Agree with others saying HN needs more content like this!
After reading I don’t get how locks held in memory affect WAL shipping. WAL reader reads it in a single thread, updates in-memory data structures periodically dumping them on disk. Perhaps you want to read one big instruction from WAL and apply it to many buffers using multiple threads?
>Adapting algorithms to work atomically at the block level is table stakes for physical replication
Why? To me the only thing you have to do atomically is WAL write. WAL readers read and write however they want given that they can detect partial writes and replay WAL.
>If a VACUUM is running on the primary at the same time that a query hits a read replica, it's possible for Postgres to abort the read.
The situation you referring to is:
1. Record inserted
2. Standby long query started
3. Record removed
4. Primary vacuum started
5. Vacuum replicated
6. Vacuum on standby cannot remove record because it is being read by the long query.
7. PG cancels the query to let vacuum proceed.
I guess your implementation generates a lot of dead tuples during compaction. You clearly fighting PG here. Could a custom storage engine be a better option?
After reading I don’t get how locks held in memory affect WAL shipping.
WAL reader reads it in a single thread, updates in-memory data structures
periodically dumping them on disk. Perhaps you want to read one big
instruction from WAL and apply it to many buffers using multiple threads?
We currently use an un-modified/generic WAL entry, and don't implement our own replay. That means we don't control the order of locks acquired/released during replay: and the default is to acquire exactly one lock to update a buffer.
But as far as I know, even with a custom WAL entry implementation, the maximum in one entry would still be ~8k, which might not be sufficient for a multi-block atomic operation. So the data structure needs to support block-at-a-time atomic updates.
I guess your implementation generates a lot of dead tuples during
compaction. You clearly fighting PG here. Could a custom storage
engine be a better option?
`pg_search`'s LSM tree is effectively a custom storage engine, but it is an index (Index Access Method and Custom Scan) rather than a table. See more on it here: https://www.paradedb.com/blog/block_storage_part_one
LSM compaction does not generate any dead tuples on its own, as what is dead is controlled by what is "dead" in the heap/table due to deletes/updates. Instead, the LSM is cycling blocks into and out of a custom free space map (that we implemented to reduce WAL traffic).
My last big co, we had a team of 10 who's entire job was to sync data from Postgres into Elastic. It would take weeks and fallover regularly due to traffic.
If we could have a DB that could do search and be a store of record, it would be amazing.
They're different access patterns, though. Are there no concerns about performance and potentially blocking behavior? Decoupling OLTP and analytics is frequently done with good reason: 1/to allow the systems to scale independently, and 2/to help prevent issues with one component from impacting the other (i.e., contain blast radius). I wouldn't want a failure of my search engine to also take down my transaction system.
You don't need to. Customers usually deploy us on a standalone replica(s) on their Postgres cluster. If a query were to take it down, it would only take down the replica(s) dedicated to ParadeDB, leaving the primary and all other read replicas dedicated to OLTP safe.
Are you saying that the cluster isn't homogenous? It sounds like you're describing an architecture that involves a cluster that has two entirely different pieces of software on it, and whose roles aren't interchangeable.
Bear with me, this will be a bit of a longer answer. Today, there are two topologies under which people deploy ParadeDB.
- <some managed Postgres service> + ParadeDB. Frequently, customers already use a managed Postgres (e.g. AWS RDS) and want ParadeDB. In that world, they maintain their managed Postgres service and deploy a Kubernetes cluster running ParadeDB on the side, with one primary instance and some number of replicas. The AWS RDS primary sends data to the ParadeDB primary via logical replication. You can see a diagram here: https://docs.paradedb.com/deploy/byoc
In this topology, the OLTP and search/OLAP workloads are fully isolated from each other. You have two clusters, but you don't need a third-party ETL service since they're both "just Postgres".
- <self-hosted Postgres> + ParadeDB. Some customers, typically larger ones, prefer to self-host Postgres and want to install our Postgres extension directly. The extension is installed in their primary Postgres, and the CREATE INDEX commands must be issued on the primary; however, they may route reads only to a subset of the read replicas in their cluster.
In this topology, all writes could be directed to the primary, all OLTP read queries could be routed to a pool of read replicas, and all search/OLAP queries could be directed to another subset of replicas.
Both are completely reasonable approaches and depend on the workload. Hope this helps :)
If you have a ETL pipeline specialized for PG logical replication (as opposed to generic JVM based Debizium/Kafka setups), you get some fraction of the same benefits. I'm curious about Conduit and its postgres plugin.
That leaves: ParadeDB uses vanilla postgres + rust extension. This is a technology detail. I was looking for an articulation of the customer benefit because of this technologically appealing architecture.
Since we both worked there: I can think of a few places at Segment where we'd have added more reporting/analytics/search if it weren't such a pain to set up a OLAP copy of our control plane databases. Remember how much engineering effort we spent on teams that did nothing but control plane database stuff?
