I'm currently trying to optimize my program. I have a large database which consists of data which are timestamped. The data I need to update is only data for the current day, so I don't want to search the entire database more than once to find only the entries of today. Is there a way to select something and then use it later in several different (MERGE INTO) commands?
I want to select all the data of today, then run a while loop (in java) on every single entry of data for today updating them all. So is this even possible? Or do I have to traverse the entire database for each while-loop iteration?
If you are optimizing your program and your database is timestamped. Then the first thing you can do is to create index for the timestamps field. This will reduce your query execution time because your filter criteria is related to that time-stamp field.
Use a proper data caching technology, like memcached in order to minimize database hits for read-heavy, slowly changing data.
Related
I have a spark job that gets data from multiple sources and aggregates into one table. The job should update the table only if there is new data.
One approach I could think of is to fetch the data from the existing table, and compare with the new data that comes in. The comparison happens in the spark layer.
I was wondering if there is any better way to compare, that can improve the comparison performance.
Please let me know if anyone has a suggestion on this.
Thanks much in advance.
One approach I could think of is to fetch the data from the existing
table, and compare with the new data that comes in
IMHO entire data compare to load new data is not performant.
Option 1:
Instead you can create google-bigquery partition table and create a partition column to load the data and also while loading new data you can check whether the new data has same partition column.
Hitting partition level data in hive or bigquery is more useful/efficient than selecting entire data and comparing in spark.
Same is applicable for hive as well.
see this Creating partitioned tables
or
Creating and using integer range partitioned tables
Option 2:
Another alternative is with GOOGLE bigquery we have merge statement, if your requirement is to merge the data with out comparision, then you can go ahead with MERGE statement .. see doc link below
A MERGE statement is a DML statement that can combine INSERT, UPDATE, and DELETE operations into a single statement and perform the operations atomically.
Using this, We can get performance improvement because all three operations (INSERT, UPDATE, and DELETE) are performed in one pass. We do not need to write an individual statement to update changes in the target table.
There are many ways this problem can be solved, one of the less expensive, performant and scalable way is to use a datastore on the file system to determine true new data.
As data comes in for the 1st time write it to 2 places - database and to a file (say in s3). If data is already on the database then you need to initialize the local/s3 file with table data.
As data comes in 2nd time onwards, check if it is new based its presence on local/s3 file.
Mark delta data as new or updated. Export this to database as insert or update.
As time goes by this file will get bigger and bigger. Define a date range beyond which updated data won’t be coming. Regularly truncate this file to keep data within that time range.
You can also bucket and partition this data. You can use deltalake to maintain it too.
One downside is that whenever database is updated this file may need to be updated based on relevant data is being Changed or not. You can maintain a marker on the database table to signify sync date. Index that column too. Read changed records based on this column and update the file/deltalake.
This way your sparl app will be less dependent on a database. The database operations are not very scalable so keeping them away from critical path is better
Shouldnt you have a last update time in you DB? The approach you are using doesnt sound scalable so if you had a way to set update time to each row in the table it will solve the problem.
I'm currently working on my first application that uses a database so I'm very new to this. The database has multiple tables that are what you would expect to normally see.
However, I created one table which only has one row and one column used to keep a count of the total items processed by the program so it's available to access elsewhere. I can't just use
SELECT COUNT(*) FROM table_name
because these items that I am processing I do not want to actually keep in a table.
It seems like a waste to use a table to store one value so I am wondering if there a better way to keep track of this value.
What is your table storing? it's storing some kind of processing audit. So make it a little more useful - add a column storing the last datetime that the data was processed. Add a column for the time it took to process. Add another column which stores the username (or some identifier) of whoever ran the process. Now add a row for every table that is processed (there's only one now but there might be more in future). Try and envisage how your processing is going to grow in future
I'm using Google's Cloud Storage & BigQuery. I am not a DBA, I am a programmer. I hope this question is generic enough to help others too.
We've been collecting data from a lot of sources and will soon start collecting data real-time. Currently, each source goes to an independent table. As new data comes in we append it into the corresponding existing table.
Our data analysis requires each record to have a a timestamp. However our source data files are too big to edit before we add them to cloud storage (4+ GB of textual data/file). As far as I know there is no way to append a timestamp column to each row before bringing them in BigQuery, right?
We are thus toying with the idea of creating daily tables for each source. But don't know how this will work when we have real time data coming in.
Any tips/suggestions?
Currently, there is no way to automatically add timestamps to a table, although that is a feature that we're considering.
You say your source files are too big to edit before putting in cloud storage... does that mean that the entire source file should have the same timestamp? If so, you could import to a new BigQuery table without a timestamp, then run a query that basically copies the table but adds a timestamp. For example, SELECT all,fields, CURRENT_TIMESTAMP() FROM my.temp_table (you will likely want to use allow_large_results and set a destination table for that query). If you want to get a little bit trickier, you could use the dataset.DATASET pseudo-table to get the modified time of the table, and then add it as a column to your table either in a separate query or in a JOIN. Here is how you'd use the DATASET pseudo-table to get the last modified time:
SELECT MSEC_TO_TIMESTAMP(last_modified_time) AS time
FROM [publicdata:samples.__DATASET__]
WHERE table_id = 'wikipedia'
Another alternative to consider is the BigQuery streaming API (More info here). This lets you insert single rows or groups of rows into a table just by posting them directly to bigquery. This may save you a couple of steps.
