In the spirit of being an event that runs on time, we'll get started here with the next session. This is Joe Conway. He's been a committer on the Postgres project for almost 20 years. He's going to talk about both the Postgres community and some upcoming features in the next releases. So, Joe, take it away. Thank you. Can you guys hear me in the back all right? Good. So, like you said, my name is Joe Conway. I'm the VP of Engineering at Crunchy Data and a long-term community member, committer. I'm on the infrastructure team for the project, and I'm also on the board of directors for the U.S. Postgres Association. So, what I'm going to do is I'm going to cover a little bit of history about the project and then quite a bit about how the community works. And then, toward the end, I'm going to cover at a real high level stuff that hit in Postgres 11, kind of more like a bullet list of features that maybe will catch your attention and I'll zero in on a few of them. But if you want to see real details about those things, one of the other community members and a core team member, Magnus Hagander, is giving an in-depth talk about Postgres 11 certain select features in Postgres 11 tomorrow in the dev room. So you should go check that out, too. And I will touch on some things that have been committed toward Postgres 12 and some other things that are being still worked on and might or might not make it into 12. Now my laptop is freezing. It's happened to the last guy. Yeah, it's working. All right, so, maybe, yeah, I guess I'll just have to use the switch on the computer. So the Postgres project started at the University of California, Berkeley. This is just a couple of pictures from the campus. But even before that, there was a project at Berkeley called Ingress. How many people here have heard of Ingress? Actually, quite a fair amount. It's not very popular these days. But basically, in 74, there was a professor named Stonebreaker at University of Berkeley. And he started a project which was really kind of the first modern relational database. And they called it Ingress and it stood for Interactive Graphics Retrieval System, which interestingly doesn't sound much like a database. But I think it was because his grant was actually based on something else and he decided to build a database with it or something like that. And that went on to spawn some well-known commercial databases. This has, you know, its roots can be seen in Sybase and MSSQL, something called nonstop SQL and some other database technologies that are out there. And in 1980, Ingress actually became a commercial company and then changed names several times. And it's actually still around today as a company called Actian, and they open sourced Ingress back in 2006. So you can still use Ingress, you can get the source code for it, you can get support from Actian. And that's not going to work. Okay. So in 1986, the same professor Stonebreaker started the Postgres project and the idea was it was Post-Ingress. So that's literally where the name comes from, Postgres. And it was supposed to be the next generation. It was an object-relational database. At this point in time, there wasn't an actual SQL standard. So they invented a language called Quell that you would use to query this database. And that original source code actually spawned other commercial databases, including Illustra and Informix, which I think at this point are pretty much dead. Illustra, Spawn, Informix, Informix got bought by IBM, and I think it kind of died a quiet death a few years ago, although I still hear about people using Informix from time to time when they're converting to Postgres. In 1994 and 95, there were two students, two graduate students, I think at Berkeley named Andrew Yu and Jolly Chen, and they're the ones who basically took the SQL, which was now an emerging standard, and strapped it on to what was Postgres at the time. And at the same time, they also critically modified the license and made it more liberal because that set us up for 1996, which is when the Postgres project actually was formed. The community started with commits into CVS from the original code that came from Berkeley in 1996. So you can see, as an open source project, Postgres has been around for over 20 years now, and as a source code, the project has been around for over 30 years now. So it's been around a long time. It's really pretty much as long as all of the other commercial databases that are out there. Also, I make note that there is a wiki page which shows all the different derived products from Postgres. So one of the things, because the license is so liberal, it's basically described as either a BSD form or a MIT form license, which the way the community generally sums it up is you can do anything you want with the source code, just don't sue us. So because of that, it's been forked and made into many different things over the years. And you can see based on this list, there's quite a lot that has been spawned by Postgres as a project. Many of these commercial entities that have made good money from it. So just for fun, I have a couple of pictures here. This is Michael Stonebreaker, the originator, Jolly Chen and Andrew Yu, the two guys who turned Postgres into SQL database. And this was the original core team. A few of these people are still around. Tom Lane and Bruce Momgen are still members of the core team. These other folks have come and gone for the most part, worked on various parts of the system. Vadim did the major work on MVCC, which is the transaction control that Postgres uses. Thomas Lockhart did a lot of the work on the early work on daytime data types and so on. And our first conference, basically, as a community was in 2006 for the 10-year anniversary of the open sourcing of the project. So this is a picture of all the people out in front of University in Toronto, I think it was. I'm in there somewhere. There's a bunch of people that are walking around the halls here. There's a Postgres table up kind of behind us over here. And a bunch of the people that were in this picture 13 years ago are still active members of the community and walking around here today. So this diagram I got actually last year at the Postgres dev room. There was a talk by, and I know I'm going to butcher her name, Latitia Avaro. And she put this slide up because she was talking about the Postgres community and it struck me as like a perfect slide for describing how the Postgres community works. And I had been thinking about the structure of the community for many years, but I had never actually put it onto a slide. And when I saw this, it was like, I've got to use that. So she gave me permission to use it, and I've used it a few times now. But basically, in order to understand the community, this kind of, this ring form over here is important. There's a core team that's five people. They provide the governance for the project. But closely associated with them and also included in much of the governance are the people who have commit access. There's 28 total people with commit access, including a few of the people who are on the core team. So the entire project is basically managed by that core group of people. But for every release, there are a lot of people that are not part of that core group that submit patches, review patches, they do translations, they do testing, they run what we call build farm animals, which is how testing gets done in an automated way across different platforms. So all of these people are very important to the community as well. These are the people that are doing reviews. People are developing patches. The people are doing translations. Postgres is translated and the actual error message and so on are translated into quite a few languages. So that's all due to that larger group. And that group keeps growing, release after release. I think someone did a count on the release notes for a major release. We try and name everyone who did something that was kind of directly touched, something that got committed. And maybe someone can correct me if I'm wrong, but I believe the list for Postgres 11 was something just over 300 people. So there's quite a few people that get involved in the production of Postgres as a database. Now also you notice that we have over here user groups and associations and advocacy. And as I mentioned, I'm on the board of directors for PGUS. There's a PG Europe organization. There's an organization in China, I think in India. Probably, I'm sure there's one in Russia as well. There's a bunch of them basically spread out. And mainly what they're there for is to advocate the use of Postgres to teach people how to use Postgres. And so those groups run most of the Postgres specific conferences where you can go learn in depth from experts. And that same group, Postgres Europe, is running a Rana PG Day here in Brussels yesterday at the Marriott, and they're running the dev room tomorrow. So tomorrow the dev room will have, I don't know exactly how many, six or so talks that will be dedicated to Postgres. And then further, you've got the users, everyone who's using Postgres, maybe participating in the mailing list, maybe not. Those folks are all part of the community too. We've got a very active and robust community. We get lots of feedback from the users that gets incorporated into fixing things that are broken or new features that come out. And so the project is very much driven by its community. Sometimes it works, sometimes it doesn't. So this picture is kind of to show you what the Postgres development cycle, a typical development cycle looks like. So this is probably, I don't know if this is readable in the back of the room here. But basically at some point here in the kind of August time frame last year, excuse me, in 2017, we branched a stable branch for Postgres 10. When that happens, basically we open up development on whatever's going to be next. So in this case it would have been Postgres 11. So all the new development starts. As part of the development process, we have a number of these commit fests. CF here, CF 1, 2, 3, and 4 commit fests. I'm going to go into that in more detail, but basically commit fests are where the other people who have submitted stuff patches, where we focus as a community on reviewing those packages, getting them cleaned up to the point where they can be applied and getting them committed. So we focus on what everyone else has provided during those time frames as opposed to what the committers themselves maybe want to work on. And more and more in recent years, even the committers have tried to put their patches into this commit fest process, which I'll