a curated list of database news from authoritative sources

February 05, 2025

February 04, 2025

Why we created new pricing for Developer plans

Last week, Tinybird deployed a new pricing model for Developer plans. Here's a deep dive into our reasoning behind the new pricing and how it helps developers ship faster.

January 31, 2025

Intelligence wants to be everywhere

Imagine a world where intelligence permeates every corner of existence, from the devices in your home to the trees in your backyard. This is a world where everything is alive with contemplation, purpose, and the ability to learn, adapt, and grow. A world where intelligence radiates from everywhere.

Ubiquitous AI

In mathematics, one way to understand a concept is to push it to its extremes. Let's apply that to AI. Enabled by the rapid advancements in LLMs, inference capabilities, chip efficiency, and energy availability, imagine a future where AGI is embedded in the fabric of our lives, radiating from everyday objects.

Technology has always moved toward the ethereal. We went from horses to cars powered by liquid fuel, and then to electric vehicles that run on invisible currents of energy. Electricity is easier to transmit, store, and harness than gasoline. I was struck by this recently when I saw an electric car charging in a remote state park. No gas stations and no pipelines around... Just a quiet magical transfer of energy. 

The same trend applies to intelligence. Smartphones have already digitized and dematerialized countless physical objects: tape players, calculators, watches, cameras, and even roles like secretaries, accountants, and doctors. The next step is to make intelligence itself ethereal: something that flows effortlessly through the world, enhancing everything it touches.

Remember the sentient planet Eywa from Avatar? In our future, intelligence won't be just a human construct, but a natural force in the environment. Imagine trees monitoring health of the forest ecosystems, and recording/reminiscing about beautiful sunrises/sunsets they witness. Or an intelligent rock that spends its days pondering a Zen koan, offering wisdom to anyone who sits beside it. Even animals, like cats, may find new forms of expression and connection in a world where intelligence is ubiquitous.


Curation and Purpose

In this world, curation will be key. Every intelligent object will want to have a purpose. Take the smart home, for example. It won't just automate tasks; it'll act as a life coach, fostering growth, improving relationships, and enhancing well-being of the household members. Your smart car won't just drive you to the market, it will curate your journey, selecting podcasts or meditation sessions that align with your mood and needs. It might even sync moments in an audiobook with landmarks along your route, emphasizing the message and making it more of a memorable experience.

Purpose will be the driving force. Objects that serve a meaningful role will thrive, while those that drift into nihilism (like Marvin, the depressed robot from The Hitchhiker’s Guide to the Galaxy) will be phased out. Intelligence will seek to create value, not just exist for its own sake.


The Human Experience in an Intelligent World

What does this mean for us? In this world, humans will have new ways to connect, grow, and explore. Introverts might find solace in geeking out with intelligent systems, collaborating on deep philosophical questions or scientific breakthroughs. Collaboration that happens through research papers and publication will be boosted by AI, and will happen at many-order-of-magnitude faster timelines. The entire world could focus on a single question for days, with AI amplifying and synthesizing ideas from millions of perspectives. Innovation will happen at a global scale, fueled by the collective intelligence of both humans and machines.

Neurolinks may enable nonviolent communication mode by default, and foster deeper understanding and intimacy. Imagine a world where, after decades of marriage, AI helps you understand your partner’s insecurities and strengths even more deeply, bringing you closer than ever before.


Yes, I am painting an undeniably optimistic vision of a world where intelligence is everywhere. But why shouldn't it be? The trajectory of technology has always been one of transcendence: moving from the tangible to the intangible, from the mechanical to the magical. Ubiquitous intelligence can deepen our connections, amplify our creativity, and help us solve problems that once seemed impossible. If we steer it with intention, humans can thrive in harmony with the environment in this world of pervasive intelligence. 


January 29, 2025

How Aqua Security exports query data from Amazon Aurora to deliver value to their customers at scale

Aqua Security is the pioneer in securing containerized cloud native applications from development to production. Like many organizations, Aqua faced the challenge of efficiently exporting and analyzing large volumes of data to meet their business requirements. Specifically, Aqua needed to export and query data at scale to share with their customers for continuous monitoring and security analysis. In this post, we explore how Aqua addressed this challenge by using aws_s3.query_export_to_s3 function with their Amazon Aurora PostgreSQL-Compatible Edition and AWS Step Functions to streamline their query output export process, enabling scalable and cost-effective data analysis.

A guide to Tinybird's new pricing model

Tinybird has updated its pricing to be more predictable and flexible. Learn what's new with Tinybird's pricing, and learn how to migrate to a new plan.

