I have a table (several tables, actually) which contains a textual item_id column, originally populated with an ID provided by a third-party data source. Unfortunately, the data source recently changed the format of their IDs. Meanwhile, my customers will call into my service using whatever format of the ID they most recently saw, meaning that they frequently get incomplete data because they're looking at an item_id which is either too old or too new.
Fortunately, the change in format was relatively straightforward, so it's easy for me to normalize both old and new item_id values into a consistent value, but I'd like to do this for ALL queries regardless of where they come from. Is it possible to set up some sort of trigger that intercepts any query against the item_id column and normalizes the queried value?
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The idea of the SaaS tool is to have dynamic tables with dynamic custom fields and values of different types, we were thinking to use "force.com/salesforce.com" example but is seems to be too complicated to maintain moving forward, also making some reports to create with a huge abstraction level, so we came up with simple idea but we have to be sure that this is kinda good approach.
This is the architecture we have today (in few steps).
Each tenant has it own separate database on the cluster (Postgres 12).
TABLE table, used to keep all of those tables as reference, this entity has ManyToOne relation to META table and OneToMany relation with DATA table.
META table is used for metadata configuration, has OneToMany relation with FIELDS (which has name of the fields as well as the type of field e.g. TEXT/INTEGER/BOOLEAN/DATETIME etc. and attribute value - as string, only as reference).
DATA table has ManyToOne relation to TABLES and 50 character varying columns with names like: attribute1...50 which are NULL-able.
Example flow today:
When user wants to open a TABLE DATA e.g. "CARS", we load the META table with all the FIELDS (to get fields for this query). User specified that he want to query against: Brand, Class, Year, Price columns.
We are checking by the logic, the reference for Brand, Class, Year and Price in META>FIELDS table, so we know that Brand = attribute2, Class = attribute 5, Year = attribute6 and Price = attribute7.
We parse his request into a query e.g.: SELECT [attr...2,5,6,7] FROM DATA and then show the results to user, if user decide to do some filters on it, based on this data e.g. Year > 2017 AND Class = 'A' we use CAST() functionality of SQL for example SELECT CAST(attribute6 AS int) AND attribute5 FROM DATA WHERE CAST(attribute6 AS int) > 2017 AND attribute5 = 'A';, so then we can actually support most principles of SQL.
However moving forward we are scared a bit:
Manage such a environment for more tenants while we are going to have more tables (e.g. 50 per customer, with roughly 1-5 mil per TABLE (5mil is maximum which we allow, for bigger data we have BigQuery) which is giving us 50-250 mil rows in single table DATA_X) which might affect performance of the queries, especially when we gave possibilities to manage simple WHERE statements (less,equal,null etc.) using some abstraction language e.g. GET CARS [BRAND,CLASS,PRICE...] FILTER [EQ(CLASS,A),MT(YEAR,2017)] developed to be similar to JQL (Jira Query Language).
Transactions lock, as we allow to batch upload CSV into the DATA_X so once they want to load e.g. 1GB of the data, it kinda locks the table for other systems to access the DATA table.
Keeping multiple NULL columns which can affect space a bit (for now we are not that scared as while TABLE creation, customer can decide how many columns he wants, so based on that we are assigning this TABLE to one of hardcoded entities DATA_5, DATA_10, DATA_15, DATA_20, DATA_30, DATA_50, where numbers corresponds to limitations of the attribute columns, and those entities are different, we also support migration option if they decide to switch from 5 to 10 attributes etc.
We are on super early stage, so we can/should make those before we scale, as we knew that this is most likely not the best approach, but we kept it to run the project for small customers which for now is working just fine.
We were thinking also about JSONB objects but that is not the option, as we want to keep it simple for getting the data.
What do you think about this solution (fyi DATA has PRIMARY key out of 2 tables - (ID,TABLEID) and built in column CreatedAt which is used form most of the queries, so there will be maximum 3 indexes)?
If it seem bad, what would you recommend as the alternative to this solution based on the details which I shared (basically schema-less RDBMS)?
IMHO, I anticipate issues when you wanted to join tables and also using cast etc.
We had followed the approach below that will be of help to you
We have a table called as Cars and also have a couple of tables like CarsMeta, CarsExtension columns. The underlying Cars table will have all the common fields for a ll tenant's. Also, we will have the CarsMeta table point out what are the types of columns that you can have for extending the Cars entity. In the CarsExtension table, you will have columns like StringCol1...5, IntCol1....5, LongCol1...10
In this way, you can easily filter for data also like,
If you have a filter on the base table, perform the search, if results are found, match the ids to the CarsExtension table to get the list of exentended rows for this entity
In case the filter is on the extended fields, do a search on the extension table and match with that of the base entity ids.
As we will have the extension table organized like below
id - UniqueId
entityid - uniqueid (points to the primary key of the entity)
StringCol1 - string,
...
IntCol1 - int,
...
In this case, it will be easy to do a join for entity and then get the data along with the extension fields.
In case you are having the table metadata and data being inferred from separate tables, it will be a difficult task to maintain this over long period of time and also huge volume of data.
HTH
I have a table with several fields, this table almost never change but for one field, "version" which change very often.
Would it be relevant to put that single field into a separate table in order to reduce how often locks are put on the main table?
For instance I have a table tType and a table tEntry.
Whenever I add/deleted/update any row of tEntry, I need to update the "version" field of tType. There might be thousand of rows inside tEntry for a single tType referenced row. Meaning the version number could change very often, though any other data of tType (such as name, id, etc.) doesn't change.