describe later. There's also, we do that one month at a time and we skip a month in between. So this is typically here for the Postgres 11, this was September, November, January, and March. This year we actually added a fifth one. We branched a little early and for Postgres 12 we actually have five commit fests. We started a bit early. But this is pretty typical over the years. You also notice I have this developer meeting at Fosdown, which is something we did this Thursday before we had the PG Day. And then at the end of this fourth commit fest, that's when we call feature freeze for that release. So the end of March or basically that usually spills over slightly into April. But about the end of the first week in April we called last year, we called feature freeze on Postgres 11. And then shortly after that we declared a beta. Do beta testing in here. There was a very short release candidate cycle and then we released 11 here toward the end of October last year. So our goal is normally to get out a release once a year and we are generally shooting for... Testing? Yeah. So we try and get a new major release out once a year. Still not? Yeah. Testing? Yeah. Just a little. Okay. In the September timeframe, the project is very conservative. So if it's not ready to go, we're not going to ship it. And again, I'll talk a little bit more about the process of how we get that done. But there have been years where the major release slipped into, as it did last year into October or even kind of later. And we've been making consolidated effort recently to kind of pull it back to where it would happen closer to September. So just a note on the versioning. How many people here are familiar with how Postgres does the versioning and the change recently? Not too many. So this could be something that would be confusing. Up until version 9.6, Postgres used a three segment release version. So this 9.6.8, 9.6 in this case is actually the major version portion. And the 0.8 indicates a bug fix release. So that's important to understand because one of the other things about the Postgres community is new features only go in the major releases. And the bug fix releases are truly only bug fix releases. And so therefore you really should, as soon as you can, when a bug fix release comes out, you should try and go apply it to your production systems because it fixes known bugs and it shouldn't create any regressions. Over the 20 years I've been involved with the project, I can remember a very small, less than one handful of times where there was some kind of a regression that was caused by a minor release. There are definitely a lot of things that get fixed in every minor release. So highly recommend that you upgrade those minor releases. The major releases take more planning and more effort so you can space them out a bit, but not the minor releases. How's that? That's good. So anyway, starting with Postgres 10, we switched basically to a two segment. So now 10 is a major release, 11 is a major release, 12 will be a major release. And when you see 10.3, that's actually a bug fix release. Probably the other thing that's worth noting here is that the minor releases, we usually target those about once a quarter. So if there is some kind of an emergency, some kind of a data loss bug that's discovered or a security issue, we do sometimes do interim minor releases. But they tend to come out, if they're coming out as scheduled, they come out once a quarter. And there is a dates listed on the Postgres website for that. So now I want to talk a little bit about the commit fests. We have this app that was put together by one of the core team member, Magnus, wrote this and maintains it. And basically this is a place where if you've got a patch that you want to submit to the Postgres project, you would go register it on the open commit fest. So I don't know if you can see this. So you can see here the 2019.03 commit fest, which is the March commit fest, is currently open. That means if you had a patch you wanted to submit to Postgres, you would go register it in there. Let's see if my internet connection works. And you can see when you go in there basically each patch gets a line item. It has a status. I'm going to talk about some of these things. And this app is kind of designed to track the mailing list discussion that will happen on the hackers mailing list with respect to that particular patch, as well as keeping track of the last version of the patch and who's going to review it and who's planning to commit it. So one entry in that commit fest can be either one patch or it can be a related set of patches that build on top of each other. And they implement some goal. It might be a bug fix. It might be some refactoring. It might be documentation improvements or it might be new features. And there are going to be one or more authors and one or more reviewers that are listed. And as I just said, it can be really a whole variety of things that are included in those. The goal of the commit fest is that patches don't get lost. Before we started doing the commit fest, we were getting an increasing volume of patches being submitted by people outside of that core group of core team members and committers and things would start getting lost through the cracks. And so the goal of this is to make sure that everyone who does want to participate in the community gets a fair chance at actually having their patch reviewed. The other thing is it makes it easier for us to find things that need our attention. And the goal here is also to encourage, if you're going to submit five patches that you want to get into Postgres 12, let's say, the hope is that you'll go review five other people's patches because that's the only way this process is going to scale. So in a nutshell, that's what we're trying to do with that. The entry states, as you can see here, needs review. Basically, that's a patch that needs someone to look at it, not necessarily a committer. And again, in fact, the idea here is that we can have people who are not committers doing the initial triage on these things, helping them get improved to the point where they're ready for a committer. And then once they're marked ready for a committer, because that's a scarce resource, a committer will go spend time on that. If the reviewer says something needs to be fixed or changed, they might set it back to waiting for author because now they're basically asking the author to make some change. Once it gets committed, obviously it gets marked as committed. We do have patches that get rejected. For a variety of reasons, sometime the author submits a patch and then kind of disappears, and we have feedback and they don't address it. Sometimes we end up marking them as returned with feedback because no one else wants to pick up a patch and finish it. And we can also have them get moved to the next commit fest. The basic process is that the day before the commit fest starts is the deadline for getting a patch in for that open commit fest. And that's defined as anywhere on earth. So at the end of February, anywhere on earth, you can still submit a patch and get it into the commit fest. And when the commit fest starts, all patches should either be in a needs review or ready for a committer state. We assign someone from the community to be a commit fest manager, that person is basically the one who's going to bug everybody and try and keep things moving, keep the statuses updated and whatnot. The reviewers will review, provide feedback, the authors will make adjustments, and then when it's finally ready, it'll get committed. If we're waiting on an author for too long, we will mark it as returned with feedback. And at the end of a commit fest in particular, if the patch still needs review, we'll automatically move it to the next commit fest. But if it's waiting on author at the end of the commit fest, it'll automatically get marked return with feedback. And so the author now needs to take some action to address whatever the feedback was in order to get it into another commit fest if they care to do that. The other thing is the last commit fest, so that commit fest in March that I showed you, that's the last one of a development cycle for a major version just before feature freeze. We do have an extra rule that basically something that's large and invasive shouldn't show up for the first time in that commit fest. We're also much more aggressive at taking large patches that look like they aren't going to make it in before the feature freeze and marking them return with feedback or whatever and moving them to another commit fest for the next major release. Okay, so now I've got some metrics here for you. This is the 9, 6, 10, and 11 cycles. You can see here 9, 6, we also had 5 commit fest. In 12, we're going to have 5 commit fest. You can see the level of activity here has been going up pretty steadily. The stark part of the bar is the committed. So you can see we're getting more stuff committed. Move to the next commit fest, stuff that just didn't get finished. And that's also increasing in size. The rejected is pretty consistent throughout. It's small, but it's there. And return with feedback has actually increased a bit because we've had more stuff submitted. Some statistics out of the repository. This is going all the way back to Postgres 7.2, which was, I think, back in around 2001. So the number of files changed from major release to major release is increasing but not by a huge amount. Insertions and deletions of lines of code is actually, with the exception of a few spikes, has been pretty consistent. And the diff size, again, Postgres 10 seemed to have an abnormally large diff size for some reason, maybe because all the partitioning code that went in. And that's been kind of slowly increasing from release to release. Okay, so now I'm going to cover some of the things that got committed in 11. I just wanted to start first with, again, some numbers. It's kind of funny. Someone was showing me last night, I guess someone did a blog about the talks at Fozdom and there was a satirical blog and basically they were roasting each of the talks. And so what they said about mine was that I was going to stand up here and read you the commit logs for an hour. But the funny thing is, I actually went through the commit logs for Postgres 10 and Postgres 11 putting this talk together.  