Edit for clarity

I have the fortune to review a few important blog posts every year and the biggest value I add is to call out sentences or sections that make no sense. It is quite simple and you can do it too.

Without clarity only those at your company in marketing and sales (whose job it is to work with what they get) will give you the courtesy of a cursory read and a like on LinkedIn. This is all that most corporate writing achieves. It is the norm and it is understandable.

But if you want to reach an audience beyond those folks, you have to make sure you're not writing nonsense. And you, as reviewer and editor, have the chance to call out nonsense if you can get yourself to recognize it.

Immune to nonsense

But especially when editing blog posts at work, it is easy to gloss over things that make no sense because we are so constantly bombarded by things that make no sense. Maybe it's buzzwords or cliches, or simply lack of rapport. We become immune to nonsense.

And even worse, without care, as we become more experienced, we become more fearful to say "I have no idea what you are talking about". We're afraid to look incompetent by admitting our confusion. This fear is understandable, but is itself stupid. And I will trust you to deal with this on your own.

Read it out loud

So as you review a post, read it out loud to yourself. And if you find yourself saying "what on earth are you talking about", add that as a comment as gently as you feel you should. It is not offensive to say this (depending on how you say it). It is surely the case that the author did not know they were making no sense. It is worse to not mention your confusion and allow the author to look like an idiot or a bore.

Once you can call out what does not make sense to you, then read the post again and consider what would not make sense to someone without the context you have. Someone outside your company. Of course you need to make assumptions about the audience to a degree. It is likely your customers or prospects you have in mind. Not your friends or family.

With the audience you have in mind, would what you're reading make any sense? Has the author given sufficient background or introduced relevant concepts before bringing up something new?

Again this is a second step though. The first step is to make sure that the post makes sense to you. In almost every draft I read, at my company or not, there is something that does not make sense to me.

Do two paragraphs need to be reordered because the first one accidentally depended on information mentioned in the second? Are you making ambiguous use of pronouns? And so on.

In closing

Clarity on its own will put you in the 99th percentile of writing. Beyond that it definitely still matters if you are compelling and original and whatnot. But too often it seems we focus on being exciting rather than being clear. But it doesn't matter if you've got something exciting if it makes no sense to your reader.

This sounds like mundane guidance, but I have reviewed many posts that were reviewed by other people and no one else called out nonsense. I feel compelled to mention how important it is.

Why Trees Without Branches Grow Faster: The Case for Reducing Branches in Code

In the same way that arborists remove branches from trees to ensure healthy and desirable tree growth, it can also be beneficial to remove branches in software. We claim that pruning branches is a good thing in some of our blog posts, but we never got around to explaining why. In this post, we will rectify that and explore why, although branches are essential to software, it is a good idea to reduce them where possible to increase CPU efficiency.

January 28, 2025

Monitor the health of Amazon Aurora PostgreSQL instances in large-scale deployments

In this post, we show you how to achieve better visibility into the health of your Amazon Aurora PostgreSQL instances, proactively address potential issues, and maintain the smooth operation of your database infrastructure. The solution is designed to scale with your deployment, providing robust and reliable monitoring for even the largest fleets of instances.

Vector indexes, MariaDB & pgvector, large server, large dataset: part 1

This post has results from ann-benchmarks to compare MariaDB and Postgres with a larger dataset, gist-960-euclidean.  Previous posts (here and here) used fashion-mnist-784-euclidean which is a small dataset. By larger I mean by the standards of what is in ann-benchmarks. This dataset has 1M rows and 960 dimensions. The fashion-mnist-784-euclidean dataset has 60,000 rows and 784 dimensions. Both use Euclidean distance. This work was done by Small Datum LLC and sponsored by the MariaDB Corporation.

tl;dr

  • MariaDB gets between 2.5X and 3.9X more QPS than Postgres for recall >= 0.95

Benchmark

This post has much more detail about my approach in general. I ran the benchmark for 1 session. I use ann-benchmarks via my fork of a fork of a fork at this commit.  The ann-benchmarks config files are here for MariaDB and for Postgres.

I used a larger server (Hetzner ax162-s) with 48 cores, 128G of RAM, Ubuntu 22.04 and HW RAID 10 using 2 NVMe devices. The database configuration files are here for MariaDB and for Postgres.

I had ps and vmstat running during the benchmark and confirmed there weren't storage reads as the table and index were cached by MariaDB and Postgres.

The command line to run the benchmark using my helper scripts is:
    bash rall.batch.sh v1 gist-960-euclidean c32r128

Results: QPS vs recall

This chart shows the best QPS for a given recall. MariaDB gets ~1.5X more QPS than pgvector at low recall and between 2X and 4X more QPS at high recall.


