Which one is better (performance wise and operation on the long run) in maintaining data loaded, managed or external?
And by maintaining, i mean that these tables will have the following operations on daily basis frequently;
Select using partitions most of the time.. but for some of it they are not used.
Delete specific records, not all the partition (for example found a problem in some columns and want to delete and insert it again). - i am not sure if this supported for normal tables, unless transactional is used.
Most important, The need to merge files frequently.. may be twice a day to merge small files to gain less mappers. I know concate is available on managed and insert overwrite on external.. which one is less cost?
It depends on your use case. External table is recommended when they are used across multiple application for example Along with hive pig or other application is also used for processing the data in this kind of scenario external tables are mainly recommended.They are used when you are mainly reading data.
While in case of managed tables hive have complete control over the data. Though you can convert any external table to managed and vice versa
alter table table_name SET TBLPROPERTIES('EXTERNAL'='TRUE');
As in your case you are doing frequent modifications in data so it is better that hive should have total control over the data. In this scenraio it is recommended to use Managed tables.
Apart from that managed table are more secure then external table because external table can be accessed by anyone. While in managed table you can implement hive level security which provided better control but in case of external you will have to implement HDFS level security.
You can refer the below links which can give you few pointers in considerations
External Vs Managed tables comparison
I’m in the middle of trying to migrate a large amount of data into a oracle database from existing excel-files.
Due to the large amount of rows loaded (10 000 and more) every time, it is not possible to use SQL Developer for this tasks.
In every work-sheet there’s data that need to go into different tables, but at the same time keep the relations and not dropping any data.
As for now, I use one .CSV file for each table and mapping them together afterwards. This is thou combined with a great risk of adding the wrong FK and with that screw up the hole shit. And I don’t have the time, energy or will for clean ups even if it is my own mess…
My initial thought was if I could bulk transfer with sql loader using some kind of plsql-script in maybe an ctl-file (the used for mapping the properties) but it seems like I.m quite out in the bush with that one… (or am I…? )
The other thought was to create a simple program In c# and use fastMember and load the database that way. (But that means that I need to take the time to actually make the program, however small it is).
I can’t possible be the only one that have had this issue, but trying to us my notToElevatedNinjaGoogling-skills ends up with either using sql developer (witch is not an alternative) or the bulk copy thing from sql load (and where I need to map it all together afterwards).
Is there any alternative solutions for my problem or is the above solutions the one that I need to cope with?
Did you consider using CSV files as external tables? As they act as if they were ordinary Oracle tables, you can write (PL/)SQL against them, inserting data into different tables in the target schema. That might give you some more freedom & control over what you are doing.
Behind the scene, it is still SQL*Loader.
I am new to Teradata & fortunately got a chance to work on both DDL-DML statements.
One thing I observed is Teradata is very slow when time comes to UPDATE the data in a table having large number of records.
The simplest way I found on the Google to perform this update is to write an INSERT-SELECT statement with a CASE on column holding values to be update with new values.
But what when this situation arrives in Data Warehouse environment, when we need to update multiple columns from a table holding millions of rows ?
Which would be the best approach to follow ?
INSERT-SELECT only OR MERGE-UPDATE OR MLOAD ?
Not sure if any of the above approach is not used for this UPDATE operation.
Thank you in advance!
At enterprise level, we expect volumes to be huge and updates are often part of some scheduled jobs/scripts.
With huge volume of data, Updates comes as a costly operation that involve risk of blocking table for some time in case the update fails (due to fallback journal). Although scripts are tested well, and failures seldom happen in production environments, it's always better to have data that needs to be updated loaded to a temporary table in required form and inserted back to same table after deleting matching records to maintain SCD-1 (Where we don't maintain history).
I'm trying to make faster dashboards in Tableau by creating views of my calculations directly in BigQuery.
Based on my understating if the gcloud documentation here, the view will re-execute the query once it is accessed, so it kinda defeats my goal.*
*My goal is to eliminate calculations on the fly, be it in Tableau or BigQuery.