But I thought it's good to look at kind of the variety and the absolute numbers of how many things get done in each release. So you can see here, bug fixes, 84. A bunch of client work. Does anyone here know what ECPG is? Steven, you don't count. I didn't even know you were back there. You can see even down to code comments, documentation changes, lots of performance related stuff and I'll get to this in a little bit, in a little bit more depth. You can see a lot of things having to do with parallel query and partitioning. And trying to categorize these is kind of hard because there's a lot of crossover. Some of the things that are kind of partitioning related are also kind of performance related as well and vice versa. Procedural languages, refactoring, 38. So these are just patches that did nothing but move code around to make it easier to understand and maintain. Postgres code is by most accounts some of the cleaner source code out there for open source projects. I don't know if everyone will agree with that. And certainly in the community we sometimes complain about our own source code. I think in general it's pretty good. A couple of security fixes, a bunch of things related to the server features. New SQL commands, sysadmin. All right, so here's a list now of things that did get committed in 11. And again, if you want in-depth information about these, go see Magnus' talk tomorrow. I think, do I see him back there too? Go see Magnus' talk in the dev room. He'll go into more details about a subset of these, I'm sure, the more important ones. But some of the ones that are interesting, this is the list of things having to do with partitioning that got into Postgres 11. So you can see it's quite a bit. Partitioning kind of became a full, fully supported feature. I mean, we've had it for a number of years, but in 10 it became part of the grammar. But it was the first release with the partitioning, and so there were a lot of things left to be done. And you can see a lot of that got done in Postgres 11. Things like hash partitioning, partition-wise join, the ability to create indexes that will be replicated on all the partitions, lots of improvements for faster use of partition tables. So in general, if you have a need for partition tables with Postgres, you definitely want to be on 11, and probably 12 when it comes out. Again, parallelization, parallelization first hit Postgres in 9.6, but it has steadily improved with each major release. So some important things here to note, you can do now hash joins in parallel, B tree index builds in parallel. Queries with a pen plan nodes, I mean, that's kind of not easy to understand, but things like union queries that would apply to, it also applies to things like partition tables. Limit clause gets passed down to the workers, and now if you use limit clauses and you have a parallelized query, it can be a lot faster because of that. Lots more in the area of performance. More of this pushdown of limits. Automatic pre-worm. How many people here are aware of PG pre-worm? Yeah, again, you guys don't count. So that's an interesting extension to Postgres. You can use it to basically, when you first start up your server and your caches get warmed up by your actual queries, well, you can use this to basically save that warmed state and so if you need to restart your server, you can start it back up and get it back to a warmed state much more quickly. And that's been improved in Postgres 11. Let's see what else is notable here. Index only, bitmap scans, covering indexes. So covering indexes, this is where you build an index on some columns, but you can include other columns in there that you basically don't want to be part of the index but you want them to be in the index so that we can do an index only scan instead of having to look at the heat. So that's another significant performance boost for certain types of queries. Just in time compilation, I don't want to forget that one. So that's a big one. Postgres 11 is the first release with it. There's similar to what I was just saying with parallel query and with partitioning. The first release of JIT is going, it's great, but it's limited in scope so I would expect to see over the next several major releases continuing refinements and improvements to the way that works. Logical replication now is truncation support, constantly doing improvements to test coverage, some authentication improvements. There's a lot of stuff in this miscellaneous list. You can now build if you're wanting to build on Windows. I don't personally recommend it, but if you want to do it, do it with Visual Studio 2017 now. Fewer super user checks. This is an important one. Steven back there was involved in that. Basically, a lot of things that we used to require the super user to do are now grantable to normal users so that you can use the principle of least privilege. You can basically just grant privileges to do specific things to specific users instead of having to give them super user to do that. SQL procedures with transaction control. So this is actual stored procedures. For years, Postgres, people talk about Postgres functions like they were stored procedures, but they're really not the same thing. Stored procedures can run with their own transaction control. So now you can basically build procedures in Postgres that act more like maybe your scripts were doing before. And it also puts us on a par with other database systems that have similar things. See if there's anything else I wanted to note. You can allow an external command for getting a passphrase for SSL key files, so you can have your SSL key files in password protected, basically, but not have the server prompt you to type a password at startup. And again, some more Windows support for huge pages. PG Bench is the tool that comes with Postgres for doing kind of basic benchmarking. There's been lots of improvements made to that over time. It's like a Swiss Army knife. You can do all kinds of different things now. P-SQL is the command line client for Postgres. The Postgres users, the hardcore ones, typically like the command line versus some GUI tool. And consequently, this tool gets a lot of love. So it's a very good command line tool. It makes very good use of ReadLine for command line history and editing and all kinds of other stuff. So more improvements to that. But the big one to note here, I guess I don't quite get why this was so important, but to a lot of people, this was huge. You can now just type quit or exit and exit. Okay, so now I'm just going to touch a little bit on things that have either been committed since Postgres 11 branched, so things that would get into 12 or are currently being worked on. So in terms of things that have actually, and this is a little bit old, we just are finishing up another one of these commit-fests. This was done at the end of the commit-fest in November. Things were moving too fast. I didn't really want to complicate the slides by trying to get it right up to date. So it's missing like the last months were the commits. But in any case, the Git log from when we branched Postgres 11 to this point at the end of November was almost 15,000 lines. So a lot of stuff got committed. Now a lot of that is bug fixes that are also getting backpatched to the older stable branches. So when I go through it, I look for things that were just net new for Postgres 12. So again, a bunch of new performance-related things that have been included in, that will be included in Postgres 12. Some of these are pretty arcane, but allow multi-inserts during copying to a partition table, hand-code string to integer conversion for performance. I mean, these are the kinds of things that happen in Postgres all the time. I'm not even sure if I have it on the other slides, but there's a guy right now, a man, Andrew Gehrth, who's working on a whole new way to print out floating point values because of the speed improvements that'll provide. There's been a logging improvements, an application name to the connection authorized message. Improve auto-vacuum. How many people here are familiar with auto-vacuum? How many people like auto-vacuum? They should actually like it. Of course, there are some improvements coming that maybe will make that potentially obsolete. More improvements to P-SQL. Again, miscellaneous replication slots dropped in single-user mode. So again, the procedural language being able to create procedures in Postgres was new in Postgres 11. And again, similar to those other features, it will get better with each release. So in 12, you'll be able to use polymorphic argument types with procedures, which means you can create a procedure that basically will take different types of arguments, integers, floats, or text, and do the right thing at runtime as long as you design them to do the right thing. One to note here, if you do development in particular, we started to require C99 for Postgres, so we're now requiring a 20-year-old compiler. And in fact, we don't even really, in the source code, we don't support all aspects of C99, but that is required now for the compiler, which means if you're going to compile on Windows, you have to have MSVC 2013 or up. Extensions can now install their own headers. Oh, another important one. Recovery.conf has been integrated into Postgresql.conf. So if you're used to setting up high availability or replicas of any kind with Postgres, you're used to manipulating the file called Recovery.conf, that now actually goes away. There's a bunch of new functions. Some of these are related to the effort to allow non-super users to do more things, principle of least privilege. There's been settings added for control of SSLTLS protocol version. There's been a few things removed. I kind of doubt anyone's using these, but there was a time travel extension that is gone. These data types, abs time, rel time, interval are all gone now. They've been deprecated forever, so hopefully you're not using them. Postgres originally supported OIDs on every table, but that's been deprecated for years and it hasn't been the default for years, and that's now been pulled out. In terms of ongoing discussions, things that have not yet been committed, the biggest one is this pluggable storage. I was just talking to Andres, who's been working on that, and it sounds like there's a reasonable chance that pluggable storage itself will get committed for 12. Zheap is one of the first new types of storage that would make use of this pluggable storage layer, and talking to some people about that, it sounds like Zheap will probably not make it into 12, but it's an ongoing discussion. Zheap is the thing I was alluding to earlier, which will, I think, if I understand it correctly, eliminate the need for vacuum because of the way it works. It works more like Oracle with undo logs, which should give you shivers. But there's been an ongoing discussion for a while about a feature called