a curated list of database news from authoritative sources

September 30, 2025

Tinybird Engineering Blog

How to get the hostname from a URL in ClickHouse

Learn how to extract hostnames from URLs in ClickHouse using the domain() function, plus performance tips and real-world examples for web analytics.
by Cameron Archer
Tinybird Engineering Blog

How to decode URL-encoded strings in ClickHouse

Learn how to decode URL-encoded strings in ClickHouse using decodeURLComponent, with performance tips, edge cases, and production deployment strategies.
by Cameron Archer
Tinybird Engineering Blog

How to parse numeric date formats in ClickHouse

Learn how to convert numeric date formats to ClickHouse Date/DateTime types using YYYYMMDDToDate functions for better performance and built-in date operations.
by Cameron Archer
Percona Database Performance Blog

Tackling the Cache Invalidation and Cache Stampede Problem in Valkey with Debezium Platform

There are two hard problems in computer science: cache invalidation, naming things, and off-by-1 errors. This classic joke, often attributed to Phil Karlton, highlights a very real and persistent challenge for software developers. We’re constantly striving to build faster, more responsive systems, and caching is a fundamental strategy for achieving that. But while caching offers […]
by Hieu Nguyen
Supabase Blog

PostgREST 13

New features and changes in PostgREST version 13.

September 29, 2025

Small Datum - Mark Callaghan

Postgres 18.0 vs sysbench on a 24-core, 2-socket server

This post has results from sysbench run at higher concurrency for Postgres versions 12 through 18 on a server with 24 cores and 2 sockets. My previous post had results for sysbench run with low concurrency. The goal is to search for regressions from new CPU overhead and mutex contention.

tl;dr, from Postgres 17.6 to 18.0

  • For most microbenchmarks Postgres 18.0 is between 1% and 3% slower than 17.6
  • The root cause might be new CPU overhead. It will take more time to gain confidence in results like this. On other servers with sysbench run at low concurrency I only see regressions for some of the range-query microbenchmarks. Here I see them for point-query and writes.

tl;dr, from Postgres 12.22 through 18.0

  • For point queries Postgres 18.0 is usually about 5% faster than 12.22
  • For range queries Postgres 18.0 is usually as fast as 12.22
  • For writes Postgres 18.0 is much faster than 12.22

Builds, configuration and hardware

I compiled Postgres from source for versions 12.22, 13.22, 14.19, 15.14, 16.10, 17.6, and 18.0.

The server is a SuperMicro SuperWorkstation 7049A-T with 2 sockets, 12 cores/socket, 64G RAM. The CPU is Intel Xeon Silver 4214R CPU @ 2.40GHz. It runs Ubuntu 24.04. Storage is a 1TB m.2 NVMe device with ext-4 and discard enabled.

Prior to 18.0, the configuration file was named conf.diff.cx10a_c24r64 and is here for 12.22, 13.22, 14.19, 15.14, 16.10 and 17.6.

For 18.0 I tried 3 configuration files:

Benchmark

I used sysbench and my usage is explained here. To save time I only run 32 of the 42 microbenchmarks 
and most test only 1 type of SQL statement. Benchmarks are run with the database cached by Postgres.

The read-heavy microbenchmarks run for 600 seconds and the write-heavy for 900 seconds.

The benchmark is run with 16 clients and 8 tables with 10M rows per table. The purpose is to search for regressions from new CPU overhead and mutex contention.

Results

The microbenchmarks are split into 4 groups -- 1 for point queries, 2 for range queries, 1 for writes. For the range query microbenchmarks, part 1 has queries that don't do aggregation while part 2 has queries that do aggregation. 

I provide charts below with relative QPS. The relative QPS is the following:
(QPS for some version) / (QPS for base version)
When the relative QPS is > 1 then some version is faster than base version.  When it is < 1 then there might be a regression. Values from iostat and vmstat divided by QPS are also provided here. These can help to explain why something is faster or slower because it shows how much HW is used per request.

I present results for:
  • versions 12 through 18 using 12.22 as the base version
  • versions 17.6 and 18.0 using 17.6 as the base version
Results: Postgres 17.6 and 18.0

Results per microbenchmark from vmstat and iostat are here.

For point queries, 18.0 often gets between 1% and 3% less QPS than 17.6 and the root cause might be new CPU overhead. See the cpu/o column (CPU per query) in the vmstat metrics here for the random-points microbenchmarks.

For range queries, 18.0 often gets between 1% and 3% less QPS than 17.6 and the root cause might be new CPU overhead. See the cpu/o column (CPU per query) in the vmstat metrics here for the read-only_range=X microbenchmarks.

For writes queries, 18.0 often gets between 1% and 2% less QPS than 17.6 and the root cause might be new CPU overhead. I ignore the write-heavy microbenchmarks that also do queries as the regressions for them might be from the queries. See the cpu/o column (CPU per query) in the vmstat metrics here for the update-index microbenchmark.