Data plane is a different story, but not everything is 1m+ RPS.
It's not going to happen anytime soon, because you simply cannot cheat physics.
A system that supports OLAP/ad-hoc queries is going to need a ton of IOPs & probably also CPU capacity to do your data transformations. If you want this to also scale beyond the capacity limits of a single node, then you're going to run into distributed joins and network becomes a huge factor.
Now, to support OLTP at the same time, your big, distributed system needs to support ACID, be highly fault-tolerant, etc.
All you end up with is a system that has to be scaled in every dimension. It needs to support the maximum possible workloads you can throw at it, or else a random, expensive reporting query is going to DOS your system and your primary customer-facing system will be unusable at the same time. It is sort of possible, but it's going to cost A LOT of money. You have to have tons and tons of "spare" capacity.
Which brings us to the core of engineering -- anyone can build a system that burns dump trucks full of venture capital dollars to create the one-system-to-rule-them-all. But businesses that want to succeed need to optimize their costs so their storage systems don't break the bank. This is why the current status-quo of specialized systems that do one task well isn't going to change. The current technology paradigm cannot be optimized for every task simultaneously. We have to make tradeoffs.
* a primary transactional DB that I can write fast, with ACID guarantees and a read-after-write guarantee, and allows failover
* one (or more) secondaries that are optimized for analytics and search. This should also tell me how caught up the system is, with the primary.
If they all can talk the same language (SQL) and can replicate from primary with no additional tools/technology (postgres replcation for example), I will take it any day.
It is about operational simplicity and not needing intimately to know multiple technologies. Granted, even if this is "just" postgresql, it really is not and all customizations will have their own tuning and whatnot, but the context is all still postgresql.
Yes, this will not magically solve the CAP theorem, but for most cases we don't need to care too much
Yeah, in general, I think a lot of businesses would love to skip ETL pipelines if possible / consolidate data. Postgres is a very much a neutral database to extend upon, maybe a wild analogy but it's the canola oil of databases
Total tangent, but I think "Canola is a neutral oil" is a lie. It's got the most distinctive (and in my opinion, bad) flavor of the common cooking oils.
If canola oil tastes like something, it's really disgusting IMO. I kinda hate the stuff even though my dad made good money growing it. OTOH, the very sweet smell of the plant's flowers is pleasant enough if pretty basic and the honey is similar.
Once upon a time, I was using postgres for OLTP and OLAP purposes combined with in-database transforms using TimescaleDB. I had a schema for optimized ingestion and then several aggregate views which produced a bunch of purpose-specific "materialized" tables for efficient analysis based on the ingestion tables.
Timescale had a nice way of abstracting away the cost of updating these views without putting too much load on ingestion (processing multiple TBs of data a time in a single instance with about 500Gb of data churn daily).
One db that could be interesting here is CrateDB. It's a Lucene based DB that supports the postgres wire protocol. So you can run SQL queries against it.
I've tried figuring out if it supports acting as a pg read-replica, which sounds to me like the ideal set up - but it doesn't seem to be supported.
I have no affiliation to them, just met the team at an event and thought it sounded cool.
One of the ParadeDB maintainers here -- Being PostgreSQL wire protocol compatible is very different from being built inside Postgres on top of the Postgres pages, which is what ParadeDB does. You still need the "T" in ETL, e.g. transforming data from your source into the format of the sink (in your example CrateDB). This is where ETL costs and brittleness come into play.
So, I'm not versed enough in legal matters to be certain about this, so I tend to fallback to caution, but (A) customers I've worked with in the past seem to be wary of such copyleft licenses and (B) the contagious nature of such license would make me think twice about using it in a project of my own as well.
It would be nice to have such notion challenged but I'm not sure what would change my mind.
I would expect that most commercial companies that use Grafana would obtain a commercial license?
For JOINs? Absolutely! Who wants to hand-code queries at the executor level?! It's expensive!
You need a query language.
You don't necessarily need ACID, and you don't necessarily need a bunch of things that SQL RDBMSes give you, but you definitely need a QL, and it has to support a lot of what SQL supports, especially JOINs and GROUP BY w/ aggregations.
NoSQLs tend to evolve into having a QL layered on top. Just start with that if you really want to build a NoSQL.
To be clear here, I'm not arguing that OpenSearch/ElasticSearch is an adequate substitute for Postgres. They're different databases, each with different strengths and weaknesses. If you need JOINs and ACID compliance, you should use Postgres. And if you need distributed search, you should use OpenSearch/ElasticSearch.
Unless they're building for single-host scale, you're not going to get JOINs for free. Lucene (the engine upon which ES/OS is based) already has JOIN capability. But it's not used in ES/OS because the performance of JOINs is absolutely abysmal in distributed databases.