Merge. It's an often asked for feature. We've got similar functionality in other features in Postgres already. This one actually got committed for 11 and then got pulled out because there were problems with it, and then I think it got committed again and pulled out again, something like that. Just looking at these guys back here. So it sounds to me like Merge is probably not going to make it into 12, but you never know, it could happen in March. There's other features having to do with the JSON standard. There's been work, I doubt this would get into 12, but there's been work going on now for a couple of years, sort of at a low level to go to 64-bit transaction IDs, which would have an impact on something called Rack Transaction Wrap-Around. There were talks yesterday at the PG Day and at the Marriott on database encryption. I don't know whether that is likely to get into 12, but I think actually the author of that yesterday said they hoped maybe it would get into 13 or 14. So that's, again, ongoing work, probably not going to make it into 12. Logical replication conflict handling, that would be one of the things that we need in order to make logical replication start to look more like a form of multi-master. Synchronous logical replication. Alternatives to open SSL. I know this is one that I'm interested in. I know Stephen back there is interested in. There's some discussions about, and I think maybe that actually is a chance to get in to 12. Global indexes. Postgres with partition tables, you can create an index that will automatically get created on the partitions, but that's not an actual global index that spans the partitions, and that's something else that people have asked for. There are pluses and minuses to it. There are definitely use cases where I think it makes sense. Autonomous transactions is another thing that gets asked for a lot. I don't know where we are on that, but I don't think that'll make it into 12. So again, we'll be building more on logical replication. We'll be doing more with foreign data wrappers, hopefully. Foreign data wrappers are basically a way to call, get data from an external data source. And the Postgres foreign data wrapper, which lets you connect to other Postgres servers, is actually pretty advanced in the way it works. It does a lot of push-down of things that allow the queries to be optimized in a sensible way. But one of the things that it cannot do right now is it cannot actually go scan a bunch of foreign data tables asynchronously, kind of in parallel. So that's something that I hope will come in the not too distant future. Another favorite of mine is Create Variable. Inlining CTEs is being worked on and discussed on the list right now. That has implications for... CTE is a common table expression, which is basically a with clause. And the way that works right now is it's an optimization barrier. It gets optimized all by itself. That has implications in that that cannot then participate in parallel query. So there are certain types of queries where you want that to actually kind of get rolled into the main body of the query so it can get optimized and parallelized. So that's going to be important for a subset of people. And so that's all I have and I think I have probably about five minutes left for questions. If anyone has a question? Question, anyone? Parallel DML. I use a lot of other PostgreSQL also for business intelligence analytics and stuff is that parallel DML is something that's interesting. Okay, there have been discussions about parallel DML. I don't know... Can you guys in the back help me out on that? No? Basically you have parallel query now and parallel DML, which... Yeah, for great inserts and updates and so on. Yeah, it's definitely been discussed. I don't think it's going to be in 12. Okay. Anyone else? I have a tangential question to everything you were talking about. Is there a good book or a resource that describes all the new stuff that people can read and understand these things if I correct me if I'm mistaken? But I think the last book for performance tuning was written for Postgres 9.0 or something like that. Yeah. Are there any plans to get us any updates? Thank you. I know the author of that book and he's made some noises about maybe doing an update to it. As far as features go, really your best bet is to look at the detailed release notes that come out with a new major release because typically I think we do a pretty good job of providing links into the documentation. So if you kind of read through those release notes, they're not that terribly long and you'll get a pretty good view of what the major features are and the two or three that you're really interested in just drill in to what it says in the docs. Get on the Postgres General mailing list. People, if you post a question to the mailing list, you'll have an answer probably within 10 minutes. So that's what I'd recommend. It's hard for books to keep up with this, especially when we're making new releases once a year. So that would be the best advice I'd have. Any more questions? Thank you.


All right, hello. Thank you for having me. So while we are not Eclipse, or while we're not Linux, just like Chris, I'm part of the Eclipse Foundation actually. But what we do heavily complements the Linux ecosystem and we're looking forward to working with you. So what I'm going to do, just a quick overview, I'm going to give a little bit about us since we're not as well known as some of the bigger companies aren't there yet. I'll define an AI ecosystem and you'll see a lot of relevant themes today that are very similar to other presentations within the speeches today. I'll talk a bit about open source frameworks. So like TensorFlow, PyTorch, for those of you who are familiar with it, if not, I'll give a high level overview of those. Different file formats. So kind of like how Chris mentioned earlier, I think a lot of foundations work together on different things. So there's file formats and specs at all the different foundations that all kind of work together in interop. I'll talk about open accelerators. So one thing kind of missing from the ecosystem today is Kubernetes actually doesn't work with NPJs or customer accelerators very well. It's still a very ill-defined area. So schedulers today are not very open to edge. There's a lot of work to do as kind of illustrated in the previous blueprints. So a lot of it's still kind of in progress. So I'm hoping to maybe present some newer ideas here today. And then finally, another area that's not well defined yet and I think will be relevant to LF AI people is open standards for AI metrics. And so to start, so conduit, we're having a developer of open source software. So my open source life mainly comes from Eclipse deep learning for J, but we also heavily develop standards. We work with other open source communities. I'm also on the chair of Keras. So I'm a Keras chair as well as we work heavily with the TensorFlow community in TensorFlow Java. So that's not a well-known fact, but we're actually, we actually have a pretty decent open source footprint. I'm also the author, I'm also a Riley author for deep learning. And we have a few when we're distributed all around the globe with our team predominantly in Asia. And so to start the definition of new open source AI ecosystem. So basically to start the components necessary to build, evaluate and execute AI algorithms. So AI algorithms, for those of you who don't know, are primarily based on this idea of operations. So operations can be something complex, like something in signal processing, or it can just be two plus two, subtraction, multiplication. You know, there are different hardware, different hardware implement, all these operations. And some, and there are different hardware decays from say, I've just mentioned ARM since they're here today. They have ARM and N, you know, there's other vendors like that with NKL, DNN, QDNN. And basically the, turns out the chip vendors are better than us at writing math for their own chip. And writing math code is a specialized area that only people who generally work in physics or something like that and heavily, or work heavily in CNC plus plus typically do. These are not things that normal software developers will typically touch. You know, there are frameworks for these things, which I'll cover as well. So, you know, that's your TensorFlow, your PyTorch, you know, kind of these Python level interfaces to these low level math routines. The file formats, you know, so there's, you know, we think of protocols. But, you know, we need to store our data somehow. So there's file formats out there, again, at different open source foundations. You know, you have Protobuf, you have Apache Arrow, you have things built into protocols on top of those things like GRPC. So, you know, we use these file formats for storing data, for storing models. Later today, Onyx will be mentioned as well. It's actually a Protobuf-based format, and it's a spec implemented as Protobuf. So you need to know, there's also HDF-5 for Keras as well. So, again, I'll get into all these in a little bit. Open accelerators. So, one thing that's really hard to do is actually do cross-platform math. That's not, you know, each chip vendor has their own way, you know, their own different kind of nuances for implementing things the way they do. So, different checks for different use cases. ARM-based architecture is predominantly for, you know, kind of the edge-based computing. You typically have NVIDIA and more narrow applications like TPUs by Google, among others. And you have other HPC vendors, like especially in Japan, working on their own kind of specialized AI accelerators for, you know, and they're all better at different things. Like so, if you do CNNs, it turns out you're better at computer vision, but other chips might be better at NLP. So, it's not, so there's reason, there's performance reasons and use case reasons why different chips only implement various operations or only implement optimized versions of certain routines. So, being able to have a cross-platformist decay for that would be interesting. So, something that can work with all these different things you don't have to. That's kind of what the deep learning frameworks take care of, but even then they only do half the job. So, it's still, so community efforts like the open blueprints that were discussed in the prior presentation are crucial to actually bridging a lot of things from research to production. And finally, again, metrics, you know, so, you know, we don't, we don't, I know that the general public talks a lot about bias in AI. You know, so racist models is something that comes up a lot and also accuracy. You know, eventually once AI is actually deployed everywhere, then how, how do we know what accuracy is? You know, so you have, you have a lot of programs out there, you know, LFAI being one of them that's trying to build out these standards. But there's, you know, there's also the, there's also the AI, the AI partnership like Google and Microsoft and open AI as well. So, there's, there's several different standards bodies kind of working on these things and it's still, it's still very early yet though. So, what is, you know, what is the notion of accuracy for different use cases?  