Is it possible to "save" these views, by way of scheduled scripts or workflows?
Thanks,
A view is best thought of as a way to reformat a table to make it look more convenient to further queries. The query still has to run on BigQuery so the benefits will be that the view may look simpler to Tableau than the raw table (particularly convenient if the view uses some complex SQL to create some of its columns). But it won't save calculation time.
But, if your view is doing some complex consolidation of a larger table then it might be worth saving the results as a new table instead of creating a view. This is OK if your underlying table doesn't change frequently (rule of thumb if you use the results every day and the table changes weekly, it is probably worthwhile and certainly so if the changes are monthly). Then Tableau will be querying pre-consolidated results rather than the much larger raw table. BigQuery storage and processing is cheap so this is often a reasonable solution.
Another alternative is to use a Tableau extract to bring the data into your local drive or server. This is only practical if the table is small enough to fit locally and will only work really well for speed if it fits into local memory (which can be a lot more than you might think). But extracts, at least on Tableau server, can be set to refresh on a schedule, making much faster user interaction and absolving you of having to remember to manually update the consolidated table.
I'm about to have to rewrite some rather old code using SQL Server's BULK INSERT command because the schema has changed, and it occurred to me that maybe I should think about switching to a stored procedure with a TVP instead, but I'm wondering what effect it might have on performance.
Some background information that might help explain why I'm asking this question:
The data actually comes in via a web service. The web service writes a text file to a shared folder on the database server which in turn performs a BULK INSERT. This process was originally implemented on SQL Server 2000, and at the time there was really no alternative other than chucking a few hundred INSERT statements at the server, which actually was the original process and was a performance disaster.
The data is bulk inserted into a permanent staging table and then merged into a much larger table (after which it is deleted from the staging table).
The amount of data to insert is "large", but not "huge" - usually a few hundred rows, maybe 5-10k rows tops in rare instances. Therefore my gut feeling is that BULK INSERT being a non-logged operation won't make that big a difference (but of course I'm not sure, hence the question).
The insertion is actually part of a much larger pipelined batch process and needs to happen many times in succession; therefore performance is critical.
The reasons I would like to replace the BULK INSERT with a TVP are:
Writing the text file over NetBIOS is probably already costing some time, and it's pretty gruesome from an architectural perspective.
I believe that the staging table can (and should) be eliminated. The main reason it's there is that the inserted data needs to be used for a couple of other updates at the same time of insertion, and it's far costlier to attempt the update from the massive production table than it is to use an almost-empty staging table. With a TVP, the parameter basically is the staging table, I can do anything I want with it before/after the main insert.
I could pretty much do away with dupe-checking, cleanup code, and all of the overhead associated with bulk inserts.
No need to worry about lock contention on the staging table or tempdb if the server gets a few of these transactions at once (we try to avoid it, but it happens).
I'm obviously going to profile this before putting anything into production, but I thought it might be a good idea to ask around first before I spend all that time, see if anybody has any stern warnings to issue about using TVPs for this purpose.
So - for anyone who's cozy enough with SQL Server 2008 to have tried or at least investigated this, what's the verdict? For inserts of, let's say, a few hundred to a few thousand rows, happening on a fairly frequent basis, do TVPs cut the mustard? Is there a significant difference in performance compared to bulk inserts?
Update: Now with 92% fewer question marks!
(AKA: Test Results)
The end result is now in production after what feels like a 36-stage deployment process. Both solutions were extensively tested:
Ripping out the shared-folder code and using the SqlBulkCopy class directly;
Switching to a Stored Procedure with TVPs.
Just so readers can get an idea of what exactly was tested, to allay any doubts as to the reliability of this data, here is a more detailed explanation of what this import process actually does:
Start with a temporal data sequence that is ordinarily about 20-50 data points (although it can sometimes be up a few hundred);
Do a whole bunch of crazy processing on it that's mostly independent of the database. This process is parallelized, so about 8-10 of the sequences in (1) are being processed at the same time. Each parallel process generates 3 additional sequences.
