Redesigning Tablespaces in an Existing Database

Like many applications, WebSphere Commerce puts all tables in USERSPACE1 unless they need larger page sizes. This actually works just fine for smaller and midrange implementations, but we have about one build a year that requires something else – either because of standards that client DBAs adhere to or because they actually are busy enough for I/O and separate buffer pools to matter. I recently got the experience to set up a framework and plan for this for a larger client and thought I’d share a few of my methods and thoughts.

Guiding Principles

When embarking on a re-design, it is a considerable amount of effort. Because of this, it is important to have a clear idea of why you’re making any changes you are. Don’t separate things into separate tablespaces just for the fun of it or just because – make informed, data supported decisions.

The main reasons you may want to separate tables into different tablespaces include:

  1. A group of tables that can be restored together through tablespace backup and restore This really isn’t realistic in an IBM WebSphere Commerce database, because all of the tables are so interconnected that you’d have a hard time finding any 5 out of the 1,000 plus tables that can be restored without a host of other tables.
  2. Tables related to specific applications If you have different applications accessing a database and some of those tables are dedicated to a single application, it may make sense to separate them out.
  3. Separate I/O paths If you have the luxury of fully separate I/O paths, especially those with different speed characteristics (such as SSD vs. SATA), it may make sense to separate I/O either for individual tables or individual partitions of tables. You will need dedicated tablespaces, storage groups, and possibly table partitions to make this work.
  4. Different Bufferpool Requirements This is the one that came to my mind as what I would primarily focus on, because the storage characteristics are so varied between the various clients that I support. But memory is always at a premium as databases grow, and managing my bufferpools more exactly is something I would love to do.
  5. Managing LOB data and filesystem requirements LOBs can be particularly problematic for performance. Tables with LOBs perform better if their tablespaces are enabled for filesystem caching, but tables without LOBs behave better without filesystem caching enabled for their tablespaces.

If splitting tables into multiple tablespaces does not meet one of these goals, then it’s important to ask yourself why you’re doing it. Doing so adds complexity to administration. Also changes are getting easier and easier to make without an outage – ADMIN_MOVE_TABLE now supports RI – so consider that you may alter where tables are later in the life of your database.

Analyzing Existing Activity

Some of the goals for deciding what to split out come out of a DBA’s knowledge of the database, but others you can get hard numbers to support, based on querying an existing database. Obviously if you’re working with a new database, you won’t have this advantage, but even for existing databases, this level of design needs to be revisited periodically.

There are a number of different things and ways to look at the data here. Queries below will also make use of my blog entry on Detailed Analysis of Individual Performance Metrics.

Note that the queries below are against a running production B2C WebSphere Commerce database, but one that has an unusual pattern for orders, or lack thereof, so may not prove a good model to follow for other WebSphere Commerce databases.

Tables with High Read Activity

There are two ways to look at high read activity – both overall numbers as far as the most read tables in your database, and also the number of rows read compared to the cardinality of the table.

Overall Highest Rows Read

Here is one of the areas where I use standard deviation to analyze a particular metric. The query I use is this:

with rr_stats as (
    select  decimal(avg(rows_read),20,2) as avg_rr,
        decimal(stddev(rows_read),20,2) as stddev_rr,
        decimal(avg(rows_read) - 2 * stddev(rows_read),20,2) as avg_less_2_stddev,
        decimal(avg(rows_read) + 2 * stddev(rows_read),20,2) as avg_plus_2_stddev
    from table (mon_get_table(NULL,NULL,-1)) as t
    where t.tabschema='WSCOMUSR'
    substr(tabname,1,30) as tabname,
                when rows_read > rr_stats.avg_plus_2_stddev then 'High'
                when rows_read < rr_stats.avg_less_2_stddev then 'Low'
                else NULL
        end as flag,
from    table (mon_get_table(NULL,NULL,-1)) as t1,
where   t1.tabschema='REDACTED'
order by rows_read desc
with ur;

