HANA Partitioning – 2 billion rows limitation – Pa...

jgleichmann · ‎02-09-2020

last updated: 2022-11-04

It took me nearly two years to write the second part of this partition series. The first one for BW partitioning can be found here.

The main question of this blog: How to determine the best partitioning combination?

In an ERP or S/4 HANA system it is possible that a table is approaching the 2 billion mark. This is a HANA design limit for a column store table (per partition). In this case you have to partition the table. But different than on a BW system there are no standard rules for a partitioning. This means you have to choose a partitioning rule by your own. But how to determine the best partitioning combination?

Alerts

Find tables with high amount of records

Term clarification

Designing Partitions

Example BSEG

Example BSIS

Example for a Z-Table <tbd>

Parameter for parallel splitting

Partitioning process

Tips

Partition / Re-partitioning references

Summary

1. Alerts

At first check if partitioning is needed at all which means check the tables with the most row entries. There is a limit per table/partition of 2 billion rows by the design of HANA.

If your alerting is working correct you will receive an alert (ID 27). It looks like this:

“Record count of column-store table partitions ( ID 27 )

Determines the number of records in the partitions of column-store tables. A table partition cannot contain more than 2,147,483,648 (2 billion) rows.”

Interval 1 hour

High: 1,900,000,000

Medium: 1,800,000,000

Low: 1,500,000,000

The limitations are described in SAP note 2154870.

Other once:

"Alert ID 20 - Table growth of non-partitioned column-store tables"

"Alert ID 17 - Record count of non-partitioned column-store tables"

2. Find tables with high amount of records

Check all tables by high amount of records. Therefore, you can use the SQL from 1969700 (HANA_Tables_TopGrowingTables_Records_History).

Rule of thumb for the initial partitioning:

min. 100 mio entries per partition for initial partitioning

max 500 mio entries per partition for initial partitioning

if you choose too many partitions you can achieve a bad performance, because one thread per partition have to be triggered (e.g. you have 20 threads as statement concurrency limit and 40 partition have to be scanned which results in waiting for resources)

if you choose too less partitions, it can be that you have to repartition pretty timely which means another maintenance window / downtime

recommendation: HASH partitioning on a selective column, typically part of primary key

making sure that a single partition doesn't exceed a size of 20 to 50 GB due to delta merge performance (SAP Note 2100010)

In our example we determined BSEG and BSIS as tables with high amount of rows. For timely planning of maintenance you should act when the table exceeds 1,5 billion entries.

For our scenario BSEG has 4,5 billion and BSIS has 4,1 billion rows. So, they are already partitioned. But is this partitioning over the time since the initial creation still optimal?

Growth over 30 days:

3. Term clarification

To determine the optimal partitioning combination, we have to clarify some terms:

Cardinality

Selectivity

HASH Partitioning

Range Partitioning

Multi-level partitioning

Cardinality

In the context of databases, cardinality refers to the uniqueness of data values contained in a column. High cardinality means that the column contains a large percentage of totally unique values. Low cardinality means that the column contains a lot of “repeats” in its data range. In a customer table, a low cardinality column would be the “Gender” column. This column will likely only have “M” and “F” as the range of values to choose from, and all the thousands or millions of records in the table can only pick one of these two values for this column. Ok, to be accurate nowadays we have to add a third value for diverse “D”.

Source: https://www.techopedia.com/definition/18/cardinality-databases

Note: Partitioning works better when the cardinality of the partitioning field is not too high

Selectivity

The selectivity basically is a measure of how much variety there is in the values of a given table column in relation to the total number of rows in a given table. The cardinality is just part of the formula that is used to calculate the selectivity.

Selectivity = cardinality/(number of rows) * 100

Hash Partitioning

Hash partitioning is used to distribute rows to partitions equally for load balancing. The number of the assigned partition is computed by applying a hash function to the value of a specified column. Hash partitioning does not require an in-depth knowledge of the actual content of the table.

Source: openHPI: In-Memory_Data_Management_2017

Range Partitioning

Range partitioning creates dedicated partitions for certain values or value ranges in a table. For example, a range partitioning scheme can be chosen to create a partition for each calendar month. Partitioning requires an in-depth knowledge of the values that are used or are valid for the chosen partitioning column.