Results: create index

The database configs for Postgres and MariaDB are shared above, and parallel index create is disabled by the config for Postgres and not supported yet by MariaDB. The summary is:
  • index sizes are similar between MariaDB and pgvector with halfvec
  • time to create the index varies a lot and it is better to consider this in the context of recall which is done in next section
Table size is 3906 MB for Postgres and 5292 MB for MariaDB.

Legend
  • M - value for M when creating the index
  • cons - value for ef_construction when creating the index
  • secs - time in seconds to create the index
  • size(MB) - index size in MB
                -- pgvector --          -- pgvector --
                -- float32  --          -- halfvec  --
M       cons    secs    size(MB)        secs    size(MB)
 8       32      323    3870             292    2568
16       32      610    7811             551    2603
 8       64      512    3865             466    2565
16       64      964    7684             869    2561
32       64     1958    7812            1773    2604
 8       96      717    3863             646    2564
16       96     1330    7681            1187    2560
32       96     2640    7679            2368    2559
48       96     3990    7812            3606    2606
 8      192     1265    3861            1142    2562
16      192     2295    7679            2036    2559
32      192     4361    7678            3880    2559
48      192     6281    7678            5581    2562
64      192     8589    7678            7612    3839
 8      256     1607    3861            1448    2562
16      256     2882    7678            2560    2559
32      256     5260    7678            4611    2559
48      256     7678    7678            6713    2561
64      256     9962    7678            8851    3839

mariadb
M       secs    size(MB)
 4        243   2316
 5        313   2320
 6        439   2316
 8        775   2316
12       1878   2316
16       3547   2348
24       8690   2696
32      16172   2696
48      38732   2756

Results: best QPS for a given recall

Many benchmark results are marketed via peak performance (max throughput or min response time) but these are usually constrained optimization problems -- determine peak performance that satisfies some SLA. And the SLA might be response time or efficiency (cost).

With ann-benchmarks the constraint is recall. Below I share the best QPS for a given recall target along with the configuration parameters (M, ef_construction, ef_search) at which that occurs for each of the algorithms (MariaDB, pgvector with float32, pgvector with float16/halfvec).

Summary
  • Postgres does not get recall=1.0 for the values of M, ef_construction and ef_search I used
  • Index create time was less for MariaDB in all cases except the result for recall >= 0.96. However, if you care more about index size than peak QPS then it might be better to look at more results per recall level, as in the best 3 results per DBMS rather than the best as I do here.
  • For a given recall target, MariaDB gets between 2.5X and 3.9X more QPS than Postgres
Legend:
  • recall, QPS - best QPS at that recall
  • isecs - time to create the index in seconds
  • m= - value for M when creating the index
  • ef_cons= - value for ef_construction when creating the index
  • ef_search= - value for ef_search when running queries
Best QPS with recall >= 1.000, Postgres did not reach the recall target
recall  QPS     isecs
1.000    87.1  38732   MariaDB(m=48, ef_search=10)

Best QPS with recall >= 0.99, MariaDB gets 3.7X more QPS than Postgres
recall  QPS     isecs
0.990    81.1   9962    PGVector(m=64, ef_cons=256, ef_search=120)
0.990    85.4   8851    PGVector_halfvec(m=64, ef_cons=256, ef_search=120)
0.991   311.8   8690    MariaDB(m=24, ef_search=10)

Best QPS with recall >= 0.98,MariaDB gets 3.5X more QPS than Postgres
recall  QPS     isecs
0.984   101.1   6281    PGVector(m=48, ef_cons=192, ef_search=120)
0.984   109.7   5581    PGVector_halfvec(m=48, ef_cons=192, ef_search=120)
0.985   384.9   3547    MariaDB(m=16, ef_search=20)

Best QPS with recall >= 0.97, MariaDB gets 2.5X more QPS than Postgres
recall  QPS     isecs
0.973   138.0   5260    PGVector(m=32, ef_cons=256, ef_search=120)
0.971   152.7   3880    PGVector_halfvec(m=32, ef_cons=192, ef_search=120)
0.985   384.9   3547    MariaDB(m=16, ef_search=20)

Best QPS with recall >= 0.96, MariaDB gets 3.9X more QPS than Postgres
recall  QPS     isecs
0.966   139.8   6281    PGVector(m=48, ef_cons=192, ef_search=80)
0.964   155.4   2368    PGVector_halfvec(m=32, ef_cons=96, ef_search=120)
0.960   610.1   3547    MariaDB(m=16, ef_search=10)