TABNAME                        ROWS_READ            AVG_RR                 FLAG STDDEV_RR              AVG_LESS_2_STDDEV      AVG_PLUS_2_STDDEV
------------------------------ -------------------- ---------------------- ---- ---------------------- ---------------------- ----------------------
ATTRVALDESC                            324723707514          1027698557.00 High         15841362471.73        -30655026386.46         32710423500.46
CATGPENREL                             319425104912          1027698557.00 High         15841362471.73        -30655026386.46         32710423500.46
CATGROUP                                67354582284          1027698557.00 High         15841362471.73        -30655026386.46         32710423500.46
ORDADJUST                               45602221591          1027698557.00 High         15841362471.73        -30655026386.46         32710423500.46
DMELEMENT                               43291418589          1027698557.00 High         15841362471.73        -30655026386.46         32710423500.46
SRCHTERMASSOC                           28945112773          1027698557.00 -            15841362471.73        -30655026386.46         32710423500.46
ORDERS                                   9847728695          1027698557.00 -            15841362471.73        -30655026386.46         32710423500.46
ATTRDESC                                 3882342048          1027698557.00 -            15841362471.73        -30655026386.46         32710423500.46
XTAHITEM                                 2769959859          1027698557.00 -            15841362471.73        -30655026386.46         32710423500.46
SCHCONFIG                                2404139505          1027698557.00 -            15841362471.73        -30655026386.46         32710423500.46
PRODUCTSET                               2376734932          1027698557.00 -            15841362471.73        -30655026386.46         32710423500.46
STATEPROV                                2365127252          1027698557.00 -            15841362471.73        -30655026386.46         32710423500.46
PX_PROMOTION                             2149025098          1027698557.00 -            15841362471.73        -30655026386.46         32710423500.46

This query defines tables which have truly exceptional numbers of rows read, to consider whether there are indexing issues or whether the tables might need to be separated due to very high use. You'll want to consider whether these tables have a high number of rows read in relation to their cardinality. The "Flag" column tells us. The average, std_dev, and upper and lower boundary columns are the same for all rows - they are provided only if you are not yet familiar with this method of identifying outliers.

Rows Read as Compared to Cardinality

Finding tables where the rows read is many times the cardinality can help find those code tables. The small tables that are read through many times and that we want to keep in memory as much as possible. This query can help identify them:

select  substr(t.tabname,1,30) as tabname, 
            when card > 0 then rows_read/card 
            else NULL 
        end as rr_per_card 
    from    table (mon_get_table(NULL, NULL, -2)) as mgt, 
        syscat.tables t 
    where   t.tabschema=mgt.tabschema 
        and t.tabname=mgt.tabname 
        and rows_read > 0 
        and rows_read > 10000*card 
        and card > 0 
        and volatile !='C' 
        and t.tabschema='REDACTED' 
    order by    card, 
    with ur

TABNAME                        ROWS_READ            CARD                 RR_PER_CARD
------------------------------ -------------------- -------------------- --------------------
GRRGSTRNT                                    743823                    1               743823
TAXCGRY                                       37312                    2                18656
PLCYACCLCK                                    49759                    2                24879
PLCYPASSWD                                    83208                    2                41604
PLCYACCT                                      94085                    2                47042
STORECONF                                  27373893                    2             13686946
ATCHOBJTYP                                  9178023                    4              2294505
DMACTTYPE                                  20402432                    4              5100608
DMELEMENTTYPE                             213803013                    6             35633835
CFCATGROUP                                 10420263                    7              1488609
CALUSAGE                                     340442                    8                42555
DISPCGPREL                                 25019058                   13              1924542
LANGUAGEDS                                 11830018                   14               845001
CFPRODUCTSET                              168646681                   14             12046191
PRODUCTSET                               2375323449                   15            158354896
SHPARRANGE                                   529786                   17                31163
ATCHRLUS                                    5958892                   18               331049
JURST                                      12021918                   18               667884

What this query finds is tables where for every single row in the table, DB2 has read it at least 10,000 times. It also ignores volatile tables. Obviously, using the cardinality relies on having current statistics. The far right column tells us, on average, how many times the entire table was scanned (though the activity could just be reading one row over again). Logically, you'll want to consider what's happening with these tables - it could just be that they need indexes added, or it could be that DB2 is reading them over and over again, and they should be in a tablespace with a bufferpool dedicated so they can remain in memory.