Take all 3 sequences and the original sequence and combine them into a batch.
Combine the batches from all 8-10 now-finished processing tasks into one big super-batch.
Import it using either the BULK INSERT strategy (see next step), or TVP strategy (skip to step 8).
Use the SqlBulkCopy class to dump the entire super-batch into 4 permanent staging tables.
Run a Stored Procedure that (a) performs a bunch of aggregation steps on 2 of the tables, including several JOIN conditions, and then (b) performs a MERGE on 6 production tables using both the aggregated and non-aggregated data. (Finished)
OR
Generate 4 DataTable objects containing the data to be merged; 3 of them contain CLR types which unfortunately aren't properly supported by ADO.NET TVPs, so they have to be shoved in as string representations, which hurts performance a bit.
Feed the TVPs to a Stored Procedure, which does essentially the same processing as (7), but directly with the received tables. (Finished)
The results were reasonably close, but the TVP approach ultimately performed better on average, even when the data exceeded 1000 rows by a small amount.
Note that this import process is run many thousands of times in succession, so it was very easy to get an average time simply by counting how many hours (yes, hours) it took to finish all of the merges.
Originally, an average merge took almost exactly 8 seconds to complete (under normal load). Removing the NetBIOS kludge and switching to SqlBulkCopy reduced the time to almost exactly 7 seconds. Switching to TVPs further reduced the time to 5.2 seconds per batch. That's a 35% improvement in throughput for a process whose running time is measured in hours - so not bad at all. It's also a ~25% improvement over SqlBulkCopy.
I am actually fairly confident that the true improvement was significantly more than this. During testing it became apparent that the final merge was no longer the critical path; instead, the Web Service that was doing all of the data processing was starting to buckle under the number of requests coming in. Neither the CPU nor the database I/O were really maxed out, and there was no significant locking activity. In some cases we were seeing a gap of a few idle seconds between successive merges. There was a slight gap, but much smaller (half a second or so) when using SqlBulkCopy. But I suppose that will become a tale for another day.
Conclusion: Table-Valued Parameters really do perform better than BULK INSERT operations for complex import+transform processes operating on mid-sized data sets.
I'd like to add one other point, just to assuage any apprehension on part of the folks who are pro-staging-tables. In a way, this entire service is one giant staging process. Every step of the process is heavily audited, so we don't need a staging table to determine why some particular merge failed (although in practice it almost never happens). All we have to do is set a debug flag in the service and it will break to the debugger or dump its data to a file instead of the database.
In other words, we already have more than enough insight into the process and don't need the safety of a staging table; the only reason we had the staging table in the first place was to avoid thrashing on all of the INSERT and UPDATE statements that we would have had to use otherwise. In the original process, the staging data only lived in the staging table for fractions of a second anyway, so it added no value in maintenance/maintainability terms.
Also note that we have not replaced every single BULK INSERT operation with TVPs. Several operations that deal with larger amounts of data and/or don't need to do anything special with the data other than throw it at the DB still use SqlBulkCopy. I am not suggesting that TVPs are a performance panacea, only that they succeeded over SqlBulkCopy in this specific instance involving several transforms between the initial staging and the final merge.
So there you have it. Point goes to TToni for finding the most relevant link, but I appreciate the other responses as well. Thanks again!
I don't really have experience with TVP yet, however there is an nice performance comparison chart vs. BULK INSERT in MSDN here.
They say that BULK INSERT has higher startup cost, but is faster thereafter. In a remote client scenario they draw the line at around 1000 rows (for "simple" server logic). Judging from their description I would say you should be fine with using TVP's. The performance hit - if any - is probably negligible and the architectural benefits seem very good.
Edit: On a side note you can avoid the server-local file and still use bulk copy by using the SqlBulkCopy object. Just populate a DataTable, and feed it into the "WriteToServer"-Method of an SqlBulkCopy instance. Easy to use, and very fast.
