I'm trying to insert large number of records by selecting from a different table.
In the below example, BAR table has around 1 million records and trying to insert all those into FOO table. Is there a way I can do this efficiently with out the loader API or batch insert with JOOQ?
FYI, I'm trying to avoid the approach to load all the records in memory, so I'm not using the loader API which expects the JOOQRecords.
dslContext
.insertInto(FOO)
.columns(FOO.A, FOO.B)
.select(
select(A, B)
.from(BAR))
.execute();
This isn't strictly a jOOQ problem as you'd run into the same issues when writing the equivalent query in JDBC or even in a stored procedure. Such a bulk data transfer operation is usually the most efficient way to copy data between tables using SQL. There might be other tools available that bypass the SQL layer (e.g. pg_dump), but with SQL, this is optimal.
If you don't have enough resources to run everything in one go, you could partition your data set into several chunks using different techniques:
By transferring data of individual date ranges
By transferring data of individual ID ranges
By using keyset pagination
When partitioning your data as mentioned above, do also check if you can decrease the transaction size, e.g. to 1000 rows per commit. This isn't exact science, you'll have to find appropriate chunk and transaction sizes empirically for your specific system.
With all of these approaches, ACID is no longer guaranteed, so if your source data is modified during the move, you'll have to detect that somehow, and "fix it" (e.g by flagging rows that have been moved)
Or, just add more memory to the system.
Related
Let's say I have a database with lots of tables, but there's one big table that's being updated regularly. At any given point in time, this table contains billions of rows, and let's say that the table is updated so regularly that we can expect a 100% refresh of the table by the end of each quarter. So the volume of data being moved around is in the order tens of billions. Because this table is changing so constantly, I want to implement a PITR, but only for this one table. I have two options:
Hack PostgreSQL's in-house PITR to apply only for one table.
Build it myself by creating a base backup, set up continuous archiving, and using a python script to execute the log of SQL statements up to a point in time (or use PostgreSQL's EXECUTE statement to loop through the archive). The big con with this is that it won't have the timeline functionality.
My problem is, I don't know if option 1 is even possible, and I don't know if option 2 even makes sense (looping through billions of rows sounds like it defeats the purpose of PITR, which is speed and convenience.) What other options do I have?
I have created a table for reporting purpose where I am storing data for about 50 columns and at some time interval my scheduler executes a service which processes other tables and fill up data in my flat table.
Currently I am deleting and inserting data in that table But I want to know if this is the good practice or should I check every column in every row and update it if any change found and insert new record if data does not exists.
FYI, total number of rows which are being reinserted is 100k+.
This is a very broad question that can only really be answered with access to your environment and discussion on your personal requirements. Obviously this is not possible via Stack Overflow.
This means you will need to make this decision yourself.
The information you need to understand to be able to do this are the types of table updates available and how you can achieve them, normally referred to as Slowly Changing Dimensions. There are several different types, each with their own advantages, disadvantages and optimal use cases.
Once you understand the how of getting your data to incrementally update as required, you can then look at the why and whether the extra processing logic required to achieve this is actually worth it. Your dataset of a few hundred thousand rows of data is not large and probably may therefore not need this level of processing just yet, though that assessment will depend on how complex and time consuming your current process is and how long you have to run it.
It is probably faster to repopulate the table of 100k rows. To do an update, you still need to:
generate all the rows to insert
compare values in every row
update the values that have changed
The expense of updating rows is heavily on the logging and data movement operations at the data page level. In addition, you need to bring the data together.
If the update is updating a significant portion of rows, perhaps even just a few percent of them, then it is likely that all data pages will be modified. So the I/O is pretty similar.
When you simply replace the table, you will start by either dropping the table or truncating it. Those are relatively cheap operations because they are not logged at the row level. Then you are inserting into the table. Inserting 100,000 rows from one table to another should be pretty fast.
The above is general guidance. Of course, if you are only changing 3 rows in the table each day, then update is going to be faster. Or, if you are adding a new layer of data each day, then just an insert, with a handful of changed historical values might be a fine approach.
So, I have read that using internal tables increases the performance of the program and that we should make operations on DB tables as less as possible. But I have started working on a project that does not use internal tables at all.
Some details:
It is a scanner that adds or removes products in/from a store. First the primary key is checked (to see if that type of product exists) and then the product is added or removed. We use ‘Insert Into’ and ‘Delete From’ to add/remove the products directly from the DB table.
I have not asked why they do not use internal tables because I do not have a better solution so far.
Here’s what I have so far: Insert all products in an internal table, place the deleted products in another internal table.
Form update.
Modify zop_db_table from table gt_table." – to add all new products
LOOP AT gt_deleted INTO gs_deleted.
DELETE FROM zop_db_table WHERE index_nr = gs_deleted-index_nr.
ENDLOOP. " – to delete products
Endform.
But when can I perform this update?
I could set a ‘Save button’ to perform the update, but then there will be the risk that the user forgets to save large amounts of data, or drops the scanner, shutting it down or similar situations. So this is clearly not a good solution.
My final question is: Is there a (good) way to implement internal tables in a project like this?
internal tables should be used for data processing, like lists or arrays in other languages (c#, java...). From a performance and system load perspective it is preferred to first load all data you need into an internal table, then process that internal table instead of loading individual records from the database.