Tables with High Update/Insert/Delete Activity


For updates, this query works similarly to the first rows_read one:

with ru_stats as (
    select  decimal(avg(rows_updated),20,2) as avg_ru,
        decimal(stddev(rows_updated),20,2) as stddev_ru,
        decimal(avg(rows_updated) - 2 * stddev(rows_updated),20,2) as avg_less_2_stddev,
        decimal(avg(rows_updated) + 2 * stddev(rows_updated),20,2) as avg_plus_2_stddev
    from table (mon_get_table(NULL,NULL,-1)) as t
    where t.tabschema='REDACTED'
    substr(tabname,1,30) as tabname,
                when rows_updated > ru_stats.avg_plus_2_stddev then 'High'
                when rows_updated < ru_stats.avg_less_2_stddev then 'Low'
                else NULL
        end as flag,
from    table (mon_get_table(NULL,NULL,-1)) as t1,
where   t1.tabschema='REDACTED'
    and rows_updated > 0
order by rows_updated desc
with ur;

TABNAME                        ROWS_UPDATED         AVG_RU                 FLAG STDDEV_RU              AVG_LESS_2_STDDEV      AVG_PLUS_2_STDDEV
------------------------------ -------------------- ---------------------- ---- ---------------------- ---------------------- ----------------------
DMELESTATS                                 31812700               72611.00 High             1156856.82            -2241102.64             2386324.64
DMEMSPOTSTATS                              10227837               72611.00 High             1156856.82            -2241102.64             2386324.64
CATENTRYATTR                                3981154               72611.00 High             1156856.82            -2241102.64             2386324.64
ORDERS                                      3578831               72611.00 High             1156856.82            -2241102.64             2386324.64
CATGPENREL                                  2140700               72611.00 -                1156856.82            -2241102.64             2386324.64
ORDERITEMS                                  1069409               72611.00 -                1156856.82            -2241102.64             2386324.64
MBRATTRVAL                                  1059392               72611.00 -                1156856.82            -2241102.64             2386324.64
USERS                                        911379               72611.00 -                1156856.82            -2241102.64             2386324.64
MEMBER                                       911365               72611.00 -                1156856.82            -2241102.64             2386324.64

As with the previous query, the "FLAG" column identifies tables that are statistically significantly out of the norm.


with ri_stats as (
    select  decimal(avg(rows_inserted),20,2) as avg_ri,
        decimal(stddev(rows_inserted),20,2) as stddev_ri,
        decimal(avg(rows_inserted) - 2 * stddev(rows_inserted),20,2) as avg_less_2_stddev,
        decimal(avg(rows_inserted) + 2 * stddev(rows_inserted),20,2) as avg_plus_2_stddev
    from table (mon_get_table(NULL,NULL,-1)) as t
    where t.tabschema='REDACTED'
    substr(tabname,1,30) as tabname,
                when rows_inserted > ri_stats.avg_plus_2_stddev then 'High'
                when rows_inserted < ri_stats.avg_less_2_stddev then 'Low'
                else NULL
        end as flag,
from    table (mon_get_table(NULL,NULL,-1)) as t1,
where   t1.tabschema='REDACTED'
order by rows_inserted desc
with ur;

TABNAME                        ROWS_INSERTED        AVG_RI                 FLAG STDDEV_RI              AVG_LESS_2_STDDEV      AVG_PLUS_2_STDDEV
------------------------------ -------------------- ---------------------- ---- ---------------------- ---------------------- ----------------------
CACHEIVL                                   72260808              115640.00 High             2482812.07            -4849984.15             5081264.15
CTXDATA                                     6582356              115640.00 High             2482812.07            -4849984.15             5081264.15
DMEXPLOG                                    4797431              115640.00 -                2482812.07            -4849984.15             5081264.15
PX_CDPROMO                                  3776761              115640.00 -                2482812.07            -4849984.15             5081264.15
PX_CDPOOL                                   3226659              115640.00 -                2482812.07            -4849984.15             5081264.15
DMUSERBHVR                                  1567480              115640.00 -                2482812.07            -4849984.15             5081264.15
MBRATTRVAL                                   920179              115640.00 -                2482812.07            -4849984.15             5081264.15
CTXMGMT                                      822950              115640.00 -                2482812.07            -4849984.15             5081264.15
MEMBER                                       795710              115640.00 -                2482812.07            -4849984.15             5081264.15


with rd_stats as (
    select  decimal(avg(rows_deleted),20,2) as avg_rd,
        decimal(stddev(rows_deleted),20,2) as stddev_rd,
        decimal(avg(rows_deleted) - 2 * stddev(rows_deleted),20,2) as avg_less_2_stddev,
        decimal(avg(rows_deleted) + 2 * stddev(rows_deleted),20,2) as avg_plus_2_stddev
    from table (mon_get_table(NULL,NULL,-1)) as t
    where t.tabschema='REDACTED'
    substr(tabname,1,30) as tabname,
                when rows_deleted > rd_stats.avg_plus_2_stddev then 'High'
                when rows_deleted < rd_stats.avg_less_2_stddev then 'Low'
                else NULL
        end as flag,
from    table (mon_get_table(NULL,NULL,-1)) as t1,
where   t1.tabschema='REDACTED'
    and rows_deleted > 0
order by rows_deleted desc
with ur;