Your Referral to tType and tEntry sounds like you are implementing a key-value store in a rdbms. There are several discussions you can google about this topic. In the www there seems to be consesus, that cons overweight pros on that. An option would be to look at key value stores, no sql, multi column DBs, etc (wikipedia)...
The next "anti-pattern" I recognized is that you try to mix transactional data with 'master data' in the table tType. Try to avoid this, even if your selects get more uncomfortable and need to be tuned better. Keep off the version info from the tType, if this changes extremely often. Look here to get the concept: MySQL JOIN the most recent row only?
I am obtaining a json array from a url and inserting data into a table. Since the contents of the url are subject to change, I want to make a second connection to a url and check for updates and insert new records in y table using sqlite3.
The issues that I face are:
1) My table doesn't have a primary key
2) The url lists the changes on the same day. Hence, if I run my app multiple times, when I insert values in my database, I get duplicate entries. I want to keep a check for the day duplicated entries that should be removed. The problem can be solved by adding a constraint, but since the url itself has duplicated values, I find it difficult.
The only way I can see you can do it if you have no primary key or something you can use that is unique to each record, is when you get your new data in you go through the new entries where for each one you check if the exact same data exists in the database already. If it doesn't then you add it, if it does then you skip over it.
You could even do something like create a unique key yourself for each entry which is a concatenation of each column of the table. That way you can quickly do the check for if the entry already exists in the database.
I see two possibilities depending on your setup:
You have a column setup as UNIQUE (this can be through a PRIMARY KEY or not). In this case, you can use the ON CONFLICT clause:
http://www.sqlite.org/lang_conflict.html
If you find this construct a little confusing, you can instead use "INSERT OR REPLACE" or "INSERT OR IGNORE" as described here:
http://www.sqlite.org/lang_insert.html
You do not have a column setup as UNIQUE. In this case, you will need to SELECT first to verify for duplicate data, and based on the result INSERT, UPDATE, or do nothing.
A more common & robust way to handle this is to associate a timestamp with each data item on the server. When your app interrogates the server it provides the timestamp corresponding to the last time it synced. The server then queries its database and returns all values that are timestamped later than the timestamp provided by the app. Then it also returns a new timestamp value for the app to store, to use on the next sync.
this is my first time using SQL at all, so this might sound basic. I'm making an iPhone app that creates and uses a sqlite3 database (I'm using the libsqlite3.dylib database as well as importing "sqlite3.h"). I've been able to correctly created the database and a table in it, but now I need to know the best way to get stuff back from it.
How would I go about retrieving all the information in the table? It's very important that I be able to access each row in the order that it is in the table. What I want to do (if this helps) is get all the info from the various fields in a single row, put all that into one object, and then store the object in an array, and then do the same for the next row, and the next, etc. At the end, I should have an array with the same number of elements as I have rows in my sql table. Thank you.
My SQL is rusty, but I think you can use SELECT * FROM myTable and then iterate through the results. You can also use a LIMIT/OFFSET(1) structure if you do not want to retrieve all elements at one from your table (for example due to memory concerns).
(1) Note that this can perform unexpectedly bad, depending on your use case. Look here for more info...
How would I go about retrieving all the information in the table? It's
very important that I be able to access each row in the order that it
is in the table.
That is not how SQL works. Rows are not kept in the table in a specific order as far as SQL is concerned. The order of rows returned by a query is determined by the ORDER BY clause in the query, e.g. ORDER BY DateCreated, or ORDER BY Price.
But SQLite has a rowid virtual column that can be used for this purpose. It reflects the sequence in which the rows were inserted. Except that it might change with a VACUUM. If you make it an INTEGER PRIMARY KEY it should stay constant.
order by rowid
Can you share your thoughts how would you implement data versioning in PostgreSQL. (I've asked similar question regarding Cassandra and MongoDB. If you have any thoughts which db is better for that please share)
Suppose that I need to version records in a simple address book. Address book records are stored in one table without relations for simplicity. I expect that the history:
will be used infrequently
will be used all at once to present it in a "time machine" fashion
there won't be more versions than few hundred to a single record.
history won't expire.
I'm considering the following approaches:
Create a new object table to store history of records with a copy of schema of addressbook table and add timestamp and foreign key to address book table.
Create a kind of schema less table to store changes to address book records. Such table would consist of: AddressBookId, TimeStamp, FieldName, Value. This way I would store only changes to the records and I wouldn't have to keep history table and address book table in sync.
Create a table to store seralized (JSON) address book records or changes to address book records. Such table would looks as follows: AddressBookId, TimeStamp, Object (varchar).
Again this is schema less so I wouldn't have to keep the history table with address book table in sync.
(This is modelled after Simple Document Versioning with CouchDB)
I do something like your second approach: have the table with the actual working set and a history with changes (timestamp, record_id, property_id, property_value). This includes the creation of records. A third table describes the properties (id, property_name, property_type), which helps in data conversion higher up in the application. So you can also track very easily changes of single properties.
Instead of a timestamp you could also have an int-like, wich you increment for every change per record_id, so you have an actual version.
You could have start_date and end_date.
When end_date is NULL, it`s the actual record.
I'm versioning glossary data, and my approach was pretty successful for my needs. Basically, for records you need versioning, you divide the fieldset into persistent fields and version-dependent fields, thus creating two tables. Some of the first set should also be the unique key for the first table.
Address
id [pk]
fullname [uk]
birthday [uk]
Version
id [pk]
address_id [uk]
timestamp [uk]
address
In this fashion, you get an address subjects determined by fullname and birthday (should not change by versioning) and a versioned records containing addresses. address_id should be related to Address:id through foreign key. With each entry in Version table you'll get new version for subject Address:id=address_id with a specific timestamp, in which way you can have a history reference.