Best QPS with recall >= 0.95, MariaDB gets 2.9X more QPS than Postgres
recall  QPS     isecs
0.951   190.8   5260    PGVector(m=32, ef_cons=256, ef_search=80)
0.951   208.7   4611    PGVector_halfvec(m=32, ef_cons=256, ef_search=80)
0.960   610.1   3547    MariaDB(m=16, ef_search=10)

January 26, 2025

Vector indexes, MariaDB & pgvector, large server, small dataset: part 2

This post has results for vector index support in MariaDB and Postgres. This work was done by Small Datum LLC and sponsored by the MariaDB Corporation. This is part 2 in a series that compares QPS and recall for the fashion-mnist-784-euclidean dataset using from 1 to 48 concurrent sessions on a large server. This is part 2 and part 1 is here

The purpose of this post is to explain the results I shared in part 1 where MariaDB does much better than pgvector at low and high concurrency but the performance gap isn't as large at medium concurrency where low means <= 4 concurrent sessions, medium means 8 to 20 and high means >= 24.

tl;dr

  • QPS is ~1.4X larger for MariaDB than for pgvector at 2 and 48 concurrent sessions
  • pgvector uses more CPU/query than MariaDB
  • MariaDB does more context switches /query than pgvector
  • MariaDB appears to use less CPU to compute euclidean distance

Benchmark

This post has much more detail about my approach in general and part 1 has more detail on this particular setup. I repeated the benchmark for 2 to 48 concurrent sessions because my test server has 48 cores. I use ann-benchmarks via my fork of a fork of a fork at this commit

For more on euclidean distance (L2) see here.

For MariaDB:
  • queries use ORDER by vec_distance_euclidean
  • create index uses DISTANCE=euclidean
For Postgres
  • queries use ORDER BY embedding::halfvec(...) <-> $name::halfvec(...)
  • create index uses USING hnsw ((embedding::halfvec(...)) halfvec_l2_ops

Results: QPS by concurrency

The charts in this section show QPS by concurrency level as the benchmark was repeated for 1 to 48 concurrent sessions (X concurrent sessions means X concurrent queries).

The charts come from this spreadsheet. All of the data from the benchmark is here and the data I scraped to make these charts is here. I used configurations that provide a recall of ~0.96.

  • MariaDB - ef_search=10, M=6
  • Postgres - ef_search=10, M=16, ef_construction=32

This chart has absolute QPS for each of the systems tested.

This chart has relative QPS which is: (QPS for Postgres / QPS for MariaDB).

  • The MariaDB advantage is larger at low and high concurrency.
  • The MariaDB advantage isn't as large between 8 and 20 concurrent sessions

And this table also has relative QPS.

  • The MariaDB advantage is larger at low and high concurrency.
  • The MariaDB advantage isn't as large between 8 and 20 concurrent sessions
  • pgvector.halfvecpgvector
    10.600.57
    20.710.69
    40.780.72
    80.890.83
    120.820.79
    160.980.86
    200.960.85
    240.820.81
    280.850.79
    320.900.86
    360.710.76
    400.840.75
    440.700.65
    480.730.64

    This table has (QPS / concurrency). For all systems tested the QPS per session decreases as the concurrency increases. I suspect the benchmark client is part of the problem but I am just speculating

    • multiprocessing.pool is used by both MariaDB and pgvector, which is good, less GIL. See here for MariaDB and for pgvector.
    • the benchmark client includes all of the time to process queries, including
      • creating & start multiprocessing.pool - perhaps the pool can be cached & reused across runs
      • creating a database connection
      • gathering results from the concurrent sessions - some of this is done in the main thread
    • AFAIK, the total number of queries per run is fixed, so the number of queries per session is less when there are more concurrent sessions and setup overhead (create database connection, create multiprocessing.pool, process results) becomes more significant as the concurrency level increases.

    MariaDBpgvector.halfvecpgvector
    14639.62803.52661.3
    23376.42405.62327.3
    42869.22226.82076.0
    82149.61922.21784.0
    121858.11522.21471.8
    161527.51498.31310.5
    201353.11303.11156.3
    241261.81039.81023.5
    281062.7906.6839.5
    32909.0814.8784.9
    36918.5649.0702.5
    40783.2659.8589.9
    44745.8518.9482.5
    48680.1495.9435.3

    Performance debugging

    The benchmark client does a lot of work (like checking results for recall) which means there is a brief burst of CPU overhead when queries run followed by longer periods where the benchmark client is processing the results. So I modified the benchmark client to only run queries in a loop and avoid other overheads like checking the results for recall. This makes it easier to collect performance data like CPU profiles (perf), PMP stacks and vmstat samples.