TABNAME                        ROWS_DELETED         AVG_RD                 FLAG STDDEV_RD              AVG_LESS_2_STDDEV      AVG_PLUS_2_STDDEV
------------------------------ -------------------- ---------------------- ---- ---------------------- ---------------------- ----------------------
CACHEIVL                                   62205357               90131.00 High             2139222.54            -4188314.08             4368576.08
CTXDATA                                     7152013               90131.00 High             2139222.54            -4188314.08             4368576.08
DMEXPLOG                                    4786455               90131.00 High             2139222.54            -4188314.08             4368576.08
DMUSERBHVR                                  1608087               90131.00 -                2139222.54            -4188314.08             4368576.08
CTXMGMT                                      894125               90131.00 -                2139222.54            -4188314.08             4368576.08
XSTORESKU                                    415115               90131.00 -                2139222.54            -4188314.08             4368576.08
TMPCMPLIST                                   168692               90131.00 -                2139222.54            -4188314.08             4368576.08
ORDERITEMS                                    59884               90131.00 -                2139222.54            -4188314.08             4368576.08

By now the format of the query should look familiar. I also found it interesting that some of the tables I listed ahead of time were only confirmed through this process. Both CACHEIVL and CTXDATA I knew would need special attention ahead of time.

Tables that are Large in Size

If simply looking for tables that are the largest in your database, the query is simple:

select  substr(tabname,1,30) as tabname, 
    from    syscat.tables 
    where   type='T' 
            and tabschema='WSCOMUSR' 
    order by card desc 
    fetch first 20 rows only 
    with ur

TABNAME                        CARD
------------------------------ --------------------
PX_CDPROMO                                 50934290
PX_CDPOOL                                  50933284
CACHEIVL                                   30270713
MBRATTRVAL                                 11243103
USERS                                      11033793
MEMBER                                     11033724
BUSEVENT                                    6805318
PPCEXTDATA                                  5368244
MBRGRPMBR                                   5326807
SRCHSTAT                                    5251287
SCHBRDCST                                   4341523
XSTORESKU                                   3908962
MBRREL                                      3529638
ORDPAYINFO                                  2776001
ADDRESS                                     2246643
CATENTRYATTR                                2218289
ORDERITEMS                                  2033306
ADDRBOOK                                    1887871
MBRROLE                                     1768298
USERREG                                     1767892

  20 record(s) selected.

One of the tricky things here is ensuring you are properly pruning your e-commerce database. If you haven't been doing that (using dbclean for WebSphere Commerce), then these numbers may be misleading. The database I'm looking at is well pruned. The next thing is to figure out what you need to do with these tables. Are they ones that are likely to be sequentially read, and so need to go in a tablespace/bufferpool appropriate for that? Or are they fairly randomly accessed? Do they warrant a single tablespace just for themselves?

Tables with Questionable Page Sizes

There's this really great blog entry from Serge Rielau and Rick Swagerman (they have many great entries) that outlines how to look at the "worst-case" row size, which is what you have to consider when you're looking at what page size a table should and must go on. Go check it out for the stored proceedure (DBA.GetRowSize) used in the next query.

This query can help you find tables where few rows fit on a page, and therefore a larger page size could be appropriate to save you space:

with T1 as(
  SELECT    varchar(tabschema, 10) as tabschema
        , substr(tabname,1,30) as tabname
        , card
        , DBA.GetRowSize(tabschema, tabname) as rowsize
        , avgrowsize
        , pagesize
            , case
                    when avgrowsize = 0 then NULL
                    else pagesize/avgrowsize
               end as avg_rows_per_page
            , case
                    when DBA.GetRowSize(tabschema, tabname) = 0 then NULL
                    else pagesize/DBA.GetRowSize(tabschema, tabname)
              end as min_rows_per_page