Relative to: 17.6
col-1 : 18.0 with the x10b config
col-2 : 18.0 with the x10c config
col-3 : 18.0 with the x10d config

col-1   col-2   col-3   point queries
1.00    0.99    1.00    hot-points_range=100
0.99    0.98    1.00    point-query_range=100
0.98    0.99    0.99    points-covered-pk_range=100
0.99    0.99    0.98    points-covered-si_range=100
0.97    0.99    0.98    points-notcovered-pk_range=100
0.98    0.99    0.97    points-notcovered-si_range=100
0.98    0.99    0.98    random-points_range=1000
0.97    0.99    0.98    random-points_range=100
0.99    0.99    0.98    random-points_range=10

col-1   col-2   col-3   range queries without aggregation
0.98    0.98    0.99    range-covered-pk_range=100
0.98    0.98    0.98    range-covered-si_range=100
0.98    0.99    0.98    range-notcovered-pk_range=100
1.00    1.02    0.99    range-notcovered-si_range=100
1.01    1.01    1.01    scan_range=100

col-1   col-2   col-3   range queries with aggregation
0.99    1.00    0.98    read-only-count_range=1000
0.98    0.98    0.98    read-only-distinct_range=1000
0.97    0.97    0.96    read-only-order_range=1000
0.97    0.98    0.97    read-only_range=10000
0.98    0.99    0.98    read-only_range=100
0.99    0.99    0.99    read-only_range=10
0.98    0.99    0.99    read-only-simple_range=1000
0.98    1.00    0.98    read-only-sum_range=1000

col-1   col-2   col-3   writes
0.99    0.99    0.99    delete_range=100
0.99    0.99    0.99    insert_range=100
0.98    0.98    0.98    read-write_range=100
0.99    1.00    0.99    read-write_range=10
0.99    0.98    0.97    update-index_range=100
0.99    0.99    1.00    update-inlist_range=100
1.00    0.97    0.99    update-nonindex_range=100
0.97    1.00    0.98    update-one_range=100
1.00    0.99    1.01    update-zipf_range=100
0.98    0.98    0.97    write-only_range=10000

Results: Postgres 12 to 18

For the Postgres 18.0 results in col-6, the result is in green when relative QPS is >= 1.05 and in yellow when relative QPS is <= 0.98. Yellow indicates a possible regression.

Results per microbenchmark from vmstat and iostat are here.

Relative to: 12.22
col-1 : 13.22
col-2 : 14.19
col-3 : 15.14
col-4 : 16.10
col-5 : 17.6
col-6 : 18.0 with the x10b config

col-1   col-2   col-3   col-4   col-5   col-6   point queries
0.98    0.96    0.99    0.98    2.13    2.13    hot-points_range=100
1.00    1.02    1.01    1.02    1.03    1.01    point-query_range=100
0.99    1.05    1.05    1.08    1.07    1.05    points-covered-pk_range=100
0.99    1.08    1.05    1.07    1.07    1.05    points-covered-si_range=100
0.99    1.04    1.05    1.06    1.07    1.05    points-notcovered-pk_range=100
0.99    1.05    1.04    1.05    1.06    1.04    points-notcovered-si_range=100
0.98    1.03    1.04    1.06    1.06    1.04    random-points_range=1000
0.98    1.04    1.05    1.07    1.07    1.05    random-points_range=100
0.99    1.02    1.03    1.05    1.05    1.04    random-points_range=10

col-1   col-2   col-3   col-4   col-5   col-6   range queries without aggregation
1.02    1.04    1.03    1.04    1.03    1.01    range-covered-pk_range=100
1.05    1.07    1.06    1.06    1.06    1.05    range-covered-si_range=100
0.99    1.00    1.00    1.00    1.01    0.98    range-notcovered-pk_range=100
0.97    0.99    1.00    1.01    1.01    1.01    range-notcovered-si_range=100
0.86    1.06    1.08    1.17    1.18    1.20    scan_range=100

col-1   col-2   col-3   col-4   col-5   col-6   range queries with aggregation
0.98    0.97    0.97    1.00    0.98    0.97    read-only-count_range=1000
0.99    0.99    1.02    1.02    1.01    0.99    read-only-distinct_range=1000
1.00    0.99    1.02    1.05    1.05    1.02    read-only-order_range=1000
0.99    0.99    1.04    1.07    1.09    1.06    read-only_range=10000
0.99    1.00    1.00    1.01    1.02    0.99    read-only_range=100
1.00    1.00    1.00    1.01    1.01    1.00    read-only_range=10
0.99    0.99    1.00    1.01    1.01    0.99    read-only-simple_range=1000
0.98    0.99    0.99    1.00    1.00    0.98    read-only-sum_range=1000

col-1   col-2   col-3   col-4   col-5   col-6   writes
0.98    1.09    1.09    1.04    1.29    1.27    delete_range=100
0.99    1.03    1.02    1.03    1.08    1.07    insert_range=100
1.00    1.03    1.04    1.05    1.07    1.05    read-write_range=100
1.01    1.09    1.09    1.09    1.15    1.14    read-write_range=10
1.00    1.04    1.03    0.86    1.44    1.42    update-index_range=100
1.01    1.11    1.11    1.12    1.13    1.12    update-inlist_range=100
0.99    1.04    1.06    1.05    1.25    1.25    update-nonindex_range=100
1.05    0.92    0.90    0.84    1.18    1.15    update-one_range=100
0.98    1.04    1.03    1.01    1.26    1.26    update-zipf_range=100
1.02    1.05    1.10    1.09    1.21    1.18    write-only_range=10000
by Mark Callaghan (noreply@blogger.com)
Percona Database Performance Blog