Creating daily tables is a reasonable option, depending on how you plan to query the data and how many input sources you have. If this is going to make your queries span hundreds of tables, you're likely going to see poor performance. Note that if you need timestamps because you want to limit your queries to certain dates and those dates are within the last 7 days, you can use the time range decorators (documented here).
We currently have a search on our website that allows users to enter a date range. The page calls a stored procedure that queries for the date range and returns the appropriate data. However, a lot of our tables contain 30m to 60m rows. If a user entered a date range of a year (or some large range), the database would grind to a halt.
Is there any solution that doesn't involve putting a time constraint on the search? Paging is already implemented to show only the first 500 rows, but the database is still getting hit hard. We can't put a hard limit on the number of results returned because the user "may" need all of them.
If the user inputed date range is to large, have your application do the search in small date range steps. Possibly using a slow start approach: first search is limited to, say one month range and if it bings back less than the 500 rows, search the two preceding months until you have 500 rows.
You will want to start with most recent dates for descending order and with oldest dates for ascending order.
It sounds to me like this is a design and not a technical problem. No one ever needs millions of records of data on the fly.
You're going to have to ask yourself some hard questions: Is there another way of getting people their data than the web? Is there a better way you can ask for filtering? What exactly is it that the users need this information for and is there a way you can provide that level of reporting instead of spewing everything?
Reevaluate what it is that the users want and need.
We can't put a hard limit on the
number of results returned because the
user "may" need all of them.
You seem to be saying that you can't prevent the user from requesting large datasets for business reasons. I can't see any techical way around that.
Index your date field and force a query to use that index:
CREATE INDEX ix_mytable_mydate ON mytable (mydate)
SELECT TOP 100 *
FROM mytable WITH (INDEX ix_mytable_mydate)
WHERE mydate BETWEEN #start and #end
It seems that the optimizer chooses FULL TABLE SCAN when it sees the large range.
Could you please post the query you use and execution plan of that query?
Don't know which of these are possible
Use a search engine rather than a database?
Don't allow very general searches
Cache the results of popular searches
Break the database into shards on separate servers, combine the results on your application.
Do multiple queries with smaller date ranges internally
It sounds like you really aren't paging. I would have the stored procedure take a range (which you calculated) for the pages and then only get those rows for the current page. Assuming that the data doesn't change frequently, this would reduce the load on the database server.
How is your table data physically structured i.e. partitioned, split across Filegroups and disk storage etc. ?
Are you using table partitioning? If not you should look into using aligned partitioning. You could partition your data by date, say a partition for each year as an example.
Where I to request a query spanning three years, on a multiprocessor system, I could concurrently access all three partitions at once, thereby improving query performance.
How are you implementing the paging?
I remember I faced a problem like this a few years back and the issue was to do with how I implemented the paging. However the data that I was dealing with was not as big as yours.
Parallelize, and put it in ram (or a cloud). You'll find that once you want to access large amounts of data at the same time, rdbms become the problem instead of the solution. Nobody doing visualizations uses a rdbms.
I am trying to select 100s of rows at a DB that contains 100000s of row and update those rows afters.
the problem is I don't want to go to DB twice for this purpose since update only marks those rows as "read".
is there any way I can do this in java using simple jdbc libraries? (hopefully without using stored procedures)
update: ok here is some clarification.
there are a few instance of same application running on different servers, they all need to select 100s of "UNREAD" rows sorted according to creation_date column, read blob data within it, write it to file and ftp that file to some server. (I know prehistoric but requirements are requirements)
The read and update part is for to ensure each instance getting diffent set of data. (in order, tricks like odds and evens wont work :/)
We select data for update. the data transfers through the wire (we wait and wait) and then we update them as "READ". then release lock for reading. this entire thing takes too long. By reading and updating at the same time, I would like to reduce lock time (from time we use select for update to actual update) so that using multiple instances would increase read rows per second.
Still have ideas?
It seems to me there might be more than one way to interpret the question here.
You are selecting the rows for the
sole purpose of updating them and
not reading them.
You are selecting the rows to show
to somebody, and marking them as
read either one at a time or all as a group.
You want to select the rows and mark
them as read at the time you select
them.
Let's take Option 1 first, as that seems to be the easiest. You don't need to select the rows in order to update them, just issue an update with a WHERE clause:
update table_x
set read = 'T'
where date > sysdate-1;
Looking at option 2, you want to mark them as read when a user has read them (or a down stream system has received it, or whatever). For this, you'll probably have to do another update. If you query for the primary key, in addition to the other columns you'll need in the first select, you will probably have an easier time of updating, as the DB won't have to do table or index scans to find the rows.
In JDBC (Java) there is a facility to do a batch update, where you execute a set of updates all at once. That's worked out well when I need to perform a lot of updates that are of the exact same form.
Option 3, where you want to select and update all in one shot. I don't find much use for this, personally, but that doesn't mean others don't. I suppose some kind of stored procedure would reduce the round trips. I'm not sure what db you are working with here and can't really offer specifics.
Going to the DB isn't so bad. If you aren't returning anything 'across the wire' then an update shouldn't do you too much damage and its only a few hundred thousand rows. What is your worry?
If you're doing a SELECT in JDBC and iterating over the ResultSet to UPDATE each row, you're doing it wrong. That's an (n+1) query problem that will never perform well.
Just do an UPDATE with a WHERE clause that determines which of those rows needs to be updated. It's a single network round trip that way.
Don't be too code-centric. Let the database do the job it was designed for.
Can't you just use the same connection without closing it?