    WHERE TABNAME not like 'SYS%'
    AND type = 'T'
    AND tab.tbspaceid = tbsp.tbspaceid
    ORDER BY tabname
SELECT tabschema
    , tabname
    , card
    , rowsize
    , avgrowsize
    , pagesize
    , case
        when rowsize > 16293 then '32K'
        when rowsize > 8101 then '16K'
        when rowsize > 4005 then '8K'
        when rowsize <= 4005 then '4K'
      end as min_page_size
    , avg_rows_per_page
    , min_rows_per_page
  ORDER BY avg_rows_per_page, min_rows_per_page

---------- ------------------------------ -------------------- ----------- ---------- ----------- ------------- ----------------- -----------------
REDACTED   BUSEVENT                                    6805318        4097       3442        8192 8K                            2                 1
DBAMON097  WORKLOAD_HIST_SUMMARY                             2        1325       1088        4096 4K                            3                 3
REDACTED   ACRELGRP                                         19        3052        975        4096 4K                            4                 1
DBA        LOCK_PARTICIPANTS                             13680        3575        535        4096 4K                            7                 1
REDACTED   ORDERITEMS                                  2033306        1957        454        4096 4K                            9                 2
REDACTED   BUSAUDIT                                       1846        1959        400        4096 4K                           10                 2
DB2INST1   EXPLAIN_OBJECT                                48298        1116        351        4096 4K                           11                 3
REDACTED   STADDRESS                                       236        3481        338        4096 4K                           12                 1
SYSTOOLS   HMON_ATM_INFO                                   954        1421        319        4096 4K                           12                 2
DBA        LOCK_PARTICIPANT_ACTIVITIES                    6574        1672        323        4096 4K                           12                 2
REDACTED   ADDRESS                                     2246643        3885        312        4096 4K                           13                 1
DB2INST1   ADVISE_INDEX                                   1122        3130        313        4096 4K                           13                 1
REDACTED   ORDERS                                      1678809        1059        305        4096 4K                           13                 3
REDACTED   PX_PROMOTION                                   1439         848        293        4096 4K                           13                 4
REDACTED   ICDATAREG                                         9         296        306        4096 4K                           13                13
REDACTED   TERMCOND                                         52        3978        282        4096 4K                           14                 1
REDACTED   CATENTRY                                     283320        1441        282        4096 4K                           14                 2
REDACTED   DMCAMPAIGN                                       78        1847        274        4096 4K                           14                 2
REDACTED   CALCODE                                        1466         748        280        4096 4K                           14                 5
REDACTED   TRADEPOSCN                                       43         559        278        4096 4K                           14                 7

  20 record(s) selected.

The first table in that list just drives me nuts. When I analyze it, I see that 40% or more of the space that the table takes up is just wasted, because it has a large varchar field that when full, can only fit one row on a page. Certain ways that the app uses the table means that there may be 300 or more associated rows in the table that all use that maximum and would always be queried as a block. Clearly that table needs a larger page size.

There are others there that may warrant investigation. Clearly we won't be changing explain or system tools tables, but it's still interesting. This query would be most useful if you're tight on space and looking to reduce wasted space in the database.

Tables with LOBs in Them

You may want to consider putting tables with LOBs in them in their own tablespace and turn on file system caching, or you may want to direct LOB data to a separate tablespace. Separate LOB data may only be in a DMS tablespace. You may also want to read Mike Krafick's blog entry on inline and dealing with LOBs.

This is a query that not only lists the tables with LOBs in them, but is also useful for analyzing the LOB columns in your tables, if you're thinking about inling LOBs or separating them out to their own tablespace:

select  substr(c.tabname,1,18) tabname, 
        substr(colname,1,30) colname, 
  from  syscat.columns c, 
        syscat.tables t, 
        syscat.tablespaces ts 
  where t.tabname=c.tabname 
        and t.tabschema=c.tabschema 
        and t.tbspaceid=ts.tbspaceid 
        and typename in ('BLOB', 'CLOB') 
        and t.tabschema='REDACTED' 
  order by  c.tabname, 
  with ur