But that is mostly true for reporting, which is probably the most common type of custom abap program. You often see developers use select...endselect-statements, that in effect loop over a database table, transferring row after row to the report, one at a time. That is extremely slow compared to reading all records at once into an itab, then looping over the itab. More than once i've cut the execution time of a report down to a fraction by just eliminating roundtrips to the database.
If you have a good reason to read from the database or update records immediately, you should do so. If you can safely delay updates and deletes to a point in time where you can process all of them together, without risking inconsistencies, I'd consider than an improvement. But if there is a good reason (like consistency or data loss) to update immediately, do it.
Update: as #vwegert mentioned regarding the select-endselect statement, the statement doesn't actually create individual database queries for each row. The database interface of the application server optimizes the query, transferring rows in bulk to the application server. From there the records are transported to the abap report one by one (because in the report there is only the work area to store a single row), which has a significant performance impact especially for queries with large result sets. A select into an internal table can transport all rows directly to the abap report (as long as there is enough memory to hold them), as now there is the internal table to hold those records in the report.
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).
I have two models -
ChatCurrent - (which stores the messages for the current active chats)
ChatArchive - (which archives the messages for the chats that have ended)
The reason I'm doing this is so that the ChatCurrent table always has minimum number of entries, making querying the table fast (I don't know if this works, please let me know if I've got this wrong)
So I basically want to copy (cut) data from the ChatCurrent to the ChatArchive model. What would be the fastest way to do this. From what I've read online, it seems that I might have to execute a raw SQL query, if you would be kind enough to even state the Query I'll be grateful.
Additional details -
Both the models have the same schema.
My opinion is that today they are not reason to denormalize database in this way to improve performance. Indexes or partitioning + indexes should be enought.
Also, in case that, for semantic reasons, you prefer have two tables (models) like: Chat and ChatHistory (or ChatCurrent and ChatActive) as you say and manage it with django, I thing that the right way to keep consistence is to create ToArchive() method in ChatCurrent. This method will move chat entries to historical chat model. You can perform this operation in background mode, then you can thread the swap in a celery process, in this way online users avoid wait for request. Into celery process the fastest method to copy data is a raw sql. Remember that you can encapsulate sql into a stored procedure.
Edited to include reply to your comment
You can perform ChatCurrent.ToArchive() in ChatCurrent.save() method:
class ChatCurrent(model.Model):
closed=models.BooleanField()
def save(self, *args, **kwargs):
super(Model, self).save(*args, **kwargs)
if self.closed:
self.ToArchive()
def ToArchive(self):
from django.db import connection, transaction
cursor = connection.cursor()
cursor.execute("insert into blah blah")
transaction.commit_unless_managed()
#self.delete() #if needed (perhaps deleted on raw sql)
Try something like this:
INSERT INTO "ChatArchive" ("column1", "column2", ...)
SELECT "column1", "column2", ...
FROM "ChatCurrent" WHERE yourCondition;
and than just
DELETE FROM "ChatCurrent" WHERE yourCondition;
The thing you are trying to do is table partitioning.
Most databases support this feature without the need for manual book keeping.
Partitioning will also yield much better results than manually moving parts of the data to a different table. By using partitioning you avoid:
- Data inconsistency. Which is easy to introduce because you will move records in bulk and then remove a lot of them from the source table. It's easy to make a mistake and copy only a portion of the data.
- Performance drop - moving the data around and the associated overhead from transactions will generally neglect any benefit you got from reducing the size of the ChatCurrent table.
For a really quick rundown. Table partitioning allows you to tell the database that parts of the data are stored and retrieved together, this significantly speeds up queries as the database knows that it only has to look into a specific part of the data set. Example: chat's from the current day, last hour, last month etc. You can additionally store each partition on a different drive, that way you can keep your current chatter on a fast SSD drive and your history on regular slower disks.
Please refer to your database manual to know the details about how it handles partitioning.
Example for PostgreSQL: http://www.postgresql.org/docs/current/static/ddl-partitioning.html
Partitioning refers to splitting what is logically one large table into smaller physical pieces. Partitioning can provide several benefits:
Query performance can be improved dramatically in certain situations, particularly when most of the heavily accessed rows of the table are in a single partition or a small number of partitions. The partitioning substitutes for leading columns of indexes, reducing index size and making it more likely that the heavily-used parts of the indexes fit in memory.
When queries or updates access a large percentage of a single partition, performance can be improved by taking advantage of sequential scan of that partition instead of using an index and random access reads scattered across the whole table.
Bulk loads and deletes can be accomplished by adding or removing partitions, if that requirement is planned into the partitioning design. ALTER TABLE NO INHERIT and DROP TABLE are both far faster than a bulk operation. These commands also entirely avoid the VACUUM overhead caused by a bulk DELETE.
Seldom-used data can be migrated to cheaper and slower storage media.
def copyRecord(self,recordId):
emailDetail=EmailDetail.objects.get(id=recordId)
copyEmailDetail= CopyEmailDetail()
for field in emailDetail.__dict__.keys():
copyEmailDetail.__dict__[field] = emailDetail.__dict__[field]
copyEmailDetail.save()
logger.info("Record Copied %d"%copyEmailDetail.id)
As per the above solutions, don't copy over.
If you really want to have two separate tables to query, store your chats in a single table (and for preference, use all the database techniques here mentioned), and then have a Current and Archive table, whose objects simply point to Chat objects/