The chart mentioned with regards to the link provided in #TToni's answer needs to be taken in context. I am not sure how much actual research went into those recommendations (also note that the chart seems to only be available in the 2008 and 2008 R2 versions of that documentation).
On the other hand there is this whitepaper from the SQL Server Customer Advisory Team: Maximizing Throughput with TVP
I have been using TVPs since 2009 and have found, at least in my experience, that for anything other than simple insert into a destination table with no additional logic needs (which is rarely ever the case), then TVPs are typically the better option.
I tend to avoid staging tables as data validation should be done at the app layer. By using TVPs, that is easily accommodated and the TVP Table Variable in the stored procedure is, by its very nature, a localized staging table (hence no conflict with other processes running at the same time like you get when using a real table for staging).
Regarding the testing done in the Question, I think it could be shown to be even faster than what was originally found:
You should not be using a DataTable, unless your application has use for it outside of sending the values to the TVP. Using the IEnumerable<SqlDataRecord> interface is faster and uses less memory as you are not duplicating the collection in memory only to send it to the DB. I have this documented in the following places:
How can I insert 10 million records in the shortest time possible? (lots of extra info and links here as well)
Pass Dictionary<string,int> to Stored Procedure T-SQL
Streaming Data Into SQL Server 2008 From an Application (on SQLServerCentral.com ; free registration required)
TVPs are Table Variables and as such do not maintain statistics. Meaning, they report only having 1 row to the Query Optimizer. So, in your proc, either:
Use statement-level recompile on any queries using the TVP for anything other than a simple SELECT: OPTION (RECOMPILE)
Create a local temporary table (i.e. single #) and copy the contents of the TVP into the temp table
I think I'd still stick with a bulk insert approach. You may find that tempdb still gets hit using a TVP with a reasonable number of rows. This is my gut feeling, I can't say I've tested the performance of using TVP (I am interested in hearing others input too though)
You don't mention if you use .NET, but the approach that I've taken to optimise previous solutions was to do a bulk load of data using the SqlBulkCopy class - you don't need to write the data to a file first before loading, just give the SqlBulkCopy class (e.g.) a DataTable - that's the fastest way to insert data into the DB. 5-10K rows isn't much, I've used this for up to 750K rows. I suspect that in general, with a few hundred rows it wouldn't make a vast difference using a TVP. But scaling up would be limited IMHO.
Perhaps the new MERGE functionality in SQL 2008 would benefit you?
Also, if your existing staging table is a single table that is used for each instance of this process and you're worried about contention etc, have you considered creating a new "temporary" but physical staging table each time, then dropping it when it's finished with?
Note you can optimize the loading into this staging table, by populating it without any indexes. Then once populated, add any required indexes on at that point (FILLFACTOR=100 for optimal read performance, as at this point it will not be updated).
Staging tables are good! Really I wouldn't want to do it any other way. Why? Because data imports can change unexpectedly (And often in ways you can't foresee, like the time the columns were still called first name and last name but had the first name data in the last name column, for instance, to pick an example not at random.) Easy to research the problem with a staging table so you can see exactly what data was in the columns the import handled. Harder to find I think when you use an in memory table. I know a lot of people who do imports for a living as I do and all of them recommend using staging tables. I suspect there is a reason for this.
Further fixing a small schema change to a working process is easier and less time consuming than redesigning the process. If it is working and no one is willing to pay for hours to change it, then only fix what needs to be fixed due to the schema change. By changing the whole process, you introduce far more potential new bugs than by making a small change to an existing, tested working process.
And just how are you going to do away with all the data cleanup tasks? You may be doing them differently, but they still need to be done. Again, changing the process the way you describe is very risky.
Personally it sounds to me like you are just offended by using older techniques rather than getting the chance to play with new toys. You seem to have no real basis for wanting to change other than bulk insert is so 2000.