Partitions may be created or dropped as needed and applications may choose to use range partitioning to manage data at a fine level of detail, for example, an application may create a partition for an upcoming month so that new data is inserte

Source: openHPI: In-Memory_Data_Management_2017

Advantage: possible dynamic range to use this generic model for 'no maintenance' partitioning. For this feature, which is only supported for range partitioning, partitions will be automatically created if a threshold is reached:

With SPS06 it is also possible to do this for defined time intervals.

ALTER TABLE T ALTER PARTITION OTHERS DYNAMIC INTERVAL 1 YEAR;

ALTER TABLE T ALTER PARTITION OTHERS DYNAMIC INTERVAL 3 MONTH;

Multi-Level partitioning

In some scenarios it makes sense to use colums which are not part of the primary key. But with single level partitioning you can only select primary key columns. Multi-level partitioning makes it possible to select on first level a key column and on second level any other column. It is also possible to mix different partitioning methods.

The following combinations are possible [firstlevel-secondlevel]:

hash-hash

range-range

hash-range

round-robin-range

4. Designing Partitions

Actually the online repartitioning is based on table replication. Tables with the naming convention _SYS_OMR_<source_table>#<id> are used as interim tables during online repartitioning operations. For details please read the “Designing Partitions” section in the documentation.

As summary:

Use partitioning columns that are often used in WHERE clauses for partition pruning

If you don’t know which partition scheme to use, start with hash partitioning

Use as many columns in the hash partitioning as required for good load balancing, but try to use only those columns that are typically used in requests

Queries do not necessarily become faster when smaller partitions are searched. Often queries make use of indexes and the table or partition size is not significant. If the search criterion is not selective though, partition size does matter.

Using time-based partitioning often involves the use of hash-range partitioning with range on a date column

If you split an index (SAP names the CS tables also as index), always use a multiple of the source parts (for example 2 to 4 partitions). This way the split will be executed in parallel mode and also does not require parts to be moved to a single server first.

Do not split/merge a table unless necessary.

Ideally tables have a time criterion in the primary key. This can then be used for time-based partitioning.

Single level partitioning limitation: the limitation of only being able to use key columns as partitioning columns (homogeneous partitioning)

the client (MANDT/MANDANT) as single attribute for partitioning is not recommended - only useful in multi level partitioning scenarios

5. Example BSEG

We determined that BSEG has 4,5 billion rows. Now we need details on which column the table has to be partitioned.

The recommendation of SAP is to use a HASH partitioning on BELNR in note 2044468.

Note 2289491 describes a range partitioning on BUKRS. As you can see it always depends on how your system is being used. Ok, let’s find it out in a test environment.

Now we know:

table has to be partitioned (4,5 billion rows)

most scanned columns

recommendation for partitioning by SAP

Questions to success:

what is the primary key of the table?

which columns are often selected?

which columns are often used in where clause

what is the cardinality of this columns?

how many rows are inside the tables?

how many partitions are needed?

2044468 - FAQ: SAP HANA Partitioning
2418299 - SAP HANA: Partitioning Best Practices / Examples for SAP Tables
2289491 - Best Practices for Partitioning of Finance Tables

To quote the HPI course on the partitioning details (In-Memory_Data_Management_2017):

“There are number of different optimization goals to be considered while choosing a suitable partitioning strategy. For instance, when optimizing for performance, it makes sense to have tuples of different tables, that are likely to be joined for further processing, on one server. This way the join can be done much faster due to optimal data locality, because there is no delay for transferring the data across the network. In contrast, for statistical queries like counts, tuples from one table should be distributed across as many nodes as possible in order to benefit from parallel processing."

To sum up, the best partitioning strategy depends very much on the specific use case.

The main challenge for hash-based partitioning is to choose a good hash function, that implicitly achieves locality or access improvements.

Primary key BSEG:

MANDT

BUKRS

BELNR

GJAHR

BUZEI

The following columns have a particularly high amount of scanned records (SQL: “HANA_Tables_ColumnStore_Columns_Statistics”, MIN_SCANNED_RECORDS_PER_S = 5000000😞

To achieve this just fill in your table inside the modification section

There are recommendations for the most famous SAP standard tables within note 2044468. If you are close to the standard and don’t use a lot of custom code try this partitioning recommendations first. If you have heavy load with own statement which uses a totally different execution plan you may have to determine your own partitioning columns. Be aware of the partitioning limits!

