Category: Columnstore

Niko Neugebauer is somewhat impressed with an update to sp_estimate_data_compression_savings:

The final results were pretty much amazing – 2.1 GB, making the estimation of the sys.sp_estimate_data_compression_savings System Stored Procedure much more precise then my own function! This leaves me very happy and makes me want to investigate and learn how this new stored procedure is capable of providing better estimations.

I decided to test on the other tables within TPCH database and my test on the Orders table have shown a different situation where the 0.7 GB estimation of the sys.sp_estimate_data_compression_savings Stored Procedure were pretty much offbeat when comparing to the CISL dbo.cstore_sp_estimate_columnstore_compression_savings – showing 0.92 GB while the end result was 0.89 GB.
I guess the final answer is that it will depend, but that the estimation of the new stored procedure are not totally offbeat is an incredibly good sign, though I would still keep an eye or even two on the provided estimated results.

Read the whole thing for more details on these examples.

Teo Lachev makes a good point regarding columnar databases:

A large company uses the SAP HANA ERP system. Users requires real-time access to transactional data. To avoid performance degradation, SLT replication (trigger-based change data capture) replicates data to another SAP HANA system that is used solely for reporting. The problem is that the more detailed the report gets and the more columns it has, the slower it gets and SAP HANA throws out of memory exceptions.

SAP HANA is an in-memory columnar database like Tabular. So, it stores data in columns, not rows. Columnar databases are primarily designed for analytical reports which typically have a few columns (sales by customer, product, date), but can potentially aggregate large datasets. As the reporting grain lowers and more columns are added (order number, order line item, customer name, phone number, etc.), a columnar database has to cross-join more and more columns. This is not efficient and performance quickly degrades irrespective that storage is fast. SSAS Tabular and Power BI are no different. SAP HANA complicates the issue further by preventing direct access to tables and requiring “analytical” views that join tables and potentially nest other views.

Read the whole thing.  Teo has a great point:  there are trade-offs between different data platform technologies, and choosing the right one is important.

Sunil Agarwal provides a pattern for trickle loading clustered columnstore indexes:

A traditional scenario of loading data into CCI is a nightly load from one or more data files containing millions of rows. Recommended technique is to load the data with batchsize >= 102400 as explained https://blogs.msdn.microsoft.com/sqlserverstorageengine/2014/07/27/clustered-column-store-index-bulk-loading-the-data/. However, we are seeing many scenarios where data source is parallel data stream (i.e. trickle insert) to be loaded to CCI for analytics, a typical IOT scenario. CCI allows concurrent data streams into the same delta rowgroup. However, you will see higher page latch contention as you increase the concurrency. You may wonder why this is so? Well, each delta RG is organized internally as a clustered btree index and the dataload follows the pattern of monotonically increasing clustered index key causing latch contention on the last page.

Check out Sunil’s post.  I also have an older post covering my experiences with CCI trickle loads and three ETL patterns which can work.

Niko Neugebauer has some documentation on important wait stats around columnstore:

I split the known wait types into the following distinct groups:
– Batch Execution Mode (HTBUILD, HTDELETE, HTMEMO, HTREINIT, HTREPARTITION, PWAIT_QRY_BPMEMORY, BPSORT)
– Columnstore Indexes (ROWGROUP_VERSION, ROWGROUP_OP_STATS, SHARED_DELTASTORE_CREATION, COLUMNSTORE_BUILD_THROTTLE, COLUMNSTORE_MIGRATION_BACKGROUND_TASK, COLUMNSTORE_COLUMNDATASET_SESSION_LIST)

With an appearance in the next SQL Server version of the Batch Execution Mode for the RowStore Indexes, the first group of the waits will suddenly be becoming more important for every single SQL Server user and mixing it together with the Wait Types specific to the internal structures and functions of the Columnstore Indexes makes no sense.

Read on to learn more about these important wait types.

Joe Obbish has another post looking at sub-optimal columnstore index performance:

It is possible to see a scalability bottleneck in the form of high average wait time for the RESERVED_MEMORY_ALLOCATION_EXT wait if a highly concurrent workload is run on a server that consumes memory with batch mode operators. I believe that the severity of the bottleneck depends on unknown factors in the server’s initial memory state and the rate of memory actually used by queries to run batch mode operations. This blog post shares a reproduction of the issue along with a call to action.

If you use clustered columnstore indexes, check out Joe’s User Voice entry.

Niko Neugebauer tests whether merge replicated tables can use columnstore indexes:

Adding this table to the publication will end up with the following, self-explaining error message, being very clear that the Clustered Columnstore Indexes are not supported for the Merge Replication[.]

There is no surprise here, as the same Clustered Columnstore Indexes are not supported for the Transactional Replication, but I feel that a great opportunity is lost and the Replication technology are being quite ignored by the emerged technologies, such as In-Memory & Columnstore, where the scenarios of replicating the Data Warehousing data is something that a lot of people can find very useful.

I wish it would be otherwise, and this would allow to bring more customers to use Columnstore Indexes.

Clustered columnstore indexes aren’t possible, but read on to learn whether non-clustered columnstore indexes are supported.