How do we know something's robust? So, for example, one area, recommender engines. Recommender engines are inherently based on user preferences. So, how do you know what you consider accurate for a given user base? Netflix will be different from Amazon. You know, so for example, we have recommender engines out there. And as users, we all know that we get recommended the second you buy a vacuum cleaner, every site will recommend you vacuum cleaners. And they just, they'll say buy five more.  So, how do we know what we as users consider to be accurate? It's hard to define, right? So, these things are, it's still an open-ended research problem. For many different kinds of AI problems, whether it's something, you know, something like recommender engines or all the way to self-driving cars. And so, to start, so this is, this is an example of kind of the whole supply chain, you know. So, starting from, starting from the top, you have distributed runtimes, you know. So, Nvidia, for example, they have, they have their own, they have, they have, they have their own MPI based clustering stuff. You have, by the Apache Foundation, you have Apache Spark, which is typically used for different, different kinds of distributed SQL queries in ETL. And then, something, something that's not as well known, but heavily used in finance actually, real-time trading is Apache Aeron, or Aeron, which is used in a lot of, it's by some consultants out of London, heavily used in the ACA framework and a few other areas. And we use it as our primary communication layer for our distributed Spark training and deep learning for JAN. So, our framework obviously is one implementation of this. So, we focus on providing, you know, we, you know, we started as kind of this Java thing. And we kind of, and that's, that's the main, that's the main thing we dominate today. But again, we also work heavily, we also have a very compact C++ layer that interrupts with every deep learning framework out there. So, we can read and write TensorFlow, Keras, and soon to be Onyx as well. And then again, as I mentioned, the file format, so from left to right, you have Onyx. So, that's, that's kind of trying to be a neutral standard for defining neural networks. Keras and TensorFlow. Keras, even though people think it's a part of TensorFlow, it is, but it also has its own file format. Even though it's the same interface, it's actually, there's actually a higher-level file format and a lower-level one. People don't even know, people don't even know that. So, in order to fully use the TensorFlow ecosystem, you actually need two different file formats. A lot of people don't know that, right?  So, another thing people don't know, for example, is that TF Lite and TensorFlow are actually fairly different. It's a completely different runtime and everything. And so, the general-purpose one and the mobile-only one that our partner with Google on are actually very different. So, AI itself seems like it's well-defined, but it's actually not, and there's actually a lot of fragmentation still. There's also efforts in the compiler and IR space. So, there's one initiative at the Apache Foundation called TVM. So, it's basically an intermediate representation, which is basically a way, it's basically right before you get to assembly. It's a way for compilers to emit something that's platform-neutral. And then you basically, it's directly mappable to assembly on a particular system. There's also LLVM has something called, from Google, called MLIR, that's being contributed to the LLVM Foundation. That's meant to be kind of basically a way for LLVM to implement machine learning code now. So, and then this will allow you to target different accelerators. Both TVM and MLIR are both kind of competing standards in that space. I don't know which one will win. I don't know if there's going to be a Kubernetes or if there's going to be multiple.  The TPU, IBM Power, NEC, and then I know Fujitsu just has their deep-running chip as well. So, you know, the East Asia I think is, some of them are Arm-based, some of them are Arm-based, but some of them are just their own custom instruction sets. All these firms are implementing specialized AI accelerators, some targeting computer vivid, others targeting, you know, just time series use cases. But there's a lot of different ways to think about this. And finally, packaging. So, packaging is generally language-specific. So, you know, we have our own, you know, so as I mentioned, Google uses us for TensorFlow Java. So, we're the main way of packaging. Any C code you think of as only having a Python wrapper, we provide a Java, we provide an automatically generated equivalent with built-in garbage collection and a bunch of other things. So, we're a way of packaging. It's our format that's also open source. It's been used since 2012 for packaging Java projects.  There's all sorts of ways you can package things, and generally it's language-specific. So, again, this is just kind of our overview. If you have questions about other standards in this space, I probably know about it, but maybe just didn't include it here. But I just want to give you, because I'm mainly for brevity, but if you have, because I know like the, I know sometimes architecture diagrams get overwhelming. So, if there's anything in particular you're going to zoom in on, just let me know later. So, starting with open-source frameworks. So, you know, right now we're kind of, we just had an unfortunate kind of consolidation in the space. The PFN folks moved from Chainr to PyTorch. So, now we're down to almost kind of a dual-opoly in the framework space. You know, with us kind of being more of a back-end framework. You know, so you kind of have two ways to build frameworks, to build neural networks now. PyTorch and TensorFlow. So, but what most, I think end-users don't know is that actually, those are actually C-based code bases. So, PyTorch is actually just a Python interface for the old Lua-based, the Lua-wrapped C code base from Torch. The same is true for TensorFlow. It's actually mainly a C++ library that has a lot of logic with Python, but all the core execution happens in C++. You have, and then you have language specific, you have server middleware, which is anything where you write a web server or things, or a system language like Go, like Kubernetes, for example. Kubernetes is written in Go. So, you have a lot of middleware, Java, Go and C-sharp kind of in the enterprise space. So, generally, these frameworks will have SDKs for using models from those languages. We provide one, like I said, we provide one for TensorFlow, for example, in Java. And then finally, you have the, you know, we're actually, most of our developers, even though we look like we're Java, we're actually, like, again, most of our developers are actually C and C++ people. So, framework development is specialized, costly, and generally requires a breadth of expertise to get right, and to implement right to bridge to middleware. You know, in this case, to things like schedulers, you know, like Spark and all that, it requires a very specific and specialized team across multiple disciplines to work well. So, sorry, just backing up a little bit, ETL frameworks. So, I mentioned Apache Spark is one. There's also more traditional ones for commoner data like Pandas and Dask.  There's lots of different ways you can do that. Those are some of the popular ones in Python. And schedulers, so Kubernetes is obviously the Linux foundation. Ray is fairly new. Ray started as mainly used for reinforcement learning. It's built by what used to be Ampliab, which created Spark. So, if you're not familiar with Ray, it's kind of open coming. It's not in a foundation yet. But the Linux foundation might want to target this one. Just throwing that out there. But in general, I think it'll be, you know, it does have an open governance model, and I think they will be, I think they're figuring out what to do with it. It's almost out of the lab now. Yarn, so that's kind of the, Yarn and Mesa is kind of the old school Apache foundation. You know, that's kind of the legacy Hadoop days. They're still around, even if they're not growing, they're still around, they're still used.  It just depends on the use case, it depends on what's there. So machine learning frameworks, so not deep learning. You know, there is more than deep learning still. You know, just the more standard models that are actually still be used in the industry, like random forest and things like that. So you know, SK Learn and XGBoost, deep learning frameworks, TensorFlow, Keras, PyTorch, deep learning for J, and then serving middleware. So there's a lot of open source serving middleware out there. So there's Celna, there's TensorFlow, there's TensorFlow serving, and then it's kind of serving as well. So there's a lot of open source kind of serving middleware out there. You know, there's different ways of implementing this. And it's basically taking a model and exposing it as a REST API or something over a network. That's still, it turns out, you know, it turns out it's a hard problem, especially if you're, say, deploying at the edge. So for example, serving middleware doesn't run well on edge chips. And generally these things also don't, generally these things only bundle other libraries. And you don't generally have integration with the serving middleware plus a custom accelerator. That's actually an edge case yet. But if you want AI to be deployed for optimal speeds and minimal latency in production, it turns out that you have to do more than trick the models. You actually have to integrate the high-level middleware that developers talk to. And so that's still kind of an open-ended problem in it. That's actually the main thing we work on. It's just that part. Okay, so here's an example of a lot of different file formats. So again, there's a lot of different foundations where all this work kind of spans. You know, you have Apache Arrow. So again, this is an in-memory file format for just data interchange between, say, Python and Spark. So that was the use case this was developed with. But basically if you've ever heard of a data frame in R, or even if you just know an Excel spreadsheet is with columns, with columns and then add types on top of that, you have Apache Arrow. It's an interchange format. You have Arrow, Parquet. Again, those are both helpfully developed in the Hadoop because systems work as well. So these are all different ways of storing data. Sometimes they're used as configuration file formats. Portabuff is an example of that. So Onyx is Portabuff-based, developed by Google. And then JSON, I think we all know what JSON is.  