New File Copy-Based Initial Sync Overwhelms the Logical Initial Sync in Percona Server for MongoDB

In a previous article, Scalability for the Large-Scale: File Copy-Based Initial Sync for Percona Server for MongoDB, we presented some early benchmarks of the new File Copy-Based Initial Sync (FCBIS) available in Percona Server for MongoDB. Those first results already suggested significant improvements compared to the default Logical Initial Sync. In this post, we extend our […]
by Corrado Pandiani

September 28, 2025

Franck Pachot

WiredTigerHS.wt: MongoDB MVCC Durable History Store

MongoDB uses the WiredTiger storage engine, which implements Multi‑Version Concurrency Control (MVCC) to provide lock‑free read consistency, similar to many RDBMS. Unlike many RDBMS, it follows a No‑Force/No‑Steal policy: uncommitted changes stay only in memory. They are never written to disk, and committed changes are written later — at checkpoint or when cache eviction needs space — into the WiredTiger table files we have explored in the previous post, persisting only the latest committed version.
MongoDB also maintains recent committed MVCC versions for a specified period in a separate, durable history store (WiredTigerHS.wt). This enables the system to reconstruct snapshots from earlier points in time. In the previous article in this series, I described all WiredTiger files except WiredTigerHS.wt, because it was empty:

ls -l /data/db/WiredTigerHS.wt

-rw-------. 1 root root 4096 Sep 27 11:01 /data/db/WiredTigerHS.wt

This 4KB file holds no records:

wt -h /data/db dump -j file:WiredTigerHS.wt

{
    "WiredTiger Dump Version" : "1 (12.0.0)",
    "file:WiredTigerHS.wt" : [
        {
            "config" : "access_pattern_hint=none,allocation_size=4KB,app_metadata=,assert=(commit_timestamp=none,durable_timestamp=none,read_timestamp=none,write_timestamp=off),block_allocation=best,block_compressor=snappy,block_manager=default,cache_resident=false,checksum=on,colgroups=,collator=,columns=,dictionary=0,disaggregated=(page_log=),encryption=(keyid=,name=),exclusive=false,extractor=,format=btree,huffman_key=,huffman_value=,ignore_in_memory_cache_size=false,immutable=false,import=(compare_timestamp=oldest_timestamp,enabled=false,file_metadata=,metadata_file=,panic_corrupt=true,repair=false),in_memory=false,ingest=,internal_item_max=0,internal_key_max=0,internal_key_truncate=true,internal_page_max=16KB,key_format=IuQQ,key_gap=10,leaf_item_max=0,leaf_key_max=0,leaf_page_max=32KB,leaf_value_max=64MB,log=(enabled=true),lsm=(auto_throttle=,bloom=,bloom_bit_count=,bloom_config=,bloom_hash_count=,bloom_oldest=,chunk_count_limit=,chunk_max=,chunk_size=,merge_max=,merge_min=),memory_page_image_max=0,memory_page_max=5MB,os_cache_dirty_max=0,os_cache_max=0,prefix_compression=false,prefix_compression_min=4,source=,split_deepen_min_child=0,split_deepen_per_child=0,split_pct=90,stable=,tiered_storage=(auth_token=,bucket=,bucket_prefix=,cache_directory=,local_retention=300,name=,object_target_size=0),type=file,value_format=QQQu,verbose=[],write_timestamp_usage=none",
            "colgroups" : [],
            "indices" : []
        },
        {
            "data" : [            ]
        }
    ]
}

The file contains only a header block with the configuration metadata. It defines the key and value format:

key_format=IuQQ    
value_format=QQQu

Those are WiredTiger types: I and Q are integers, respectively 4-byte and 8-byte, and u is a variable-length type, as an array of bytes.

The history store key (IuQQ) includes the table identifier (collection), the key in this table (recordID), the MVCC start timestamp (indicating when this version was current), and a counter. Its value (QQQu) contains the MVCC stop timestamp (when the version became obsolete), the durable timestamp (reflecting when the record reached a persistence point, such as a checkpoint), an update type, and the byte array is the BSON representation of the document version. Start and stop timestamps track version visibility for this document version. The durable timestamp shows when a version is safe to remove, supporting features such as rollback-to-stable, replication catch-up, and crash recovery.