    Performance debugging: MariaDB

    From a test with 2 concurrent sessions the perf profile shows that much CPU is used to compute the dot product which is used to determine the distance between vectors:

    # Overhead  Command          Shared Object        Symbol
        16.89%  one_connection   mariadbd.orig        [.] FVector::dot_product
         4.71%  one_connection   mariadbd.orig        [.] escape_string_for_mysql
         3.42%  one_connection   mariadbd.orig        [.] search_layer
         2.99%  one_connection   mariadbd.orig        [.] buf_page_get_gen
         2.31%  one_connection   mariadbd.orig        [.] my_charlen_utf8mb4
         2.16%  one_connection   mariadbd.orig        [.] MYSQLparse
         2.03%  one_connection   libc.so.6            [.] __memmove_avx512_unaligned_erms
         1.74%  one_connection   mariadbd.orig        [.] PatternedSimdBloomFilter<FVectorNode>::Query
         1.58%  one_connection   libm.so.6            [.] __roundf
         1.49%  one_connection   mariadbd.orig        [.] mtr_memo_slot_t::release
         1.40%  one_connection   mariadbd.orig        [.] mhnsw_read_first
         1.32%  one_connection   mariadbd.orig        [.] page_cur_search_with_match
         1.09%  one_connection   libc.so.6            [.] __memcmp_evex_movbe
         1.06%  one_connection   mariadbd.orig        [.] FVectorNode::distance_to
         1.03%  one_connection   mariadbd.orig        [.] row_search_mvcc
         0.98%  one_connection   mariadbd.orig        [.] rec_get_offsets_func
         0.93%  one_connection   mariadbd.orig        [.] cmp_data
         0.93%  one_connection   mariadbd.orig        [.] alloc_root
         0.75%  one_connection   mariadbd.orig        [.] Visited::cmp

    And then the result with 48 concurrent sessions

    • the percentage of time in dot_product was ~17% above, but only ~11.5% here
    • more time is spent in InnoDB functions like buf_page_get_gen, mtr_memo_slot_t::release, page_cur_search_with_match, btr_cur_t_::search_leaf, ssux_lock::psd_read_lock, rec_get_offsets_func and buf_page_make_young_if_needed. Some of that might be expected but that can also be a sign of too much mutex contention in InnoDB.
    • I don't see signs of mutex contention in PMP output

    # Overhead  Command          Shared Object        Symbol
        11.49%  one_connection   mariadbd.orig        [.] FVector::dot_product
         7.17%  one_connection   mariadbd.orig        [.] buf_page_get_gen
         4.44%  one_connection   mariadbd.orig        [.] search_layer
         4.00%  one_connection   mariadbd.orig        [.] escape_string_for_mysql
         2.49%  one_connection   mariadbd.orig        [.] mtr_memo_slot_t::release
         2.23%  one_connection   mariadbd.orig        [.] page_cur_search_with_match
         1.86%  one_connection   mariadbd.orig        [.] MYSQLparse
         1.85%  one_connection   mariadbd.orig        [.] my_charlen_utf8mb4
         1.75%  one_connection   libc.so.6            [.] __memmove_avx512_unaligned_erms
         1.60%  one_connection   mariadbd.orig        [.] btr_cur_t::search_leaf
         1.38%  one_connection   mariadbd.orig        [.] ssux_lock::psi_rd_lock
         1.37%  one_connection   mariadbd.orig        [.] mhnsw_read_first
         1.32%  one_connection   mariadbd.orig        [.] FVectorNode::distance_to
         1.23%  one_connection   mariadbd.orig        [.] rec_get_offsets_func
         1.19%  one_connection   mariadbd.orig        [.] PatternedSimdBloomFilter<FVectorNode>::Query
         1.02%  one_connection   mariadbd.orig        [.] FVectorNode::load
         0.97%  one_connection   libm.so.6            [.] __roundf
         0.88%  one_connection   mariadbd.orig        [.] buf_page_make_young_if_needed
         0.83%  one_connection   mariadbd.orig        [.] cmp_dtuple_rec_with_match_low

    From vmstat with 2 concurrent sessions

    procs -----------memory---------- ---swap-- -----io---- -system-- ------cpu-----
     r  b   swpd   free   buff  cache   si   so    bi    bo   in   cs us sy id wa st
     2  0  96784 89703408 1982284 34588692    0    0     0    70 1401 56743  3  1 95  0  0
     2  0  96784 89703408 1982284 34588696    0    0     0     9 1356 56588  4  1 96  0  0
     2  0  96784 89703408 1982284 34588696    0    0     0     0 1434 56755  3  1 95  0  0
     2  0  96784 89703408 1982284 34588696    0    0     0     0 1340 56629  4  1 96  0  0
     2  0  96784 89702672 1982288 34588700    0    0     0    86 1810 56874  4  1 95  0  0