So there's Portabuff-based file formats. There's HDFI, which is Keras. And then you have another one now is Flatbuffers, built by the guy who did protocol buffers. So there's N plus 1 file format standards, each better than the last sometimes. Some use for legacy reasons. So Onyx, for example, picked Portabuff just because it was widely adopted, not because it was the best one to use. And then you have other lower-level communications on top of those file formats. So in this case, GRPC and Aeron, as I mentioned before. So accelerators. So, you know, you have basically, so you can imagine application developers taking an open standard like Onyx and then wanting to run it on a target chip. And then that chip will have their own optimized math, the way of running certain kinds of, you know, maybe convolutional neural networks, computer vision, RNNs for time series, you know, or whatever optimized libraries might be present on that chip vendor's chip. So what you might want to do is you might want to have a cross-platform middleware that knows the difference between what runs well on Intel and what runs well on ARM. So that way you, as a developer, can just output, say, an Onyx model and then know you're going to get high performance on whatever chip. You know, one of the biggest, so the reason I bring this up is because one of the biggest problems of most, I think most people aren't familiar with this today, and the Onyx people should talk about is this idea of op coverage. So as I mentioned before, certain chips will only implement certain operations because they're targeted at certain use cases. So whenever you hear about TensorFlow, TensorFlow actually itself is a fragmented thing that runs differently on different chips. Most people don't know that. So for example, if you run on the TPU, they actually implement only a small subset of TensorFlow. TF Lite is a small subset of TensorFlow, which is for mobile, right? So chips don't always implement the latest architectures either. So for example, if you go to use QtNN from NVIDIA, they don't generally have the latest CNN architectures either. So this is a fairly common problem in the space. So these highly optimized libraries are generally behind the cutting edge. So it's hard to build something general purpose, and it's not their fault. You have a lot of time being spent on these things, and so you need to pick carefully what you put in your roadmap to optimize. So having something general purpose that knows what to use where can be really helpful. And so this idea of integrating these things is actually still a hard problem because you kind of have a lot of things happening in the lab in Python, then they build the model, but then it turns out that without a lot of extra pre-processing, so for example, TensorFlow, in order to actually deploy a TensorFlow model, there's actually a lot of pre-processing you have to do. Most people don't know that. So you actually need to massage what we call massaging the graph, so to speak. The graph is kind of the instruction set, so it's like you need to add and subtract and multiply. That's a graph essentially. It's just a list of operations that you need to run in order to perform some target operation in AI, as we call it. So in this case, it turns out when you call tf.save or keras.save, there's actually a lot of stuff in there and actually it won't run. So just because you can save a model doesn't mean it'll run properly. So like I said, there's a lot of noise. And then depending on if you want to run in a different chip or not, you might need to quantize a model. You might need to post-process it in some form. So that post-processing work is a black art. To most people who build the models, that stuff is a black art. And it's not clear what will work in what scenario. So sometimes you might have one model, you build for one scenario, and then a completely different model but what you're doing is you might make accuracy and you might make accuracy trade-offs and then just so it can run. Again, most people don't know that. So that integration problem and actually making AI models run, especially at the accelerator level, it's not a well-solved problem yet. Most people just... So anytime you see an AI vendor, what they do is they just use other people's software and then wrap it in Kubernetes. That's what most people do and they say they did their own thing. Actually, they just bundled a bunch of stuff and they don't know how the underlying silicon works.  If we're going to deploy AI models at the edge and actually go after this ubiquitous computing problem, you need to know how this stuff works. You can't hand-wave it. So you need to work with the community. You need to work with a lot of these efforts and that's essentially what we're trying to do. We're just trying to solve the integration problem. This is the only thing I do. So if you have any questions about that, please do let me know. And I'll end with metrics. So these are Linux Foundation projects. Some of these are Linux Foundation projects or widely used open-source visualization-based tools. So one thing I want to say is depending on whether you're implementing different kinds of model accuracy dashboard, a model bias dashboard, whenever you do that, you typically want to use open standards. So it turns out Prometheus and a lot of the things in the Linux ecosystem are actually very good for this. And so we typically think of these for DevOps. But what if you have the same kind of DevOps-style dashboard for modeling accuracy and bias? And then what if you data scientists could then use that output and say, okay, this is the real world version. This is how well we're doing. Otherwise they would never know. Because data scientists, one thing out there is they want to build the latest model, but it doesn't necessarily mean it's going to work in the real world. Machine learning models, they also have technical data. The world changes. What do you do when that happens? So a small example, that would be fraud detection. So fraud detection changes every day. So what do you do about that? You might build machine learning models to detect this kind of fraud, but you almost always need to retrain the models because, again, the world changes. This is called one way to describe this. This is something called concept drift. So you have the real world as one concept, and then what your model thinks is the real world is another. When those two things differ, how do you know you visualize it? And with that, I'll end and I'll take questions. Thank you.


The following is a presentation of TFNN. Trading Hour with your host, David White. Call now. Toll free at 1-877-927-6648 or internationally at<PHONE_NUMBER>. Now, David White. Welcome all to another excellent edition of the Power Trading Hour. Let's see what we have here. It's not done yet. I got something baking here on the machine. And as always, it doesn't matter. It doesn't matter where you're at. As long as you're here when the show starts. And of course, you can be anywhere. The following takes place between 2 p.m. and 3 p.m. And, well, what does seven minutes a difference make? Let's see just real quick here what the high of the day was in the S&P. 43, 47, I'm going to say. Oh, my cake is done here over here as I'm brewing data. So let me get this other thing started here while I'm talking to you. OK, now I can do that one. Anyway, so we'll keep a look at it. But I was hoping that we'd go off if you were bullish and test the lows either yesterday or today before the Fed that we just shot right up. It tells you that probably we've seen the high for the week. But options at least up until the start of the show have been rather bullish. We've talked about that for two weeks. I kind of thought that they would push it up in front of the Fed. And then it really looked like maybe they were going to push it down in front of the Fed. Then we had this huge move off the lows. But again, it continues to act like a bear market where you have these sharp fast retracements and not a lot of time to get in or out before the markets really start moving and your risk reward is in the rearview mirror. Give me a call at<PHONE_NUMBER> and email me at path at tfnn.com. I don't know. I've had tremendous luck. They're in here bad mouthing Microsoft. I've had tremendous luck with Windows 10 and Windows 11. But I'm programming on it. So maybe there's a difference. Maybe they're doing other kind of work as they call them now workloads. Anyway, we'll keep a close eye on the market here. We've got people talking. Well, now we're just up 23 points on the S&P cash. Could we get another thrust up? I don't know.  I don't know if you guys can hear that. A second bunch out of the oven here. So let's see you do that. And then I can do this. And then I can email that during the break. Okay. But I can't do anything else there. Until then. Anyway, just watching fairly sharply. But again, if you missed the announcement, really what they're saying is everybody's got seven rate hikes penciled in for the year. And that pretty much did away with any of the bounce that we've had so far today. I'm unclear if a lot of people were leaking that there wouldn't be, that we'd be back to four or something. But certainly there was something going on. Yesterday as we closed the day at about three o'clock, we had word coming out that one of the Fed appointees was not going to make it and retracted her nomination. And a lot of people were thinking that breathing a big sigh of relief because it would have been rather a poison pill for the market, the financials, the energies, just about everything. As a Fed representative, you're supposed to keep interest rates low and other things like employment high. I think that's the dual mandate idea of it. She had a very different idea that it should be used as a club for the way that some people think the government should be run, but very little to do with the actual Fed and its actual mission statement. So a lot of people were happy with that. We got a little good news in China. I have to give a tip of the hat yesterday to one of the dinners out here who was going along the Chinese stocks. The first thing I always thought of is when you're headed down, the advice of, I said, where should I come? No, no, no, it was Jesse Livermore. That is, if you ever come into a point where there's incredible depression in the market, then it's probably a good time to cover. You may see those later at another date, but you're probably in the right time for a really good pop. I never really got to see what was going on. I did like the idea, though, as poor as everybody was talking about China this week.  