I'm arguing that sometimes you don't need ACID, or rather, sometimes you accept that ACID is too painful so you accept not having ACID, but no one ever really doesn't want a QL -- they only think that they don't want a QL until they learn better.
I.e., NoACID does not imply NoQueryLanguage, and you can always have a QL, so you should always get a QL, and you should always use a QL.
> Unless they're building for single-host scale, you're not going to get JOINs for free.
If by 'free' you mean not having to code them, then that's wrong. You can always have or implement a QL.
If by 'free' you mean 'performant', then yes, you might have to denormalize your data so that JOINs vanish, though at the cost of write amplification. But so what, that's true whether you use a QL or not -- it's true in SQL RDBMSes too.
Our customers typically deploy ParadeDB in a primary-replicas topology, with one primary Postgres node and 2 or more read replicas, depending on read volume. Queries are executed on a single node today, yes.
We have plans to eventually support distributed queries.
Obligatory whine that the term NoSQL got co-opted to mean "no relational". There's tons of space for a better query language for querying relation databases.
It's funny; as someone who is exactly pg_search's market, I actually often want the opposite: ACID, MVCC transactions, automatic table and index management... but no query language.
At the data scale + level of complexity our OLAP queries operate at, we very often run into situations where Postgres's very best plan [with a well-considered schema, with great indexes and statistics, and after tons of tuning and coaxing], still does something literally interminable — not for any semantic reason to do with the query plan, but rather due to how Postgres's architecture executes the query plan[1].
The last such job, I thought would be simple enough to run in a few hours... I let it run for six days[2], and then gave up and killed it. Whereas, when we encoded the same "query plan" as a series of bulk-primitive ETL steps by:
1. dumping the raw source data from PG to CSV with a `COPY`,
2. whipping out simple POSIX CLI tools like sort/uniq/grep/awk (plus a few hand-rolled streaming aggregation scripts) to transform/reduce/normalize the source data into the shape we want it in,
3. and then loading the resulting CSVs back into PG with another `COPY`,
...then the runtime of the whole operation was reduced to just a few hours, with the individual steps completing in ~30 minutes each. (And that's despite the overhead of parsing and/or emitting non-string fields from/to CSV with almost every intermediate step!)
Honestly, if Postgres would just let us program it the way one programs e.g. Redis through Lua, or ETS tables in Erlang — where the tables and indices are ADTs with low-level public APIs, and you set up your own "query plan" as a set of streaming-channel actors making calls to these APIs — then we would be a lot happier. But even in PL/pgSQL (which we do use, here and there), the only APIs are high-level ones.
• Sure, you can get a cursor on a query; but you can't e.g. get an LMDB-like B-tree cursor on a target B-tree index, and ask it to jump [i.e. re-nav down from root] or walk [i.e. nav up from current pos to nearest common ancestor then back down] to "the first row-tuple greater-than-or-equal to [key]".
• You can't write your own efficient implementation of TABLESAMPLE semantics to set up your own Bigtable-esque balanced cluster-order-partitioned parallel seq scan.
• You can't collect pointers to row-tuples, partially materialize them, filter them by some criterion on the read (but perhaps not parsed!) columns, and then more-fully materialize those same row-tuples "directly" from the references to them you still hold.
---
[1] One example of what I mean by "execution": did you know that Postgres doesn't use any form of concurrency for query plans — not even the most basic libuv-like "This Merge Append node's child-node A is in a blocking-wait on IO; that blocking-wait should yield, so that the Merge Append node's child-node B can instead send row-tuple batches for a while" kind of concurrency?
---
[2] If you're wondering, the query that ran for six days was literally just this (anonymized):
SELECT a, b, SUM(value) AS total_value
FROM (
SELECT a, b, value FROM source1
UNION ALL
SELECT a, b, value FROM source2
) AS u
GROUP BY a, b;
`source1` and `source2` are ~150GB tables. (Or at least, they're 150GB when dumped to CSV.) Two integer keys (a,b), and a bigint value. With a b-tree index on `(a,b) INCLUDE (value)`, with correct statistics.
And its EXPLAIN query plan looked like this (with `SET enable_hashagg = OFF;`) — nominally pretty good:
GroupAggregate (cost=1.17..709462419.92 rows=40000 width=40)
Group Key: a, b
-> Merge Append (cost=1.17..659276497.84 rows=6691282944 width=16)
Sort Key: a, b
-> Index Only Scan using source1_a_b_idx on source1 (cost=0.58..162356175.31 rows=3345641472 width=16)
-> Index Only Scan using source2_a_b_idx on source2 (cost=0.58..162356175.31 rows=3345641472 width=16)
Each one of the operations here is "obvious." It's what you'd think you'd want! You'd think this would finish quickly. And yet.