To get some records in it, I start MongoDB as a one-member replicaset:

mongod --dbpath /data/db --replSet rs0 --wiredTigerCacheSizeGB 0.25 &  

mongosh --eval '
  rs.initiate( { _id: "rs0", members: [
  {_id: 0, priority: 1, host: "localhost:27017"},
  ]});
'

I insert five documents and update them, to have two versions of the documents, the current one with { val: "newvalue" } and the previous one with { val: "oldvalue" }:

db.test.drop();  
for (let i = 0; i < 5; i++) {  
    db.test.insertOne({  
        _id: i,  
        val: "oldvalue",  
        filler: "X".repeat(1024)  
    });  
}   
for (let i = 0; i < 5; i++) {  
    db.test.updateOne(  
        { _id: i },  
        { $set: { val: "newvalue" } } // change to whatever new value you want  
    );  
}

Until a checkpoint or cache eviction occurs, all changes remain in memory (the WiredTiger cache), protected by write-ahead logging (WAL). To get something in the files, I watch the mongod log and wait for a checkpoint:

{"t":{"$date":"2025-09-27T20:33:18.140+00:00"},"s":"I",  "c":"WTCHKPT",  "id":22430,   "ctx":"Checkpointer","msg":"WiredTiger message","attr":{"message":{"ts_sec":1759005198,"ts_usec":140184,"thread":"12233:0x7f908e1f76c0","session_name":"WT_SESSION.checkpoint","category":"WT_VERB_CHECKPOINT_PROGRESS","category_id":7,"verbose_level":"DEBUG_1","verbose_level_id":1,"msg":"saving checkpoint snapshot min: 196, snapshot max: 196 snapshot count: 0, oldest timestamp: (1759005138, 1) , meta checkpoint timestamp: (1759005188, 1) base write gen: 1"}}}

The durable history storage file size has increased:

ls -alrt WiredTigerHS.wt
-rw-------. 1 root root 20480 Sep 27 20:33 WiredTigerHS.wt

I stopped mongod to be able to read the files with wt (that I compiled in a Docker container, as in the earlier post) of this series:

pkill mongod

There are 18 records in the durable history file, and the ones from my collection are visible as I filled a field with a thousand 'X' characters (0x58), so they are easy to spot in a hex/BSON dump:

wt -h /data/db dump file:WiredTigerHS.wt 

WiredTiger Dump (WiredTiger Version 12.0.0)
Format=print
Header
file:WiredTigerHS.wt
access_pattern_hint=none,allocation_size=4KB,app_metadata=,assert=(commit_timestamp=none,durable_timestamp=none,read_timestamp=none,write_timestamp=off),block_allocation=best,block_compressor=snappy,block_manager=default,cache_resident=false,checksum=on,colgroups=,collator=,columns=,dictionary=0,disaggregated=(page_log=),encryption=(keyid=,name=),exclusive=false,extractor=,format=btree,huffman_key=,huffman_value=,ignore_in_memory_cache_size=false,immutable=false,import=(compare_timestamp=oldest_timestamp,enabled=false,file_metadata=,metadata_file=,panic_corrupt=true,repair=false),in_memory=false,ingest=,internal_item_max=0,internal_key_max=0,internal_key_truncate=true,internal_page_max=16KB,key_format=IuQQ,key_gap=10,leaf_item_max=0,leaf_key_max=0,leaf_page_max=32KB,leaf_value_max=64MB,log=(enabled=true),lsm=(auto_throttle=,bloom=,bloom_bit_count=,bloom_config=,bloom_hash_count=,bloom_oldest=,chunk_count_limit=,chunk_max=,chunk_size=,merge_max=,merge_min=),memory_page_image_max=0,memory_page_max=5MB,os_cache_dirty_max=0,os_cache_max=0,prefix_compression=false,prefix_compression_min=4,source=,split_deepen_min_child=0,split_deepen_per_child=0,split_pct=90,stable=,tiered_storage=(auth_token=,bucket=,bucket_prefix=,cache_directory=,local_retention=300,name=,object_target_size=0),type=file,value_format=QQQu,verbose=[],write_timestamp_usage=none
Data
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                                        by Franck Pachot
Franck Pachot

WiredTigerHS.wt: MongoDB MVCC Durable History Store

MongoDB uses the WiredTiger storage engine, which implements Multi‑Version Concurrency Control (MVCC) to provide lock‑free read consistency, similar to many RDBMS. Unlike many RDBMS, it follows a No‑Force/No‑Steal policy: Uncommitted changes stay only in memory. They are never written to disk, and committed changes are written later—at checkpoint or when cache eviction needs space—into the WiredTiger table files we have explored in the previous post, persisting only the latest committed version.

MongoDB also maintains recent committed MVCC versions for a specified period in a separate, durable history store (WiredTigerHS.wt). This enables the system to reconstruct snapshots from earlier points in time. In the previous article in this series, I described all WiredTiger files except WiredTigerHS.wt, because it was empty:

ls -l /data/db/WiredTigerHS.wt

-rw-------. 1 root root 4096 Sep 27 11:01 /data/db/WiredTigerHS.wt

This 4KB file holds no records:

wt -h /data/db dump -j file:WiredTigerHS.wt

{
    "WiredTiger Dump Version" : "1 (12.0.0)",
    "file:WiredTigerHS.wt" : [
        {
            "config" : "access_pattern_hint=none,allocation_size=4KB,app_metadata=,assert=(commit_timestamp=none,durable_timestamp=none,read_timestamp=none,write_timestamp=off),block_allocation=best,block_compressor=snappy,block_manager=default,cache_resident=false,checksum=on,colgroups=,collator=,columns=,dictionary=0,disaggregated=(page_log=),encryption=(keyid=,name=),exclusive=false,extractor=,format=btree,huffman_key=,huffman_value=,ignore_in_memory_cache_size=false,immutable=false,import=(compare_timestamp=oldest_timestamp,enabled=false,file_metadata=,metadata_file=,panic_corrupt=true,repair=false),in_memory=false,ingest=,internal_item_max=0,internal_key_max=0,internal_key_truncate=true,internal_page_max=16KB,key_format=IuQQ,key_gap=10,leaf_item_max=0,leaf_key_max=0,leaf_page_max=32KB,leaf_value_max=64MB,log=(enabled=true),lsm=(auto_throttle=,bloom=,bloom_bit_count=,bloom_config=,bloom_hash_count=,bloom_oldest=,chunk_count_limit=,chunk_max=,chunk_size=,merge_max=,merge_min=),memory_page_image_max=0,memory_page_max=5MB,os_cache_dirty_max=0,os_cache_max=0,prefix_compression=false,prefix_compression_min=4,source=,split_deepen_min_child=0,split_deepen_per_child=0,split_pct=90,stable=,tiered_storage=(auth_token=,bucket=,bucket_prefix=,cache_directory=,local_retention=300,name=,object_target_size=0),type=file,value_format=QQQu,verbose=[],write_timestamp_usage=none",
            "colgroups" : [],
            "indices" : []
        },
        {
            "data" : [            ]
        }
    ]
}

The file contains only a header block with the configuration metadata. It defines the key and value format:

key_format=IuQQ    
value_format=QQQu

Those are WiredTiger types: I and Q are integers, respectively 4-byte and 8-byte, and u is a variable-length type, as an array of bytes.

The history store key (IuQQ) includes the table identifier (collection), the key in this table (recordID), the MVCC start timestamp (indicating when this version was current), and a counter (if multiple updates at the same timestamp). Its value (QQQu) contains the MVCC stop timestamp (when the version became obsolete), the durable timestamp (reflecting when the record reached a persistence point, such as a checkpoint), an update type, and the byte array is the BSON representation of the document version. Start and stop timestamps track version visibility for this document version. The durable timestamp shows when a version is safe to remove, supporting features such as rollback-to-stable, replication catch-up, and crash recovery.

To get some records in it, I start MongoDB as a one-member replicaset:

mongod --dbpath /data/db --replSet rs0 --wiredTigerCacheSizeGB 0.25 &  

mongosh --eval '
  rs.initiate( { _id: "rs0", members: [
  {_id: 0, priority: 1, host: "localhost:27017"},
  ]});
'

I insert five documents and update them, to have two versions of the documents, the current one with { val: "newvalue" } and the previous one with { val: "oldvalue" }:

db.test.drop();  
for (let i = 0; i < 5; i++) {  
    db.test.insertOne({  
        _id: i,  
        val: "oldvalue",  
        filler: "X".repeat(1024)  
    });  
}   
for (let i = 0; i < 5; i++) {  
    db.test.updateOne(  
        { _id: i },  
        { $set: { val: "newvalue" } } // change to whatever new value you want  
    );  
}

Until a checkpoint or cache eviction occurs, all changes remain in memory (the WiredTiger cache), protected by write-ahead logging (WAL). To get something in the files, I watch the mongod log and wait for a checkpoint:

{"t":{"$date":"2025-09-27T20:33:18.140+00:00"},"s":"I",  "c":"WTCHKPT",  "id":22430,   "ctx":"Checkpointer","msg":"WiredTiger message","attr":{"message":{"ts_sec":1759005198,"ts_usec":140184,"thread":"12233:0x7f908e1f76c0","session_name":"WT_SESSION.checkpoint","category":"WT_VERB_CHECKPOINT_PROGRESS","category_id":7,"verbose_level":"DEBUG_1","verbose_level_id":1,"msg":"saving checkpoint snapshot min: 196, snapshot max: 196 snapshot count: 0, oldest timestamp: (1759005138, 1) , meta checkpoint timestamp: (1759005188, 1) base write gen: 1"}}}

The durable history storage file size has increased:

ls -alrt WiredTigerHS.wt
-rw-------. 1 root root 20480 Sep 27 20:33 WiredTigerHS.wt

I stopped mongod to be able to read the files with wt (that I compiled in a Docker container, as in the earlier post) of this series:

pkill mongod

There are 18 records in the durable history file, and the ones from my collection are visible as I filled a field with a thousand "X" characters (0x58), so they are easy to spot in a hex/BSON dump:

wt -h /data/db dump file:WiredTigerHS.wt 

WiredTiger Dump (WiredTiger Version 12.0.0)
Format=print
Header
file:WiredTigerHS.wt
access_pattern_hint=none,allocation_size=4KB,app_metadata=,assert=(commit_timestamp=none,durable_timestamp=none,read_timestamp=none,write_timestamp=off),block_allocation=best,block_compressor=snappy,block_manager=default,cache_resident=false,checksum=on,colgroups=,collator=,columns=,dictionary=0,disaggregated=(page_log=),encryption=(keyid=,name=),exclusive=false,extractor=,format=btree,huffman_key=,huffman_value=,ignore_in_memory_cache_size=false,immutable=false,import=(compare_timestamp=oldest_timestamp,enabled=false,file_metadata=,metadata_file=,panic_corrupt=true,repair=false),in_memory=false,ingest=,internal_item_max=0,internal_key_max=0,internal_key_truncate=true,internal_page_max=16KB,key_format=IuQQ,key_gap=10,leaf_item_max=0,leaf_key_max=0,leaf_page_max=32KB,leaf_value_max=64MB,log=(enabled=true),lsm=(auto_throttle=,bloom=,bloom_bit_count=,bloom_config=,bloom_hash_count=,bloom_oldest=,chunk_count_limit=,chunk_max=,chunk_size=,merge_max=,merge_min=),memory_page_image_max=0,memory_page_max=5MB,os_cache_dirty_max=0,os_cache_max=0,prefix_compression=false,prefix_compression_min=4,source=,split_deepen_min_child=0,split_deepen_per_child=0,split_pct=90,stable=,tiered_storage=(auth_token=,bucket=,bucket_prefix=,cache_directory=,local_retention=300,name=,object_target_size=0),type=file,value_format=QQQu,verbose=[],write_timestamp_usage=none
Data
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                                        by Franck Pachot

September 26, 2025

AWS Database Blog - Amazon Aurora

Identifying and resolving performance issues caused by TOAST OID contention in Amazon Aurora PostgreSQL Compatible Edition and Amazon RDS for PostgreSQL

In this post, we explore the challenges of OID exhaustion in PostgreSQL, focusing on its impact on TOAST tables and how it leads to performance issues. We will cover how to identify the problem by reviewing wait events, session activity, and table usage. Additionally, we discuss practical solutions, from cleaning up data to more advanced strategies such as partitioning.
by Baji Shaik
Small Datum - Mark Callaghan

Postgres 18.0 vs sysbench on a small server

This has benchmark results for Postgres 18.0 using sysbench on a small server. Previous results for 18 rc1 are here.

tl;dr

  • From 12.22 to 18.0
    • there are no regressions larger than 2% but many improvements larger than 5%. Postgres continues to do a great job at avoiding regressions over time.
  • From 17.6 to 18.0
    • I continue to see small CPU regressions (1% or 2%) in Postgres 18 for short range queries on low-concurrency workloads. I see it for shorter but not for longer range queries so my guess is that this is new overhead in query execution setup or optimization. I hope to explain this.
Builds, configuration and hardware

I compiled Postgres from source for versions 12.22, 13.22, 14.19, 15.14, 16.10, 17.6, and 18.0.

The HW is an ASUS ExpertCenter PN53 with AMD Ryzen 7735HS CPU, 32G of RAM, 8 cores with AMD SMT disabled, Ubuntu 24.04 and an NVMe device with ext4 and discard enabled.

Prior to 18.0, the configuration file was named conf.diff.cx10a_c8r32 and is here for 12.22, 13.22, 14.19, 15.14, 16.10 and 17.6.

For 18.0 I tried 3 configuration files:

Benchmark

I used sysbench and my usage is explained here. To save time I only run 32 of the 42 microbenchmarks 
and most test only 1 type of SQL statement. Benchmarks are run with the database cached by Postgres.

The read-heavy microbenchmarks run for 600 seconds and the write-heavy for 900 seconds.

The benchmark is run with 1 client, 1 table and 50M rows. The purpose is to search for CPU regressions.

Results

The microbenchmarks are split into 4 groups -- 1 for point queries, 2 for range queries, 1 for writes. For the range query microbenchmarks, part 1 has queries that don't do aggregation while part 2 has queries that do aggregation. 

I provide charts below with relative QPS. The relative QPS is the following:
(QPS for some version) / (QPS for base version)
When the relative QPS is > 1 then some version is faster than base version.  When it is < 1 then there might be a regression. Values from iostat and vmstat divided by QPS are also provided here. These can help to explain why something is faster or slower because it shows how much HW is used per request.

I present results for:
  • versions 12 through 18 using 12.22 as the base version
  • versions 17.6 and 18.0 using 17.6 as the base version
Results: Postgres 17.6 and 18.0

For the read-only_range=X benchmarks there might be small regressions (1% or 2%) when X is 10 or 100 but not 10000. The value of X is the length of the range scan. I have seen similar regressions in the beta and RC releases. Given that this occurs when the range scan is shorter, the problem might be new overhead in query execution setup or optimization. But I have yet to explain this.

Relative to: 17.6 with x10a
col-1 : 18.0 with x10b and io_method=sync
col-2 : 18.0 with x10c and io_method=worker
col-3 : 18.0 with x10d and io_method=io_uring

col-1   col-2   col-3  point queries
1.01    1.01    0.97    hot-points_range=100
1.01    1.00    0.99    point-query_range=100
1.01    1.01    1.00    points-covered-pk_range=100
1.01    1.02    1.01    points-covered-si_range=100
1.01    1.01    1.00    points-notcovered-pk_range=100
1.01    0.99    1.00    points-notcovered-si_range=100
1.02    1.02    1.03    random-points_range=1000
1.01    1.00    0.99    random-points_range=100
1.00    1.00    0.99    random-points_range=10