And of course, I think we were talking in the den about just how many people were talking whether or not stocks were investable at all if they were a China stocks. And generally, you get that kind of stuff just before you get a rip of 20 or 30 percent in a day in stocks that have been that beaten up. Doesn't mean that they aren't eventually going to zero, but you know what, these rips in bear markets are where you can make a absolute ton of money. And most people don't know it, but there's a guy, I can't remember the quote either, but he says, you make most of your money in a bear market. You just don't realize it at the time. Mostly because if you can buy a bear market low, you make so much money, but I do digress. Yeah, but that's it. I'll have to look at you. Someone's in the den talking about whether we've seen their highs. I'm just thinking maybe we have. And the reason why is maybe they've been selling calls all morning and buying back puts and getting that. Again, these numbers from options will track in and I'll run them after the show. But, you know, they've been fairly good at showing 4,300 to 40. Well, yeah, 4,300, 4,400 and actually 430 to 440 in the spies, which is actually where the options are written on them. So they've been fairly good and you got a pretty good indication. You got your rip, but we'll have to see. Now, if we go negative, it's not the first time that we'll have gone negative on a huge bounce on Fed Day, only to go down 50 points, only to close back up 50 points, depending on what they say. And of course, we've got about 30 seconds left. We've got Powell, which I think is supposed to talk at 230. So we'll probably have a lot more sideways action till about 315. Then we'll see what's going on. General Ripper, I know a major miner. He used to be my favorite major miner. Be back. David White's trading newsletter, The Path of Lease Resistance, is delivered daily before the markets open to make every trading day an easy win. Visit tfnn.com today and subscribe to David White's ultimate trading newsletter for $119 a month and try all of our newsletters risk-free with our 30-day money-back guarantee. Take The Path of Lease Resistance at TFNN Educating Investors. TAS Profile Scanner is the premier market profile-based scanner. 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Each host is an experienced trader and gives their take on the market while taking calls and questions live from around the world. From the moment the market opens until the closing bell sounds, Tiger TV has eight different shows with expert hosts to help you make the right moves with your money. Watch online at tfnn.com or on TFNN's YouTube channel and become the investor you were born to be. TFNN, educating investors. Meet internationally at<PHONE_NUMBER>. As we return, we'll keep a close eye on the index of moving. Kind of quieted down a little bit. We're up about 17 points now on the S&P cash. Maybe I'll get a chance to run the options here right before the show. Everything's running a little slow. Everybody's running a little fast. Kind of hard to walk by that the consensus at least is for seven rate hikes this year. Let me take a quick look at some of these other things moving here. Go down to 1900 here as we break through there in 1898. Let's see what else we have here. Dollar index, $89. And so I have everything right. That's gold. That's crude. Crued back to $75. $95.42 so we're kind of back to the lows the last few days with just sport. And kind of where I was thinking that we'll have to see what happens in the next hour or so. But if I was looking for a low in those, those would be right around now. Or between now and the end of the day. We'll see how well they do. If you get some kind of buy signal out here, some sign of strength. These things don't wipe out. But anyway, we're up to 99 bucks earlier in the day on crude. We'll see whether or not we can hold here. But it's going to be interesting. It's going to be probably a lot of up and downs with Fed testimony. So we'll take a look at it. What else do we have going on here? Did I have anything I wanted to do? No. I don't think so. Things moving just too fast. So we'll start looking at some charts out here today. Instead of news and everything else because it probably just really doesn't matter. One of the things we were looking for was Microsoft. I said it had one of the best looking options position. It may still get there, but look like a high possibility of 300. You got to 292.81 today. Volume in not all that exciting, but we'll see how the day ends up. Apple was another one that looked better. It got to 158.64 today. Some pretty good signals out here. At least a double gap at 162.50 for these. So I think maybe the 165 is starting to kind of get out of with two days to go kind of out of the way. But that 162 double gap right there looks very interesting for Apple. I would love to see this thing be at like 159.50 on Friday morning or something and then be able to buy the 160s for like a quarter or something and see whether or not we run into the weekend. Of course, we talked about this one earlier in the weekend that the production staff for the people that assemble the phones and stuff is Foxconn and of course they're closed for COVID issues. Unclear whether they open again on Monday, but I could see that kind of news pop the thing right back up if it is just a couple of days error. No question or no answers yet on Apple whether they're going to delay the rollout of these new phones. About here as we go through it. So those are two. Let's take a look at some other ones. Amazon was pushing harder earlier in the day. And again volume interesting. It was called 2.4 million shares so far today, but then more of a doji as you go back into 3000, which is pretty stiff resistance on this at the moment. But if you were following the options, which not a big indicator on Amazon any of these stocks that are $2,000, $3,000 options really don't have the same effect as they do on a $100 stock for indications. Let me put it that way. A lot of people buy way out of the money options on these big expensive or at least high dollar priced or priced in high dollars. Of course, it's going to get a what was it 10 to 1, 21, whatever it is, giant stock split coming at you. Let's see. Let's update this. Sure. Gonna just fool around here. Probably still a slight bullish bias to this week from options, but we will have to see. Okay question about potential blow off tops. If you got my newsletter this morning, you would have known Amazon or not Amazon and advanced micro had some of the highest short numbers yesterday of anything. So I figured those guys were easy targets to get blown out today. 1536 got a little bit of a kind of a tombstone doji going on here. I don't know if that's the end of that or if everybody just piled on short one more time out here. It'd be interesting to watch those numbers tonight. I don't get them in her day, but you now have a double gap right there at 110. So probably good indication. This thing is going to come back to 110. I don't know when maybe it's next week. Maybe it's tomorrow, but those double gaps get filled fairly quickly to two. Okay. What else do we have questions on CCJ from the first email coming back to support. Got about 8 million shares out here. You had a brief breakout never really got above that $28 with volume, which is now you're back into the trading range.  It started off at 10 bucks at the start of this run. So it's going to take a while to consolidate out those moves before we start looking higher out here. So yeah, tough to see a great deal going on in this over the next day or so. But you know, with the China things going well and China stocks doing well, or at least a big bounces today. What is it? Baba for BA. Let's go ahead and check that email. And if Steve Rhodes is listening, I sent you an email. Where does Baba in the weekend? I think right here at 100 bucks.  As I said, I love double gaps and one of the reasons that the art of timing the trade charts is written this way. But you've got, you know, you're right at that double gap line that goes back to the eighth and the ninth just a few days ago. So you've got into that. But unless something really happens on the market, my guess is they're just going to hold it up here around 100 bucks. My guess also is that there are a ton of options, both puts and calls that they can take to money heaven out here if it just holds up here for another day. And of course, probably a lot of people bought in at the Lowe's. But that's interesting nonetheless. So I'm thinking short term 100 bucks. See you next week. We'll get back. See you in China. It's unlikely. I'm not after seeing the tax pressure. You look at it. Yeah. Having fun trading the markets, but having trouble finding like minded individuals to discuss your trading and investment ideas with become an apex creditor in the trading markets and join the tiger's den trading room only at tfnn.com. 