(And no, the machine it ran on was not resource-bottlenecked. It had 1TB of RAM with no contention from other jobs, and this PG session was allowed to use much of it as work memory. But even if it was spilling to disk at every step... that should have been fine. The CSV equivalent of this inherently "spills to disk", for everything except the nursery levels of sort(1)'s merge-sort. And it does fine.)
> At the data scale + level of complexity our OLAP queries operate at, we very often run into situations where Postgres's very best plan [with a well-considered schema, with great indexes and statistics, and after tons of tuning and coaxing], still does something literally interminable — not for any semantic reason to do with the query plan, but rather due to how Postgres's architecture executes the query plan[1].
Well, ok, this is a problem, and I have run into it myself. That's not a reason for not wanting a QL. It's a reason for wanting a way to improve the query planning. Query hints in the QL are a bad idea for several reasons. What I would like instead is out-of-band query hints that I can provide along with my query (though obviously only when using APIs rather than `psql`; for `psql` one would have to provide the hints via some \hints commnad) where I would address each table source using names/aliases for the table source / join, and names for subqueries, and so really something like a path through the query and subqueries like `.<sub_query_alias0>.<sub_query_alias1>.<..>.<sub_query_aliasN>.<table_source_alias>` and where the hint would indicate things like what sub-query plan type to use and what index to use.
I mean, in my case, I don't think what I want could be implemented via query hints. The types of things I would want to communicate to the server, are pragmas entirely divorced from the semantics of SQL: pragmas that only make sense if you can force the query's plan to take a specific shape to begin with, because you're trying to tune specific knobs on specific plan nodes, so if those plan nodes aren't part of the final query, then your tuning is meaningless.
And if you're pinning the query plan to a specific shape, then there's really no point in sending SQL + hints; you may as well just expose a lower-level "query-execution-engine abstract-machine bytecode" that the user can submit, to be translated in a very low-level — but contractual! — way into a query plan. Or, one step further, into the thing a query plan does, skipping the plan-node-graph abstraction entirely in favor of arbitrarily calling the same primitives the plan nodes call [in a sandboxed way, because such bytecode should be low-level enough that it can encode invalid operation sequences that will crash the PG connection backend — and this is fine, the user signed up for that; they just want to be assured that such a crash won't affect data integrity outside the current transaction.]
Such a bytecode wouldn't have to be used as the literal compiled internal representation of SQL within the server, mind you. (It'd be ideal if it was, but it doesn't need to be.) Just like e.g. the published and versioned JVM bytecode spec isn't 1:1 with the bytecode ISA the JVM actually uses as its in-memory representation for interpretation — there's module-load-time translation/compilation from the stable public format, to the current internal format.
But your mental model of your query is still in a language, even if it's only natural language. Why wouldn't you write a QL and compiler for it that outputs a query plan AST/bytecode/whatever to your liking? The PG SQL compiler and query planner just isn't to your liking, but you really want to be writing queries by hand? I guess what you're saying is you want something like LinkQ that lets you build complex plans/ASTs w/o the complexity of NoSQL hand-coded queries.
Oh, and BTW, PG is getting async I/O in the next release. It's not the same as concurrency, but if your workloads are I/O-bound (and likely they are) then it's as good as concurrency.
Interestingly enough, it looks like the team was just hacking on an open source extension and organically attracted some customers, which snowballed into raising capital. So definitely seems like there’s a market.
Running elasticsearch is a miserable experience. If you can run one tool that you already have with slightly more effort, amazing. And you never need to think about rebuilding indexes or tuning the garbage collector or planning an ES major version migration.
Because it's a whole another architectural component for data that is already there, in a tool that is made just for that lacking just of `SELECT full_text_search('kitty pictures');`
There can be multiple reasons, one that I can think of right away would be to keep the stack as simple as possible until you can. Realistically speaking most of the companies do not operate at the scale where they would need the specialized tools.
There is a cost associated with adopting and integrating another tool like ElasticSearch. For some orgs, the ROI might not be there. And if their existing database provide some additional capabilities in this space, that might be preferrable.
> This is another one of those “when you have a hammer, everything looks like your thumb” stories.
Are you referring to people who think that every reporting problem must be solved by a dedicated OLAP database?
Hard to blame the CAP theorem since this is a problem across an interface layer. If the DB knew about the data it could manage the LSM tree without issue.
Love this style of no-fluff technical deep dive. HN needs more content like this.
Agree with others saying HN needs more content like this!
After reading I don’t get how locks held in memory affect WAL shipping. WAL reader reads it in a single thread, updates in-memory data structures periodically dumping them on disk. Perhaps you want to read one big instruction from WAL and apply it to many buffers using multiple threads?
>Adapting algorithms to work atomically at the block level is table stakes for physical replication
Why? To me the only thing you have to do atomically is WAL write. WAL readers read and write however they want given that they can detect partial writes and replay WAL.