col-1   col-2   col-3  range queries without aggregation
0.99    0.99    0.98    range-covered-pk_range=100
1.00    0.99    1.00    range-covered-si_range=100
1.00    0.99    0.98    range-notcovered-pk_range=100
0.99    0.99    0.99    range-notcovered-si_range=100
1.04    1.04    1.04    scan_range=100

col-1   col-2   col-3  range queries with aggregation
1.01    1.00    1.01    read-only-count_range=1000
1.01    1.00    1.00    read-only-distinct_range=1000
0.99    1.00    0.98    read-only-order_range=1000
1.01    1.00    1.00    read-only_range=10000
0.99    0.99    0.98    read-only_range=100
0.98    0.99    0.98    read-only_range=10
1.01    1.00    0.99    read-only-simple_range=1000
1.00    1.00    0.99    read-only-sum_range=1000

col-1   col-2   col-3  writes
1.00    1.00    0.99    delete_range=100
0.99    0.99    0.98    insert_range=100
0.99    0.99    0.98    read-write_range=100
0.98    0.99    0.98    read-write_range=10
0.99    1.00    0.99    update-index_range=100
0.99    1.00    1.00    update-inlist_range=100
0.99    1.00    0.98    update-nonindex_range=100
0.99    0.99    0.98    update-one_range=100
0.99    1.00    0.99    update-zipf_range=100
1.00    1.00    0.99    write-only_range=10000

Results: Postgres 12 to 18

From 12.22 to 18.0 there are no regressions larger than 2% but many improvements larger than 5% (highlighted in greeen). Postgres continues to do a great job at avoiding regressions over time.

Relative to: 12.22
col-1 : 13.22
col-2 : 14.19
col-3 : 15.14
col-4 : 16.10
col-5 : 17.6
col-6 : 18.0 with the x10b config

col-1   col-2   col-3   col-4   col-5   col-6   point queries
1.06    1.05    1.05    1.09    2.04    2.05    hot-points_range=100
1.01    1.03    1.03    1.02    1.04    1.04    point-query_range=100
1.00    0.99    0.99    1.03    0.99    1.01    points-covered-pk_range=100
1.04    1.03    1.02    1.05    1.01    1.03    points-covered-si_range=100
1.01    1.00    1.01    1.04    1.01    1.02    points-notcovered-pk_range=100
1.01    1.02    1.03    1.05    1.02    1.04    points-notcovered-si_range=100
1.02    1.00    1.02    1.05    1.00    1.02    random-points_range=1000
1.01    1.01    1.01    1.03    1.01    1.02    random-points_range=100
1.01    1.01    1.01    1.02    1.01    1.01    random-points_range=10

col-1   col-2   col-3   col-4   col-5   col-6   range queries with aggregation
0.99    1.00    1.00    1.00    0.99    0.98    range-covered-pk_range=100
1.01    1.01    1.00    1.00    0.99    0.99    range-covered-si_range=100
1.00    1.00    1.01    1.01    1.00    1.00    range-notcovered-pk_range=100
1.00    1.00    1.00    1.01    1.02    1.01    range-notcovered-si_range=100
1.00    1.30    1.19    1.18    1.16    1.20    scan_range=100

col-1   col-2   col-3   col-4   col-5   col-6   range queries without aggregation
1.04    1.02    1.00    1.05    1.02    1.03    read-only-count_range=1000
1.00    1.00    1.03    1.04    1.03    1.04    read-only-distinct_range=1000
1.00    1.00    1.04    1.04    1.06    1.06    read-only-order_range=1000
1.01    1.01    1.04    1.07    1.06    1.07    read-only_range=10000
1.00    1.00    1.01    1.01    1.02    1.01    read-only_range=100
1.00    1.00    1.00    0.99    1.01    0.99    read-only_range=10
1.01    1.01    1.02    1.02    1.03    1.03    read-only-simple_range=1000
1.01    1.00    1.00    1.03    1.02    1.02    read-only-sum_range=1000

col-1   col-2   col-3   col-4   col-5   col-6   writes
1.01    1.02    1.01    1.03    1.13    1.12    delete_range=100
0.99    0.98    0.97    0.98    1.06    1.05    insert_range=100
0.99    1.00    1.00    1.01    1.02    1.02    read-write_range=100
0.99    1.01    1.01    1.01    1.03    1.01    read-write_range=10
1.00    1.00    1.01    1.00    1.09    1.08    update-index_range=100
1.00    1.10    1.09    1.09    1.10    1.09    update-inlist_range=100
1.03    1.05    1.06    1.05    1.15    1.14    update-nonindex_range=100
0.99    0.98    0.99    0.98    1.07    1.06    update-one_range=100
1.01    1.04    1.06    1.05    1.18    1.17    update-zipf_range=100
0.98    1.01    1.01    0.99    1.07    1.07    write-only_range=10000


by Mark Callaghan (noreply@blogger.com)
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September 24, 2025

Murat Demirbas

Four Ivies. Two days.

This is my long-overdue trip report from last summer: July 10–11, 2024. We toured Ivy League campuses to help our rising senior son weigh his options, with our two daughters (our kids are four years apart each) tagging along for an early preview. Day one was Yale and Brown, followed by a night in New Jersey. Day two took us to Princeton and UPenn, then the long drive back to Buffalo. Of course we drove, that's how we roll.