TABNAME            COLNAME                        LENGTH      AVGCOLLEN   LOGGED CARD                 PAGESIZE
------------------ ------------------------------ ----------- ----------- ------ -------------------- -----------
ATTACHMENT         CONTENT                           10000000           5 Y                         0        4096
BUSAUDIT           PARAMETERS                        10000000         134 Y                      1846        4096
CATCONFINF         CONFIGURATION                      1000000           5 Y                         0        8192
CATENTDESC         LONGDESCRIPTION                    1000000          55 Y                    283318       16384
CATENTDESC         XMLDETAIL                          1000000           5 Y                    283318       16384
CMLARGEFILE        CMFILE                           300000000           5 Y                         0        4096
CMSMALLFILE        CMFILE                               32000           5 Y                         0        4096
CNTRSTORE          STOREXML                           1000000         112 Y                        15        4096
COLLDESC           LONGMKTGTEXT                       1000000          54 Y                      8961        8192
DKPDCDESC          LONGDESCRIPTION                    1000000           5 Y                         0        4096
DMACTIVITY         FLOWXML                            1000000           5 Y                      3512       16384
DMELETEMPLATE      BEHAVIORXML                        1000000          77 Y                        75       16384
DMELETEMPLATE      DISPLAYXML                         1000000           5 Y                        75       16384
DMELETEMPLATE      IMPLXML                            1000000         112 Y                        75       16384
DMELETEMPLATE      RELATEDXML                         1000000          57 Y                        75       16384


I am intentionally not sharing the extent of my final decisions here. I share a lot, but some of my work is the intellectual property of my employer. I did end up with a few crucial tables broken out into individual tablespaces, one or two tablespaces for a logical grouping of tables, one tablespace for those highly-read code tables, and one for LOBs.

I hope the methodology and thought process here helps someone.

Ember Crooks
Ember Crooks

Ember is always curious and thrives on change. She has built internationally recognized expertise in IBM Db2, spent a year working with high-volume MySQL, and is now learning Snowflake. Ember shares both posts about her core skill sets and her journey learning Snowflake.

Ember lives in Denver and work from home

Articles: 557


  1. Something you left out of consideration and as much as some people don’t like to believe it, Size matters(this isn’t always your table with the most rows, either). I have had to create additional tablespaces due to not having anymore space in a tablespace. Then you have to deal with lowering your High Water Mark as well. Not a fun process. It is much easier to think of these items prior to implementation rather than changing an existing database.

    • True – this was especially a problem back before the days of “LARGE” tablespaces – when the limit on a single table in a DMS tablespace on a single partition for 4K page size was 64GB. Luckily there are a number of things we can use to deal with that – the use of LARGE tablespaces, table partitioning, database partitioning, etc. It certainly is a good idea to break out particularly large tables.

  2. Great article with even better SQL.
    There is a lot more to be said about this subject, I agree with Dave. The fact that you are currenly running on V9.7 or even on V10 does not say a lot about the current state of your tablespaces. When you make it a habit to play it save during conversion (redirected restores only) your database could still contains the heritage of the older versions (SMS, non-large non-reclaimable DMS ect) or (do not forget to mention that) tablespaces residing on ext3 filesystems.
    But again, thank you for another great article.

  3. Thanks Ember for sharing your ideas. I agree with Dave: “size matters”. In our case we split every table into their own Data, Index and LOB (if applies) tablespaces. At the end this has given us more opportunity to do either offline/online maintenance or DDL changes and addresses many of the issues you mention above: tables with High DML Activity, need for different page size (Data vs Index vs LOB), FS caching.

  4. Hi, What is the best practice defining a DMS container, like should we define one container or it should be multiple specially if all the containers are at the same FS.

    • Well, really, you should be using automatic storage on db2 10.1 and later – no need to define the storage at the tablespace level. Prior to 10.1, my opinion is that one container per fully separate I/O path – which usually means just one container. Containers can easily be grown and are not too hard to shrink.

  5. Hi Ember,
    General query regarding number of tablespaces . How many tablespaces does a production db contains .(what is the big number that you had observed in your experience ).

    • Back in the bad old days, I saw some databases (maybe in support of PeopleSoft?) where each table was in it’s own tablespace. Actually, I talked to a large national retailer within the last year who still did it that way. The reasoning originally was that in certain conditions the entire tablespace would be locked. Today, that level of locking just doesn’t happen outside of things like offline backups, so the reasoning is not there for that method in my mind.

      The number of tablespaces can quickly multiply when you look at the categories I’ve talked about and the fact that you may need a tablespace of each page size for each of those categories.

      It’s not about hitting a hard number, but really about not shooting yourself in the foot on maintenance, and keeping your reasoning for each tablespace solid.

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