>If a VACUUM is running on the primary at the same time that a query hits a read replica, it's possible for Postgres to abort the read.
The situation you referring to is: 1. Record inserted 2. Standby long query started 3. Record removed 4. Primary vacuum started 5. Vacuum replicated 6. Vacuum on standby cannot remove record because it is being read by the long query. 7. PG cancels the query to let vacuum proceed.
I guess your implementation generates a lot of dead tuples during compaction. You clearly fighting PG here. Could a custom storage engine be a better option?
Thanks for the questions!
We currently use an un-modified/generic WAL entry, and don't implement our own replay. That means we don't control the order of locks acquired/released during replay: and the default is to acquire exactly one lock to update a buffer.But as far as I know, even with a custom WAL entry implementation, the maximum in one entry would still be ~8k, which might not be sufficient for a multi-block atomic operation. So the data structure needs to support block-at-a-time atomic updates.
`pg_search`'s LSM tree is effectively a custom storage engine, but it is an index (Index Access Method and Custom Scan) rather than a table. See more on it here: https://www.paradedb.com/blog/block_storage_part_oneLSM compaction does not generate any dead tuples on its own, as what is dead is controlled by what is "dead" in the heap/table due to deletes/updates. Instead, the LSM is cycling blocks into and out of a custom free space map (that we implemented to reduce WAL traffic).
> To be an effective alternative to Elasticsearch we needed to support high ingest workloads in real time.
Why not just use OpenSearch or ElasticSearch? The tool is already in the inventory; why use a screwdriver when a chisel is needed and available?
This is another one of those “when you have a hammer, everything looks like your thumb” stories.
Because you don’t need to sync and you have ACID with joins.
Is there a whole business to be had with those advantages alone? I’m curious as to who the target market is.
My last big co, we had a team of 10 who's entire job was to sync data from Postgres into Elastic. It would take weeks and fallover regularly due to traffic.
If we could have a DB that could do search and be a store of record, it would be amazing.
They're different access patterns, though. Are there no concerns about performance and potentially blocking behavior? Decoupling OLTP and analytics is frequently done with good reason: 1/to allow the systems to scale independently, and 2/to help prevent issues with one component from impacting the other (i.e., contain blast radius). I wouldn't want a failure of my search engine to also take down my transaction system.
You don't need to. Customers usually deploy us on a standalone replica(s) on their Postgres cluster. If a query were to take it down, it would only take down the replica(s) dedicated to ParadeDB, leaving the primary and all other read replicas dedicated to OLTP safe.
Are you saying that the cluster isn't homogenous? It sounds like you're describing an architecture that involves a cluster that has two entirely different pieces of software on it, and whose roles aren't interchangeable.
Bear with me, this will be a bit of a longer answer. Today, there are two topologies under which people deploy ParadeDB.
- <some managed Postgres service> + ParadeDB. Frequently, customers already use a managed Postgres (e.g. AWS RDS) and want ParadeDB. In that world, they maintain their managed Postgres service and deploy a Kubernetes cluster running ParadeDB on the side, with one primary instance and some number of replicas. The AWS RDS primary sends data to the ParadeDB primary via logical replication. You can see a diagram here: https://docs.paradedb.com/deploy/byoc
In this topology, the OLTP and search/OLAP workloads are fully isolated from each other. You have two clusters, but you don't need a third-party ETL service since they're both "just Postgres".
- <self-hosted Postgres> + ParadeDB. Some customers, typically larger ones, prefer to self-host Postgres and want to install our Postgres extension directly. The extension is installed in their primary Postgres, and the CREATE INDEX commands must be issued on the primary; however, they may route reads only to a subset of the read replicas in their cluster.
In this topology, all writes could be directed to the primary, all OLTP read queries could be routed to a pool of read replicas, and all search/OLAP queries could be directed to another subset of replicas.
Both are completely reasonable approaches and depend on the workload. Hope this helps :)
Which of these two is the higher order bit?
* ParadeDB speaks postgres protocol
* These setups don't have a complex ETL pipeline
If you have a ETL pipeline specialized for PG logical replication (as opposed to generic JVM based Debizium/Kafka setups), you get some fraction of the same benefits. I'm curious about Conduit and its postgres plugin.
That leaves: ParadeDB uses vanilla postgres + rust extension. This is a technology detail. I was looking for an articulation of the customer benefit because of this technologically appealing architecture.
The value prop for customers vs Elasticsearch are:
- ACID w/ JOINs
- Real-time indexing under UPDATE-heavy workloads. Instacart wrote about this, they had to move away from Elasticsearch during COVID because of this problem: https://tech.instacart.com/how-instacart-built-a-modern-sear...