If you want to check the optimal partitioning combinations use this statement:

select statement_hash, statement_string, 

execution_count, total_execution_time from m_sql_plan_cache 

where statement_string like '%BSEG%' 

order by total_execution_time desc;

From the result you have to analyze the “where” clause and find a common pattern.

Take as few fields as needed. Normally fields like MATNR, BELNR or DOCNR are selective fields and well suited for partitioning.

For a deep dive you can additionally run the DataCollector with the selected statement_hash (HANA_SQL_StatementHash_DataCollector) within the statement collection (1969700).

Additionally, you can use the ColumnStore statistics (HANA_Tables_ColumnStore_Columns_Statistics_2.00.030+ ) which is also part of the SQL collection to analyze how many scans are on which column. If you are using multiple clients, it may be wise to add the field MANDT as partitioning criteria.

HANA_Data_ColumnValueCounter_CommandGenerator

SELECT TOP 100

  'BSEG' TABLE_NAME,

  S.*

FROM

( SELECT

    BUKRS,

    LPAD(COUNT(*), 11) OCCURRENCES,

    LPAD(TO_DECIMAL(COUNT(*) / SUM(COUNT(*)) OVER () * 100, 10, 2), 7) PERCENT

  FROM

  ( SELECT

      BUKRS

    FROM

      "SAPSCHEMA"."BSEG

  )

  GROUP BY

    BUKRS

  ORDER BY

    OCCURRENCES DESC

) S

Only 2 values for BUKRS “0010” and “0020”. This means we have a too low cardinality and partitioning won’t work well on this attribute. So, this means both extreme too high and low are bad.

Most scanned columns are PCTR, HKONT and BELNR.

We have about 30 clients in this test system which means it makes sense to use it in the partitioning clause on the first level as entry point.

MANDT first level hash partitioning

BELNR second level hash partitioning

With 4,5 billion rows we need about 12-15 partitions which means 300 million rows per partition (rule of thumb: between 100 - 500 million), if we would use only single level partitioning. With multi-level partitioning the fill degree of the partitioning depends on the single column data cardinality of each level.

Note

At the end it is just a indication for the given analyzed timeframe and no guarantee for the future. This is a process you should repeat if the application and selections are changing. This can be due user behaviour or new SAP packages or custom code.

6. Example: BSIS

Primary Key

MANDT

BUKRS

HKONT

AUGDT

AUGBL

ZUONR

GJAHR

BELNR

BUZEI

Column Stats

often executed statements on BSIS

cardinality

Facts:

4,1 billion rows

About 30 client values

Only one value for BUKRS

The top occurrences in BELNR have summed up not 0,001%

The recommendation of SAP is a RANGE partitioning on BUKRS. If RANGE on BUKRS or a similar range partitioning is not possible, use HASH partitions on BELNR.

We can't use BUKRS as partitioning object due too low cardinality. BELNR alone has a high cardinality and could be not efficient enough.

2 recommendation is in this scenario:

MANDT first level range partitioning

HKONT second level range partitioning

MANDT first level hash partitioning

BELNR second level hash partitioning

With 4,1 billion rows we need about 12-14 partitions which means 300 million rows per partition (rule of thumb: between 100 - 500 million), if we would use only single partitioning. With multilevel partitioning the fill degree of the partitioning depends on the single column data cardinality of each level.

7. Another example for Z-table

<tbd>

8. Parameter for splitting

As already mentioned in the first part of this series, we can optimize the splitting procedure with some parameters which have to be adjusted according your hardware:

indexserver.ini -> [partitioning] -> split_threads (default: 16) [range: 1-128]

indexserver.ini -> [partitioning] -> bulk_load_threads (default: 4) [range: 1-20]

- Under a system with 176 CPU cores, the repartitioning was failed due to this error and completed in 9.18H with settings below.

indexserver.ini -> [partitioning] -> split_threads =50

indexserver.ini -> [partitioning] -> bulk_load_threads = 8

- With 120 CPU cores, it took 14.40H without the error after setting as below.