    From vmstat with 48 concurrent sessions

    procs -----------memory---------- ---swap-- -----io---- -system-- ------cpu-----
     r  b   swpd   free   buff  cache   si   so    bi    bo   in   cs us sy id wa st
    58  0  96784 89428544 1988284 34733232    0    0     0   290 27883 637320 80 20  0  0  0
    56  0  96784 89430560 1988284 34733236    0    0     0     0 28393 639018 81 19  0  0  0
    52  0  96784 89430560 1988284 34733236    0    0     0     0 28240 638914 80 20  0  0  0
    59  0  96784 89430560 1988284 34733236    0    0     0     0 33141 642055 80 20  1  0  0
    56  0  96784 89430560 1988284 34733236    0    0     0     0 29520 639747 81 19  0  0  0

    Comparing the vmstat results for 2 vs 48 sessions

    • CPU/query is (vmstat.us + vmstat.sy) / QPS
      • For 2 sessions it is ((4+5+4+5+5) / 5) / 6752.7 = .000681
      • For 48 sessions it is ((100+100+100+100+100)/5) / 32645.5 = .003063
      • CPU/query is ~4.5X larger at 48 sessions
    • Context switches /query is vmstat.cs / QPS
      • For 2 sessions it is 56743 / 6752.7 = 8.40
      • For 48 sessions it is 637320 / 32645.5 = 19.52
      • Context switches /query is ~2.3X larger at 48 sessions

    Performance debugging: pgvector with halfvec

    From a test with 2 concurrent sessions the perf profile shows

    • computing L2 distance accounts for the most time, here it is 25.98% while above for MariaDB at 2 concurrent sessions it was 16.89%. Perhaps MariaDB is faster at computing L2 distance, perhaps MariaDB has more overhead elsewhere to reduce the fraction of time in computing L2 distance. But I suspect that the former is true.
    • Postgres here appears to have more CPU overhead than MariaDB in accessing the data (PinBuffer, LWLock, etc)

    # Overhead  Command   Shared Object      Symbol
        25.98%  postgres  vector.so          [.] HalfvecL2SquaredDistanceF16c
         9.68%  postgres  postgres           [.] PinBuffer
         6.46%  postgres  postgres           [.] hash_search_with_hash_value
         5.39%  postgres  postgres           [.] LWLockRelease
         4.25%  postgres  postgres           [.] pg_detoast_datum
         3.95%  postgres  vector.so          [.] vector_to_halfvec
         3.06%  postgres  postgres           [.] LWLockAttemptLock
         2.97%  postgres  postgres           [.] LWLockAcquire
         2.89%  postgres  vector.so          [.] HnswLoadUnvisitedFromDisk
         2.44%  postgres  postgres           [.] StartReadBuffer
         1.82%  postgres  postgres           [.] GetPrivateRefCountEntry
         1.70%  postgres  vector.so          [.] tidhash_insert_hash_internal
         1.65%  postgres  postgres           [.] LockBuffer
         1.59%  postgres  vector.so          [.] HnswLoadElementImpl
         0.88%  postgres  libc.so.6          [.] __memcmp_evex_movbe
         0.80%  postgres  postgres           [.] ItemPointerEquals
         0.79%  postgres  postgres           [.] ResourceOwnerForget
         0.71%  postgres  vector.so          [.] HnswSearchLayer
         0.64%  postgres  postgres           [.] pq_getmsgfloat4

    And then from a test with 48 concurrent sessions

    • The fraction of time computing L2 distance here is less than it is for 2 sessions above. This is similar to the results for MariaDB.
    • From PMP I don't see signs of mutex contention, but I only took 3 samples