Beyond these two benefits, then the added benefits are:
- Infrastructure simplification (no need for ETL)
- Lower costs
Speaking the wire protocol is nice, but it's not worth much.
they both sound like postgres to me, just with different extensions
Since we both worked there: I can think of a few places at Segment where we'd have added more reporting/analytics/search if it weren't such a pain to set up a OLAP copy of our control plane databases. Remember how much engineering effort we spent on teams that did nothing but control plane database stuff?
Data plane is a different story, but not everything is 1m+ RPS.
It's not going to happen anytime soon, because you simply cannot cheat physics.
A system that supports OLAP/ad-hoc queries is going to need a ton of IOPs & probably also CPU capacity to do your data transformations. If you want this to also scale beyond the capacity limits of a single node, then you're going to run into distributed joins and network becomes a huge factor.
Now, to support OLTP at the same time, your big, distributed system needs to support ACID, be highly fault-tolerant, etc.
All you end up with is a system that has to be scaled in every dimension. It needs to support the maximum possible workloads you can throw at it, or else a random, expensive reporting query is going to DOS your system and your primary customer-facing system will be unusable at the same time. It is sort of possible, but it's going to cost A LOT of money. You have to have tons and tons of "spare" capacity.
Which brings us to the core of engineering -- anyone can build a system that burns dump trucks full of venture capital dollars to create the one-system-to-rule-them-all. But businesses that want to succeed need to optimize their costs so their storage systems don't break the bank. This is why the current status-quo of specialized systems that do one task well isn't going to change. The current technology paradigm cannot be optimized for every task simultaneously. We have to make tradeoffs.
I don't know. For me, I need
* a primary transactional DB that I can write fast, with ACID guarantees and a read-after-write guarantee, and allows failover
* one (or more) secondaries that are optimized for analytics and search. This should also tell me how caught up the system is, with the primary.
If they all can talk the same language (SQL) and can replicate from primary with no additional tools/technology (postgres replcation for example), I will take it any day.
It is about operational simplicity and not needing intimately to know multiple technologies. Granted, even if this is "just" postgresql, it really is not and all customizations will have their own tuning and whatnot, but the context is all still postgresql.
Yes, this will not magically solve the CAP theorem, but for most cases we don't need to care too much
Yeah, in general, I think a lot of businesses would love to skip ETL pipelines if possible / consolidate data. Postgres is a very much a neutral database to extend upon, maybe a wild analogy but it's the canola oil of databases
Total tangent, but I think "Canola is a neutral oil" is a lie. It's got the most distinctive (and in my opinion, bad) flavor of the common cooking oils.
What would you say is the most neutral oil then?
Sunflower oil? It seems to very reliably taste like nothing.
Personally I have Canola and Sunflower oil tied. Vegetable Oil I guess deserves a mention here too.
If canola oil tastes like something, it's really disgusting IMO. I kinda hate the stuff even though my dad made good money growing it. OTOH, the very sweet smell of the plant's flowers is pleasant enough if pretty basic and the honey is similar.
Shout out to grapeseed oil
Once upon a time, I was using postgres for OLTP and OLAP purposes combined with in-database transforms using TimescaleDB. I had a schema for optimized ingestion and then several aggregate views which produced a bunch of purpose-specific "materialized" tables for efficient analysis based on the ingestion tables.
Timescale had a nice way of abstracting away the cost of updating these views without putting too much load on ingestion (processing multiple TBs of data a time in a single instance with about 500Gb of data churn daily).
One db that could be interesting here is CrateDB. It's a Lucene based DB that supports the postgres wire protocol. So you can run SQL queries against it.
I've tried figuring out if it supports acting as a pg read-replica, which sounds to me like the ideal set up - but it doesn't seem to be supported.
I have no affiliation to them, just met the team at an event and thought it sounded cool.
One of the ParadeDB maintainers here -- Being PostgreSQL wire protocol compatible is very different from being built inside Postgres on top of the Postgres pages, which is what ParadeDB does. You still need the "T" in ETL, e.g. transforming data from your source into the format of the sink (in your example CrateDB). This is where ETL costs and brittleness come into play.
You can read more about it here: https://www.paradedb.com/blog/block_storage_part_one
Sounds very interesting! Unfortunately AGPL license makes it hard to bring into projects.
How so? Many popular projects are AGPL. MinIO, Grafana, etc.
We wrote about this here: https://www.paradedb.com/blog/agpl
So, I'm not versed enough in legal matters to be certain about this, so I tend to fallback to caution, but (A) customers I've worked with in the past seem to be wary of such copyleft licenses and (B) the contagious nature of such license would make me think twice about using it in a project of my own as well.
It would be nice to have such notion challenged but I'm not sure what would change my mind.
I would expect that most commercial companies that use Grafana would obtain a commercial license?
For JOINs? Absolutely! Who wants to hand-code queries at the executor level?! It's expensive!
You need a query language.
