described it here Denormalizing to enforce business rules: Running Totals
A common solution to this problem is to maintain a (say) monthly opening balance in a snapshot schema. Calculating the current balance can be done by adding transactional data for the month to the monthly opening balance. This approach is often taken in accounts packages, particularly where you might have currency conversion and revaluations.
If you have problems with data volume you can archive off the older balances.
Also, the balances can be useful for reporting if you don't have a dedicated external data warehouse or a management reporting facility on the system.
Your friend is wrong and you are right, and I would advise you don't change things now.
If your db ever goes slow because of this, and after you have verified all the rest (proper indexing), some denormalisation may be of use.
You could then put a BalanceAtStartOfYear field in the Accounts table, and summarize only this year records (or any similar approach).
But I would certainly not recommend this approach upfront.
Here is would like to suggest you how can you store your opening balance with a very simple way:-
Create a trigger function on the transaction table to be called only after update or insert.
Create a column having name in the master table of account naming Opening Balance.
save your opening balance in array in the opening balance column in master table.
you even not need to use server side language use this store array simply you can use database array functions like available in PostgreSQL.
when you want to recalculate you opening balance in array just group your transaction table with array function and update the whole data in the master table.
I have done this in PostgreSQL and working fine.
over the period of time when your transaction table will become heavy then you can partition for your transaction table on the base of date to speed up the performance.
this approach is very easy and need not to use any extra table which can slow performance if joining table because lesser table in the joining will give you high performance.
My approach is to store the debits in a debit column, credit in the credit column and when fetching the data create two arrays, debit and credit array. Then keep appending the selected data to the array and do this for python:
def real_insert(arr, index, value):
try:
arr[index] = value
except IndexError:
arr.insert(index, value)
def add_array(args=[], index=0):
total = 0
if index:
for a in args[: index]:
total += a
else:
for a in args:
total += a
return total
then
for n in range(0, len(array), 1):
self.store.clear()
self.store.append([str(array[n][4])])
real_insert(self.row_id, n, array[n][0])
real_insert(self.debit_array, n, array[n][7])
real_insert(self.credit_array, n, array[n][8])
if self.category in ["Assets", "Expenses"]:
balance = add_array(self.debit_array) - add_array(self.credit_array)
else:
balance = add_array(self.credit_array) - add_array(self.debit_array)
Simple answer: Do all three.
Store the current balance; and in each transaction store the movement and a snapshot of the current balance at that point in time. This would give something extra to reconcile in any audit.
I've never worked on core banking systems, but I have worked on investment management systems, and in my experience this is how It's done.
Related
I am building a home budget app with NextJS + Prisma ORM + PostgreSQL.
I am not sure if my current strategy of how to handle deleting/reverting past transactions makes sense in terms of scaling up/db performance..
So, app functions in this way:
User adds transaction that are assigned to a chosen bank account. Every transaction row in db includes fields like amount, balanceBefore and balanceAfter.
After successful transaction, banks account current balance is being updated.
Now, assuming the situation where multiple transactions has been inserted and user realises he made a mistake somewhere along the line. He would then need to select the transaction and delete/update it, which would follow updating every row following this transaction to update balanceAfter and balanceBefore fields so the transactions history is correct.
Is there a better way of handling this kind of situation? Having to update a row that is couple thousand records in past might be heavy on resources.
Not only should you never delete or update a financial transaction but neither should your input data contain balances (before or after). Instead of updating or deleting a transaction you generate 2 new ones: one which reverses the incorrect transaction (thus restoring balances) and one that inserts the correct values.
As for balances, do not store them, just store the transaction amount. Then create a view which calculates the balances on the fly when needed. By creating a view you do not need to perform any calculations when DML is preformed on your transactions. See the following example for both the above.
I have an AWS application where DynamoDB is used for most data storage and it works well for most cases. I would like to ask you about one particular case where I feel DynamoDB might not be the best option.
There is a simple table with customers. Each customer can collect virtual coins so each customer has a balance attribute. The balance is managed by 3rd party service keeping up-to-date value and the balance attribute in my table is just a cached version of it. The 3rd party service requires its own id of the customer as an input so customers table contains also this externalId attribute which is used to query the balance.
I need to run the following process once per day:
Update the balance attribute for all customers in a database.
Find all customers with the balance greater than some specified constant value. They need to be sorted by the balance.
Perform some processing for all of the customers - the processing must be performed in proper order - starting from the customer with the greatest balance in descending order (by balance).
Question: which database is the most suitable one for this use case?
My analysis:
In terms of costs it looks to be quite similar, i.e. paying for Compute Units in case of DynamoDB vs paying for hours of micro instances in case of RDS. Not sure though if micro RDS instance is enough for this purpose - I'm going to check it but I guess it should be enough.
In terms of performance - I'm not sure here. It's something I will need to check but wanted to ask you here beforehand. Some analysis from my side:
It involves two scan operations in the case of DynamoDB which
looks like something I really don't want to have. The first scan can be limited to externalId attribute, then balances are queried from 3rd party service and updated in the table. The second scan requires a range key defined for balance attribute to return customers sorted by the balance.
I'm not convinced that any kind of indexes can help here. Basically, there won't be too many read operations of the balance - sometimes it will need to be queried for a single customer using its primary key. The number of reads won't be much greater than number of writes so indexes may slow the process down.
Additional assumptions in case they matter:
There are ca. 500 000 customers in the database, the average size of a single customer is 200 bytes. So the total size of the customers in the database is 100 MB.