indexserver.ini -> [partitioning] -> split_threads =95

indexserver.ini -> [partitioning] -> bulk_load_threads = 10

Source:
Note 2890332

HANA 2.0 SPS04 Rev. >= 43

indexserver.ini -> [mergedog] -> auto_merge_decision_func = '((DRC*TMD > 3600*(MRC+0.0001)) or ((DMS>PAL/2000 or DMS > 1000 or DCC>100) and (DRC > MRC/100 or INSTR(BASENAME, "_SYS_OMR_") > 0)) or (DMR>0.2*MRC and DMR > 0.001) or (DRC=0 and DMR=0 and MPU=1))' 

indexserver.ini -> [table_replication] -> optimize_replica_for_online_ddl_in_log_replay = 'true'

indexserver.ini -> [table_replication] -> optimize_replica_for_online_ddl = 'true'

indexserver.ini -> [persistence] -> logreplay_savepoint_interval_s = '600'

Source:
Note 2874176

9. Partitioning process

You can monitor the partitioning process via M_JOB_PROGRESS monitoring view.

master process has in total 7 steps: Mapping, GetDataRanges, PreProcess, RepartitionMain, RepartitionDeltaAndMVCC, PostProcess, DropSources

Each worker group consists of 13 steps: CreateTargets, DeterminepartsMain, DeterminepartsDelta, InitPersistenceMain, InitPersistenceDelta, CheckConsistencyMain, CheckConsistencyDelta, RepartitionMainLocal, RepartitionDeltaLocal, RepartitionMVCCMain, RepartitionMVCCDelta, UpdateRuntime, Finalize

Each thread worker has 3 steps: SplitMergeAttributes, Save, Idle

Example:

Table : 80 columns

Indexes: PK + 2 secondary indexes

=> 83 attributes to split (+internal once)

CPU threads : 64vCPUs

If you have configured 60 split threads. One group will be started and use 60 threads. When one column is finished the next one is started. The bulk load threads won't be considered.

Grouping (repartitioning)

If you already have a partition design in place, but it is not fitting any more to your needs, you have to take attention for the repartitioning process to the grouping.

Example same as before only with the fact that is already partitioned with HASH 32.

old design: HASH 32

new design: HASH 4

32 can be divided by 4. This means 4 groups will be started. But if you stay with split threads 60 you will overload the system! So, go with 60/4 = 15. The system will start 4 groups with each 15 threads.

Note

old design / new design

or

new design = old design / 2

bulk_load_threads = old design / new design

split_threads = log. CPU / bulk_load_threads

10. Tips

Duration of a partitioning process

Export the table from production in binary format and import it in a separated system/schema to validate the duration of the partitioning process. Before you begin you should load and merge the table. This will speed up the procedure.

If you want to partition online you should consider the locking phase / delta merge phase. If those phases running long in your test system you should verify the impact with capture&replay or just do it offline (without application workload) or in times with less workload on the system. There are also some known issues regarding merges/partitioning locks. Please check your revision!

Dependency of the runtime

The runtime of the partitioning process depends on different factors:

number of fields/columns of the table (each column will be processed by one thread)

data type / length of the field

delta size

size of the column

cardinality of the column

composition of the PKEY

concat attributes

number of threads for partitioning (available CPU cores)

LOAD attribute PAGE (NSE)

Repartitioning process:

be sure that your new design is a factor 2 multiple or divider of the current number of partitions
- 4 => 8
- 6 => 3
- 32 => 4

adjust the parameters in order to the partition grouping

Because only in this case the repartitioning can happen in parallel on different partition groups and hosts (“parallel split / merge”).

You can speed up the procedure with some tuning of parameters or deleting indexes. This should always be tested carefully.

Attention

When you drop unique indexes you can't partition your table online! This would end in duplicate entries!!!

11. Partition / Re-partitioning references

3234063 - How to partition/re-partition a table

3111531 - SAP HANA - How to convert an existing hash partitioned table to range.

ALTER TABLE Statement (Data Definition)

Non-heterogeneous Alter Partition Clauses

Heterogeneous Alter Partition Clauses

12. Summary

There is no silver bullet for every customer. It depends always on the usage and the change of values in a table. Additionally the changes of the applications and the different selections. Try different partitioning columns in your test system and check the performance. You should also optimize the compression of those big tables from time to time. The optimize compression will take place automatically as part of a delta merge process, but decided from the system itself. On huge tables it can be that this happens pretty late which results in a bad performance.The partitioning can also have some positive impacts beside come across the record limitation. For instance better delta merge and record change performance.

So, stay tuned and give your system reguarly health checks. SAP provides such services called TPO, but also some well known HANA experts provide such services 😉