You don't necessarily need ACID, and you don't necessarily need a bunch of things that SQL RDBMSes give you, but you definitely need a QL, and it has to support a lot of what SQL supports, especially JOINs and GROUP BY w/ aggregations.
NoSQLs tend to evolve into having a QL layered on top. Just start with that if you really want to build a NoSQL.
To be clear here, I'm not arguing that OpenSearch/ElasticSearch is an adequate substitute for Postgres. They're different databases, each with different strengths and weaknesses. If you need JOINs and ACID compliance, you should use Postgres. And if you need distributed search, you should use OpenSearch/ElasticSearch.
Unless they're building for single-host scale, you're not going to get JOINs for free. Lucene (the engine upon which ES/OS is based) already has JOIN capability. But it's not used in ES/OS because the performance of JOINs is absolutely abysmal in distributed databases.
I'm arguing that sometimes you don't need ACID, or rather, sometimes you accept that ACID is too painful so you accept not having ACID, but no one ever really doesn't want a QL -- they only think that they don't want a QL until they learn better.
I.e., NoACID does not imply NoQueryLanguage, and you can always have a QL, so you should always get a QL, and you should always use a QL.
> Unless they're building for single-host scale, you're not going to get JOINs for free.
If by 'free' you mean not having to code them, then that's wrong. You can always have or implement a QL.
If by 'free' you mean 'performant', then yes, you might have to denormalize your data so that JOINs vanish, though at the cost of write amplification. But so what, that's true whether you use a QL or not -- it's true in SQL RDBMSes too.
Our customers typically deploy ParadeDB in a primary-replicas topology, with one primary Postgres node and 2 or more read replicas, depending on read volume. Queries are executed on a single node today, yes.
We have plans to eventually support distributed queries.
Obligatory whine that the term NoSQL got co-opted to mean "no relational". There's tons of space for a better query language for querying relation databases.
It's funny; as someone who is exactly pg_search's market, I actually often want the opposite: ACID, MVCC transactions, automatic table and index management... but no query language.
At the data scale + level of complexity our OLAP queries operate at, we very often run into situations where Postgres's very best plan [with a well-considered schema, with great indexes and statistics, and after tons of tuning and coaxing], still does something literally interminable — not for any semantic reason to do with the query plan, but rather due to how Postgres's architecture executes the query plan[1].
The last such job, I thought would be simple enough to run in a few hours... I let it run for six days[2], and then gave up and killed it. Whereas, when we encoded the same "query plan" as a series of bulk-primitive ETL steps by:
1. dumping the raw source data from PG to CSV with a `COPY`,
2. whipping out simple POSIX CLI tools like sort/uniq/grep/awk (plus a few hand-rolled streaming aggregation scripts) to transform/reduce/normalize the source data into the shape we want it in,
3. and then loading the resulting CSVs back into PG with another `COPY`,
...then the runtime of the whole operation was reduced to just a few hours, with the individual steps completing in ~30 minutes each. (And that's despite the overhead of parsing and/or emitting non-string fields from/to CSV with almost every intermediate step!)
Honestly, if Postgres would just let us program it the way one programs e.g. Redis through Lua, or ETS tables in Erlang — where the tables and indices are ADTs with low-level public APIs, and you set up your own "query plan" as a set of streaming-channel actors making calls to these APIs — then we would be a lot happier. But even in PL/pgSQL (which we do use, here and there), the only APIs are high-level ones.
• Sure, you can get a cursor on a query; but you can't e.g. get an LMDB-like B-tree cursor on a target B-tree index, and ask it to jump [i.e. re-nav down from root] or walk [i.e. nav up from current pos to nearest common ancestor then back down] to "the first row-tuple greater-than-or-equal to [key]".
• You can't write your own efficient implementation of TABLESAMPLE semantics to set up your own Bigtable-esque balanced cluster-order-partitioned parallel seq scan.
• You can't collect pointers to row-tuples, partially materialize them, filter them by some criterion on the read (but perhaps not parsed!) columns, and then more-fully materialize those same row-tuples "directly" from the references to them you still hold.
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[1] One example of what I mean by "execution": did you know that Postgres doesn't use any form of concurrency for query plans — not even the most basic libuv-like "This Merge Append node's child-node A is in a blocking-wait on IO; that blocking-wait should yield, so that the Merge Append node's child-node B can instead send row-tuple batches for a while" kind of concurrency?
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[2] If you're wondering, the query that ran for six days was literally just this (anonymized):
`source1` and `source2` are ~150GB tables. (Or at least, they're 150GB when dumped to CSV.) Two integer keys (a,b), and a bigint value. With a b-tree index on `(a,b) INCLUDE (value)`, with correct statistics.And its EXPLAIN query plan looked like this (with `SET enable_hashagg = OFF;`) — nominally pretty good:
Each one of the operations here is "obvious." It's what you'd think you'd want! You'd think this would finish quickly. And yet.(And no, the machine it ran on was not resource-bottlenecked. It had 1TB of RAM with no contention from other jobs, and this PG session was allowed to use much of it as work memory. But even if it was spilling to disk at every step... that should have been fine. The CSV equivalent of this inherently "spills to disk", for everything except the nursery levels of sort(1)'s merge-sort. And it does fine.)