I need to repeat step 1 from the above procedure (update the balance of all customers) several times during the day (ca. 20-30 times per day) but the necessity to retrieve sorted data is only once per day.
There is only one application (and one instance of the application) performing the above procedure. Besides that, I need to handle simple CRUD which can read/update other attributes of the customers.
I think people are overly afraid of DynamoDB scan operations. They're bad if used for regular queries but for once-in-a-while bulk operations they're not so bad.
How much does it cost to scan a 100 MB table? That's 25,000 4KB blocks. If doing eventually consistent that's 12,250 read units. If we assume the cost is $0.25 per million (On Demand mode) that's 12,250/1,000,000*$0.25 = $0.003 per full table scan. Want to do it 30 times per day? Costs you less than a dime a day.
The thing to consider is the cost of updating every item in the database. That's 500,000 write units, which if in On Demand at $1.25 per million will be about $0.63 per full table update.
If you can go Provisioned for that duration it'll be cheaper.
Regarding performance, DynamoDB can scan a full table faster than any server-oriented database, because it's supported by potentially thousands of back-end servers operating in parallel. For example, you can do a parallel scan with up to a million segments, each with a client thread reading data in 1 MB chunks. If you write a single-threaded client doing a scan it won't be as fast. It's definitely possible to scan slowly, but it's also possible to scan at speeds that seem ludicrous.
If your table is 100 MB, was created in On Demand mode, has never hit a high water mark to auto-increase capacity (just the starter capacity), and you use a multi-threaded pull with 4+ segments, I predict you'll be done in low single digit seconds.
I have a question about how I can model my stock database in order to get the best performance possible.
In SQL Server or in the Oracle, each update executed generates a little lock.
I'd like to know what's the best solution that you could tell me
Solution 1: create a product stock table with quantity column and for each input or output execute a SQL update against this column
Solution 2: create a table for product stock movement where for each input I would execute an insert with a positive quantity and for each output I would execute an insert with a negative quantity.
At the end of the day, I would execute an process for update the quantity of the stock products with the "sum" result of the product stock movement table
After that, I would delete all records in the product stock movement table
With solution 1, I would have the advantage that execute an simple select to get the product stock quantity but during the day I would have the disadvantage that have many locks due many quantity updates regarding output sold products
With solution 2, I'd have the disadvantage when, I will need to get the product stock quantity, I'd need to make a query with a join with product stock movement table and make a sum in all inputs and outputs of the consulted product, but in this way, during all day I wouldn't have any locks
What do you think about that two solutions presented?
Is it a good practice to make the modeling described in solution 2?
Thank you so much
A lot of assumptions are made here with a potential solution to a "hypothetical" problem. You don't have numbers to confidently state either of these design will lead to a problem. We don't know your hardware specs either.
Do some due diligence first and figure out how much volume are you dealing with on a daily or monthly basis etc along with how much read/write is going to happen any given time (min/hour)? Once you have these numbers (even if they are not accurate you should get some sense of activity) run some benchmarks on the actual instance that's hosting the database (or a comparable one) for both of your solution and see for yourself what performs better.
Repeat the exercise with 3x or 5x more read/write and compare again so you are covered for the growth in future.
Decisions made with a bunch of assumptions leads to very opinionated design with always results in poor choice. Always use data to drive your decisions and validate your assumption.
PS: talking from experience here. Given we are dealing with a very large transactions, we generally have a summary table and a detail table and use triggers to update count in summary table when new records get inserted in details etc.
Good luck.
I got this situation. Logic is customer will be given credit sale and they will repay money in installments. I need to store this details about products, qty and the amounts they are giving in installments.
In dashboard i need to show all customers with name total sale amount, paid amount and balance amount.
Approach i thought
tblCredit = Stores as rows for all the time they pay amount
(e.g) shan(Name), paper (product), 1500 (qty) , 2000 (Price), 100
(Debit) { initial purchase) }
shan (Name), -, -, -, 200 (Debit)
In query filter by name and sum(Price) - Sum(Debit amount) will give
balance
But this approach once the data grows is this aggregation will be trouble some ?
Is it possible like caching the aggregated result with timestamps or
something like that and update that at every operation when we are
inserting data in that table and show result from that ?
Note
Data growth rate will be high.
I am very new to designing.
Please guide me the best approach to handle this.
Update
Apart from dashboard i need to show report when users clicks report to know how much credit given for whom. So in any case i need a optimized query and logic and design to handle this.
Usually a dashboard do not need to get the data in real time. You may think of using data snapshot (schedule data insert after your aggregation) rather than maintaining a summary table update by different types of sales transactions, which is difficult in maintaining the integrity especially handling back-day process.
I am writing a rather large application that allows people to send text messages and emails. I will charge 7c per SMS and 2c per email sent. I will allow people to "recharge" their account. So, the end result is likely to be a database table with a few small entries like +100 and many, many entries like -0.02 and -0.07.
I need to check a person's balance immediately when they are trying to send an email or a message.
The obvious answer is to have cached "total" somewhere, and update it whenever something is added or taken out. However, as always in programming, there is more to it: what about monthly statements, where the balance needs to be carried forward from the previous month? My "intuitive" solution is to have two levels of cache: one for the current month, and one entry for each month (or billing period) with three entries:
The total added
The total taken out
The balance to that point
Are there better, established ways to deal with this problem?
Largely depends on the RDBMS.
If it were SQL Server, one solution is to create an Indexed view (or views) to automatically incrementally calculate and hold the aggregated values.
Another solution is to use triggers to aggregate whenever a row is inserted at the finest granularity of detail.