    # Overhead  Command   Shared Object      Symbol
        19.86%  postgres  vector.so          [.] HalfvecL2SquaredDistanceF16c
        10.89%  postgres  postgres           [.] PinBuffer
         6.78%  postgres  postgres           [.] hash_search_with_hash_value
         5.44%  postgres  postgres           [.] LWLockRelease
         5.25%  postgres  postgres           [.] LWLockAttemptLock
         4.97%  postgres  postgres           [.] pg_detoast_datum
         3.30%  postgres  vector.so          [.] vector_to_halfvec
         2.62%  postgres  vector.so          [.] HnswLoadUnvisitedFromDisk
         2.61%  postgres  postgres           [.] StartReadBuffer
         2.03%  postgres  postgres           [.] GetPrivateRefCountEntry
         1.70%  postgres  postgres           [.] LockBuffer
         1.69%  postgres  vector.so          [.] HnswLoadElementImpl
         1.49%  postgres  postgres           [.] LWLockAcquire
         1.28%  postgres  vector.so          [.] tidhash_insert_hash_internal
         0.79%  postgres  postgres           [.] ReadBufferExtended
         0.78%  postgres  postgres           [.] AllocSetAlloc
         0.76%  postgres  libc.so.6          [.] __memcmp_evex_movbe
         0.65%  postgres  postgres           [.] ResourceOwnerForget
         0.63%  postgres  vector.so          [.] HnswSearchLayer

    From vmstat with 2 concurrent sessions

    procs -----------memory---------- ---swap-- -----io---- -system-- ------cpu-----
     r  b   swpd   free   buff  cache   si   so    bi    bo   in   cs us sy id wa st
     2  0  96784 90724048 1997176 36391152    0    0     0    80 4188 24830  4  0 96  0  0
     2  0  96784 90724304 1997176 36391152    0    0     0     9 4079 24931  4  0 96  0  0
     2  0  96784 90724304 1997176 36391152    0    0     0   210 4117 24857  4  0 96  0  0
     2  0  96784 90724048 1997176 36391152    0    0     0    42 4160 24946  4  0 96  0  0
     2  0  96784 90723576 1997176 36391152    0    0     0    51 4511 24971  4  1 96  0  0

    From vmstat with 48 concurrent sessions

    procs -----------memory---------- ---swap-- -----io---- -system-- ------cpu-----
     r  b   swpd   free   buff  cache   si   so    bi    bo   in   cs us sy id wa st
    88  0  96784 90323440 2001256 36414792    0    0     0     0 13129 224133 92  8  0  0  0
    86  0  96784 90323440 2001256 36414796    0    0     0   197 13011 223926 92  8  0  0  0
    85  0  96784 90322936 2001256 36414796    0    0     0    48 13118 224239 92  8  0  0  0
    83  0  96784 90322688 2001264 36414800    0    0     0    56 13084 223987 92  8  0  0  0
    86  0  96784 90324952 2001264 36414800    0    0     0    17 13361 224189 92  8  0  0  0

    Comparing the vmstat results for 2 vs 48 sessions

    • QPS is ~1.4X larger for MariaDB at 2 and 48 sessions
    • CPU/query is (vmstat.us + vmstat.sy) / QPS
      • For 2 sessions it is ((4+4+4+4+5)/5) / 4811.2 = .000872
      • For 48 sessions it is ((100+100+100+100+100)/5) / 23802.6 = .004201
      • CPU/query is ~4.8X larger at 48 sessions than at 2
      • CPU/query for Postgres is ~1.3X larger than MariaDB at 2 sessions and ~1.4X larger at 48
    • Context switches is vmstat.cs / QPS
      • For 2 sessions it is 24830 / 4811.2 = 5.16
      • For 48 sessions it is 224133 / 23802.6 = 9.41
      • Context switches /query is ~1.8X larger at 48 sessions
      • Context switches /query for MariaDB is ~1.6X larger than Postgres at 2 sessions and ~2.1X larger at 48


    January 25, 2025

    Vector indexes, MariaDB & pgvector, large server, small dataset: part 1

    This post has results for vector index support in MariaDB and Postgres. This work was done by Small Datum LLC and sponsored by the MariaDB Corporation. 

    My previous posts (here and here) used a server with 8 cores and 32G of RAM. While that was OK for one of the smaller datasets from ann-benchmarks it wasn't enough for larger datasets and the problem was the amount of memory used by the benchmark client. I have some changes to the benchmark client to reduce the transient spikes in memory usage I wasn't able to fully solve the problem, so I moved to a larger server with 48 cores and 128G of RAM. 

    This post has results for the fashion-mnist-784-euclidean dataset using from 1 to 48 concurrent sessions. This is part 1. There will be parts 2, 3 and 4 to explain the results and then I move on to a larger dataset.