> At the data scale + level of complexity our OLAP queries operate at, we very often run into situations where Postgres's very best plan [with a well-considered schema, with great indexes and statistics, and after tons of tuning and coaxing], still does something literally interminable — not for any semantic reason to do with the query plan, but rather due to how Postgres's architecture executes the query plan[1].
Well, ok, this is a problem, and I have run into it myself. That's not a reason for not wanting a QL. It's a reason for wanting a way to improve the query planning. Query hints in the QL are a bad idea for several reasons. What I would like instead is out-of-band query hints that I can provide along with my query (though obviously only when using APIs rather than `psql`; for `psql` one would have to provide the hints via some \hints commnad) where I would address each table source using names/aliases for the table source / join, and names for subqueries, and so really something like a path through the query and subqueries like `.<sub_query_alias0>.<sub_query_alias1>.<..>.<sub_query_aliasN>.<table_source_alias>` and where the hint would indicate things like what sub-query plan type to use and what index to use.
I mean, in my case, I don't think what I want could be implemented via query hints. The types of things I would want to communicate to the server, are pragmas entirely divorced from the semantics of SQL: pragmas that only make sense if you can force the query's plan to take a specific shape to begin with, because you're trying to tune specific knobs on specific plan nodes, so if those plan nodes aren't part of the final query, then your tuning is meaningless.
And if you're pinning the query plan to a specific shape, then there's really no point in sending SQL + hints; you may as well just expose a lower-level "query-execution-engine abstract-machine bytecode" that the user can submit, to be translated in a very low-level — but contractual! — way into a query plan. Or, one step further, into the thing a query plan does, skipping the plan-node-graph abstraction entirely in favor of arbitrarily calling the same primitives the plan nodes call [in a sandboxed way, because such bytecode should be low-level enough that it can encode invalid operation sequences that will crash the PG connection backend — and this is fine, the user signed up for that; they just want to be assured that such a crash won't affect data integrity outside the current transaction.]
Such a bytecode wouldn't have to be used as the literal compiled internal representation of SQL within the server, mind you. (It'd be ideal if it was, but it doesn't need to be.) Just like e.g. the published and versioned JVM bytecode spec isn't 1:1 with the bytecode ISA the JVM actually uses as its in-memory representation for interpretation — there's module-load-time translation/compilation from the stable public format, to the current internal format.
But your mental model of your query is still in a language, even if it's only natural language. Why wouldn't you write a QL and compiler for it that outputs a query plan AST/bytecode/whatever to your liking? The PG SQL compiler and query planner just isn't to your liking, but you really want to be writing queries by hand? I guess what you're saying is you want something like LinkQ that lets you build complex plans/ASTs w/o the complexity of NoSQL hand-coded queries.
Oh, and BTW, PG is getting async I/O in the next release. It's not the same as concurrency, but if your workloads are I/O-bound (and likely they are) then it's as good as concurrency.
Interestingly enough, it looks like the team was just hacking on an open source extension and organically attracted some customers, which snowballed into raising capital. So definitely seems like there’s a market.
Running elasticsearch is a miserable experience. If you can run one tool that you already have with slightly more effort, amazing. And you never need to think about rebuilding indexes or tuning the garbage collector or planning an ES major version migration.
Because it's a whole another architectural component for data that is already there, in a tool that is made just for that lacking just of `SELECT full_text_search('kitty pictures');`
There can be multiple reasons, one that I can think of right away would be to keep the stack as simple as possible until you can. Realistically speaking most of the companies do not operate at the scale where they would need the specialized tools.
> Why not just use OpenSearch or ElasticSearch?
There is a cost associated with adopting and integrating another tool like ElasticSearch. For some orgs, the ROI might not be there. And if their existing database provide some additional capabilities in this space, that might be preferrable.
> This is another one of those “when you have a hammer, everything looks like your thumb” stories.
Are you referring to people who think that every reporting problem must be solved by a dedicated OLAP database?
Why not the Cassandra based elastics if you need ingest?
diagramas made how?
Guessing Figma.
Yes, Figma!
[flagged]
The tradeoffs you cite aren’t about the CAP theorem. It’s more about RUM conjecture: http://daslab.seas.harvard.edu/rum-conjecture/
Since this is about aborted writes during VACUUM then this is likely also relevant: https://www.cs.cmu.edu/~pavlo/blog/2023/04/the-part-of-postg...
Hard to blame the CAP theorem since this is a problem across an interface layer. If the DB knew about the data it could manage the LSM tree without issue.