Prelude

Lining up campus tours is its own sport. They are booked months in advance. Pro-tip: when your kid is born, call the colleges to reserve their campus visit. We lucked into two open slots, then hacked together a Python script to snipe cancellations and grabbed the other two. Not proud of this, but that's what it takes if you don't book months in advance.

The U.S. college admissions process is Byzantine. It is a weird mix of ritual and performance. There are entire books about how to write the college essay. I have plenty to say about the so-called holistic review process, but that's for another post. Back in Turkey, I just had to take a National University Entrance exam, and score very high to get placed in to a top university. That was also a broken system and was stressful, but at least there were no essays, no extracurriculars, no culture fit, no campus visits.

Here, though, the campus visit is part of the show. It is especially essential if you are considering to sign a binding early decision. Early decision boosts acceptance odds 3–4x. But it also locks you in. Our son didn't end up doing ED. His top choices didn't offer it, and he didn't want to burn his chances elsewhere.


Yale: Cathedrals and Low Energy

After six hours on the road we rolled into New Haven, paid for street parking, and joined the tour. Yale sits right in the city center, and the architecture hits you: gothic cathedrals and stone facades older than the country itself.

The name still carries weight. Even in Turkey, Yale was known through Yale locks, whose founder was a distant relative of the university's founder, Elihu Yale. The Yale programs are ranked high, the libraries are priceless, and the faculty-to-student ratio is great.

One stop in our campus tour was the Beinecke Library. Its marble-and-granite exterior filters light to protect fragile manuscripts. Our guide told us that in a fire, oxygen would be sucked out to save the books, even at the expense of people inside. Dying for the books is romantic, but the fact-check says this is a myth.

Yale also revealed the Ivy pattern we would see in other stops of our tour: Two years of mandatory dorms, no AP credit (just placement), and an abundance of pride in being  an Ivy.

We noted these as downsides at Yale. There is no strong pitch for undergraduate research. Some buildings are beautiful, others are just tired: 1960s concrete, no AC, worn interiors. On a hot day, it felt even worse. The CS building in particular was old, dark, and smelly. It looked like it was designed by someone who hated students.


Brown: The underrated Ivy

Ninety minutes later we were at Providence, attending the Brown campus tour at 3 pm.

Brown impressed us. The open curriculum gives students great freedom, for example, CS mixed with theater, neuroscience, and entrepreneurship. Research opportunities are emphasized from the start of the tour. Every student writes a senior thesis. Brown supports student research financially, and third- and fourth-year students can TA undergrad classes. Stay for a fifth year and you can leave with a combined MS. The culture is collaborative, not competitive. If you fail a class, it doesn't show up on your transcript. This way students are encouraged to take risks... or quit and be lazy, I don't know.

The campus sits on a hill overlooking Providence, close enough to the city. The faculty are strong, the vibe is progressive, and the students approachable. No Ivy airs here. Did you know that Emma Watson studied here? Our tour guide was excellent. Under his spell, my youngest daughter declared she would apply early decision to Brown when her time comes. Our son liked it too. He pointed out that Brown CS graduates earn the most one year out of college. The CS building is cramped and outdated, but still much better than Yale's. We all walked away charmed.

We drove to Jersey for a hotel. Our dinner was Dave's Hot Chicken. 


Princeton: The Old Country Club

Next morning: Princeton. It has a huge campus. We parked at the stadium, and took a shuttle to the welcome center.

Princeton is historic and prestigious. Einstein once taught here. Princeton still has very strong faculty and a lot of resources. Undergraduates do research for senior thesis. But our tour guide spent more time talking about dining clubs and traditions than academics. It felt hollow. Too polished, too self-satisfied. Brown had been about people. Princeton was about tradition and Ivy airs. Unlike Brown, Princeton does not offer an open curriculum or fifth-year MS. 

One odd scene was a busload of Chinese families arriving with luggage in tow, apparently right out of the airport. They dragged luggages across campus, straight into the tour. Princeton seems to have strong prestige in China.


UPenn: Philly Hustle

From Princeton, it was a short drive to Philadelphia. But what a view change. UPenn is right under the Philly downtown skyscrapers scenery.

UPenn struck us as hands-on and pragmatic. In your first year you get a writing course. In your 3rd and 4th years you write research reports and senior thesis. Double majors are allowed, minors too. The Wharton School of Business looms large: alumni include both Donald Trump and Jho Low, the billion-dollar corruption guy. What are they teaching there?

Food trucks lined the campus streets, serving better meals than many college dining halls. The lamb shawarma was awesome! While UPenn is dead in the center of downtown, they still have compulsory two years dorm stay like the other ivies. Our younger daughter adored the tour guide, adopted her as an older sister, and by the end of the tour, declared UPenn as her new top choice.


Closing

So our ranking is:

  • Brown
  • UPenn
  • Princeton
  • Yale

Brown feels underrated within the Ivies. The Ivies as a whole, though, are overrated. Colleges in general are overrated. These schools still coast on prestige built centuries ago. But the world has changed drastically. If they want to matter in the age of the internet and AI, they will need to adapt.

by Murat (noreply@blogger.com)
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