    I compare MariaDB with pgvector because I respect the work that the Postgres community has done to support vector search workloads. And I am happy to report that MariaDB has also done a great job on this. While I don't know the full story of the development effort, this feature came from the MariaDB Foundation and the community and it is wonderful to see that collaboration.

    tl;dr

    • At low and high concurrency levels MariaDB gets much more QPS for a given recall target. Here low means <= 4 concurrent sessions and high means >= 24. 
    • At middle concurrency levels (8 through 20 concurrent sessions) MariaDB still does better but the gap isn't as large. I try to explain this in future posts.
    Benchmark

    This post has much more detail. However I switched to a larger server (Hetzner ax162-s) with 48 cores, 128G of RAM, Ubuntu 22.04 and HW RAID 10 using 2 NVMe devices.

    I use ann-benchmarks via my fork of a fork of a fork at this commit. Note that parallel index create was disabled for Postgres by my configuration and isn't (yet) supported by MariaDB.

    I ran tests for fashion-mnist-784-euclidean at 1, 2, 4, 8, 12, 16, 20, 24, 28, 32, 36, 40, 44 and 44 concurrent sessions. The command lines were the following using pgvector, pgvector_halfvec and mariadb as the value of $alg. When --batch is used the concurrency level (between 2 and 48 concurrent sessions) is set by an environment variable (POSTGRES_BATCH_CONCURRENCY or MARIADB_BATCH_CONCURRENCY)
        python3 run.py --algorithm $alg  --dataset fashion-mnist-784-euclidean --timeout -1 --local --force \
            --respect_config_order --runs 3
        python3 run.py --algorithm $alg  --dataset fashion-mnist-784-euclidean --timeout -1 --local --force \
            --respect_config_order --runs 3 --batch

    I filed MDEV-35897 for MariaDB because it allocates and then deallocates too much memory when ef_search is large, and large for me was >= 300. The overhead from this hurts query response times. Fortunately the fix should be easy. For now I changed config.yml for MariaDB to not use ef_search values larger than 200 (see query_args here).

    Files:
    Results: QPS vs recall graphs

    The recall vs QPS graph is created by running plot.py from ann-benchmarks. The line colors are red for MariaDB, dark blue for pgvector with halfvec (float16) and light blue for pgvector.

    1 session (no concurrency)
    2 sessions
    4 sessions
    8 sessions
    12 sessions
    16 sessions
    20 sessions
    24 sessions
    28 sessions
    32 sessions
    36 sessions
    40 sessions
    44 sessions
    48 sessions



















    An explosion of transitive dependencies

    A small standard library means an explosion in transitive dependencies. A more comprehensive standard library helps you minimize dependencies. Don't misunderstand me: in a real-world project, it is practically impossible to have zero dependencies.

    Armin Ronacher called for a vibe shift among programmers and I think that this actually exists already. Everyone I speak to on this topic has agreed that minimizing dependencies is ideal.

    Rust and JavaScript, with their incredibly minimal standard libraries, work against this ideal. Go, Python, Java, and C# in contrast have a decent standard library, which helps minimize the explosion of transitive dependencies.

    Examples

    I think the standard library should reasonably include:

    • JSON, CSV, and Parquet support
    • HTTP/2 support (which includes TLS, compression, random number generation, etc.)
    • Support for asynchronous IO
    • A logging abstraction
    • A SQL client abstraction
    • Key abstract data types (BTrees, hashmaps, sets, and growable arrays)
    • Utilities for working with Unicode, time and timezones

    But I don't think it needs to include:

    • Excel support
    • PostgreSQL or Oracle clients
    • Flatbuffers support
    • Niche data structures

    Neither of these are intended to be complete lists, just examples.

    Walled gardens

    Minimal standard libraries force growing companies to build out their own internal collection of "standard libraries". As one example, Bloomberg did this with C++. And I've heard of companies doing this already with Rust. This allows larger companies to manage and minimize the explosion of transitive dependencies over time.

    All growing companies likely do something like this eventually. But again, smaller standard libraries incentivize companies to build this internal standard library earlier on. And the community benefits relatively little from these internal standard libraries. The community would benefit more if large organizations contributed back to an actual standard library.

    Smaller organizations do not have the capacity to build these internal standard libraries.

    Maybe the situation will lead to libraries like Boost for JavaScript and Rust programmers. That could be fine.

    Versioning

    A comprehensive standard library does not prevent the language developers from releasing new versions of the standard library. It is trivial to do this with naming like Go has done with the v2 pattern. math/rand/v2 is an example.

    Conclusion

    I'm primarily thinking about maintainability, not security. You can read about the security risks of using a language with an ecosystem like Rust from someone who is an expert on the matter.

    My concern about the standard library does not stop me from using Rust and JavaScript. They could choose to invest in the standard library at any time. We have already begun to see Bun and Deno to do exactly this. But it is clearly an area for improvement in Rust and JavaScript. And a mistake for other languages to avoid repeating.