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Using this first of its kind program, The Art of Timing the Trade Charts allows you to scan thousands of stocks for Fibonacci formation setups including guardleafs, ABCs, butterflies and much more. The Art of Timing the Trade Charts is designed to help you when scouring the markets for stocks just beginning to form the trading patterns that many investors spend days, weeks or even months searching to find. And right now we're offering licenses available at only $79 a month. We are so confident that you're going to love this new charting software that will even give you a 30 day unconditional money back guarantee. Don't miss out on this incredible new piece of software. Get your copy of The Art of Timing the Trade Charts today by visiting tfnn.com. This segment is brought to you by Think or Swim. For more information, just click the Think or Swim banner on the front page of tfnn.com. E-mail already. What do you think about going along on BABA or on PDD? It seems to be China unlikely to invade Taiwan after seeing the world's reaction to the rest of Ukraine and how they're changing the position on Chinese tech stocks. Unless I saw some news that I didn't see, they're still not allowing anybody to see the books, which means that it will be delisted in the year. So I'm not a big fan. I mean, playing the bounce is one thing. And again, I would have been using options on any of this stuff out here. But I think if you've never been to China, the idea that they wouldn't invade Taiwan because what happened in Russia and Ukraine is probably a foreign idea to you. But they don't care about what happens next couple of years. They talk about what happens in 50 and 100. There's a lot of discussion about the culture, about one China. China is going to rule the world. If you think that Nazis had an idea of the master race, there's a lot of that going on in China besides all the other stuff associated with things in the 30s like slavery and force work. And of course, human organ harvesting. These aren't a lot of great guys. They don't think the way that you think or that I think. But I think I know the way that they think. And the idea is if we get all bogged down in the Ukraine, they can walk in there and anything they can do. And they'll be more than glad to wait five years. To them, it's not anything. The people will complain a little bit.  So it would be a lot different if they thought that was it. But they all have some kind of idea and brainwashed into the idea. But it doesn't matter what happens the next year or two. It's China in a thousand years from now. And we've got to make it great. We've got to do it now. And that's the way it is. They all have some kind of very strange idea. Probably not very different than what they have in Russia now. And that he knew he was going to probably take a little flack. Probably didn't think he was going to get as much as he did. But the question now that I posed yesterday was can you run them out of money and bleed them dry in a killing field to the point that Russia has to go home with its tail between its legs or at least Putin? And a lot of people standing up and saying that they don't like it. One of the big time heroes of the Soviet Union in Russia was out either last night or this morning saying that he didn't like it at all. I think he was one of the biggest generals of all time. There may be a lot of other stuff going on there. Literally everybody's corrupt. If you became a general, you're corrupt in Russia. So maybe he's got an idea that he could take over. You never know. But I think to judge China on the idea that they care what happens in the next couple of years, I mean, what do you got to figure? The quarter of our senators and congressmen are at least compromised in some way or another by China. So yeah, maybe they have to all talk tough for a year or two. But how many of them are going to want to get some of that sweet, sweet, sweet China money in the future? So I'm not going to discount that as much as other folks are. It's probably a little less likely, but not impossible for them to still invade Taiwan. Okay. Let me read this here. So that's it. Don't get too far ahead of yourself on China. They do not think the way that we think. Although I think the next generation might just depends on how quickly we get there. It's kind of like Russia. We've already had theirs. Kind of take a look at it. I can't remember. Did I look at NVIDIA? NVIDIA. Take a look at that real quick as we come back to do. Yes, I looked at it already. Oh, the quad won four guys. Okay. I get the idea. Okay. Well, he came in with the same volume at 246.65. That was the February 28th high. He did go over it. Well, he didn't go over it. He went under it by a buck. And now you got a tombstone doji out here. Energy was actually fairly good on this way back up. But I don't see much happening here. Okay. Now we're down six. Is that right? Yes. Six bucks on the S&P. Very interesting action today. I'm going to say I went back through the details. One of the reasons why I wouldn't long go on into this is that any bounce you get 80% of the time by the time the Fed comes out, it's evaporated. This is a little bit different in that the bounce evaporated after the Fed came out because generally you have a nice little bounce. It's given up by 11 o'clock and then everybody just sits around looking at each other till 2 o'clock. We do have Chairman Powell talking at the moment. So we'll see.<PHONE_NUMBER>. Email me at path at tfnn.com.  Okay. And a question on UVXY. Have we seen the high of the week? I suspect so. It'd be very tough even if we start heading UVXY. If we start heading up, we'd have to lose everything. We'd have to be back down to 4114 I think to break 23 down. There's a few reasons why, but most people don't understand how the VIX actually works and it would take a great deal to move it that far. So no, I think we still probably, I'll look at the options hopefully during the next break, but I still suspect that we're at 4,300 on the low end, maybe 4,350 or so. But it will depend. We may be just be waiting for the Fed to quit yakking and move on, but we'll see what else we have out here. After the bell tonight, I did want to get this in because it is in earlier. By the way, oh, by the way, volume is about 10 billion shares. So it's going to be a decent volume day, depending on what happens. There's Erling. After the bell tonight, we do have Lenar in housing, of course, not good news pre-market on loans. We also have William Sonoma. Don't think either one of these is going to move the bigger market that much. If I get to pin action on this, William Sonoma, kind of close back up to its high. So that may be the interesting one after hours. We'll be back in a minute. From home or investment property, Tiger Realty has the experience across all areas of real estate in the Tampa Bay area to help buyers and sellers make the most informed decisions across all price levels. From the price you should be paying per square foot in certain up and coming areas to the type of cash flow investment properties are capable of creating, Tiger Real Estate can help you make the best decision when it comes to all areas of the market. Before you make one of the biggest decisions of your financial future, call Tiger Real Estate LLC today at<PHONE_NUMBER> or email us at tiger at tfnn.com. That's<PHONE_NUMBER>. Call us today. The technology around us is changing every day. With so much happening, it can seem impossible to keep up with all the information. 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That's $155.92 with 8 million shares. I got into that yesterday with a 4.2 million shares and you pop back in it. Now, again, just because we've got lows doesn't mean we're going higher instantly. You could get a U bottom out here, but it does look good and the risk reward is probably okay. He did spike it today. I'm told, although I don't spend a lot of time looking at these, that they're big into NFTs and everybody I hear that was chanting all about Bitcoin is now chanting about NFTs and the metaverse. And they got to throw their money away on something. Now, the question is how far can this run before everybody figures out it's just another Bitcoin kind of thing. Could go for years. I don't know. This just seems a little even a little bit more silly than Bitcoin for the NFTs, but you know, Bitcoin ran for a long time. Anyway, what you want to see this thing do is close back above either the nine day or three by three displaced moving average to get a signal that it's on the move. You can sit on your hands and put a stop down below, but it would probably take something like that to get it going much higher. And I don't see a lot of risk in it. You've got a decent low out here. It's just the market's a little weirder than most people think, but you don't have that much higher to go to get a close above the nine day one sixty three sixty nine. So that looks okay. Bitcoin. That's what I always thought it was, but I do not know. Okay, back to kind of flat options continue to be at least a little bit bullish when I looked in during the break. We're up 29 points now on the S&P cash. And again, this is a lot more like indigestion. You know, something's going on, but whatever it is, it's kind of painful at the moment. Question from Ralph on Tesla. See anything else? Now, I think this thing's just going to slowly meander back down to 600 bucks. You're going to get some big bounces between there, but I just think you're setting up a move back to about 600 bucks. But we'll see. And it's hard for anybody to look at this and think that it doesn't go to 1100 bucks if they're in the cult. And that's what matters. Not what I believe, but what the cult members in Tesla believe it's a 40 or $60 stock at 800 bucks today. So, you know, what can you say? Stocks can be mispriced a lot longer than I can talk about them being mispriced. Okay, what else do we have out here? Let's take a quick look at the SMHs as we're here. Yeah, you popped on this. You did so on fairly decent volume. It went right up to the mid part of the gap and filled that. You are above three by three. Again, I think probably by the end of the day, we're going to start seeing a few more bullish signs, at least for the next couple of days. But we shall see. I don't see anything really bearish in that. Question about taking a quick look at micron. Of course, this is the one that everybody jumps on instantly when they think a bottom is in. The SMH is volume is okay today. 25 million shares so far. That goes back into about 28 million shares back on March 9. And what did you have? 28 million shares on March 8. So about average volume so far. So I think you're okay on that. You did gap back above the nine day. It may do a little bit of back and forth out here. But as long as it stays above the nine day, I think you have to stick with it if you're long already. Question on planet here. Is it finally bottom PLTR, right? It is. Okay. So what do we have here? Well, you're going to close above the nine day. We already talked about this one making a fairly decent low. So yeah, not a bad looking thing. Just how it depends on how much money you want off the low. I would say 1325 is where this thing's headed. And at that point, you've got a lot of volume to overcome from this big thrust down on the 17th of February with 151 million shares. So yeah, could you get up there slightly? If I got 1325, your head and my head and everybody's head would spin so fast in me selling it. But yeah, at least it's made some kind of low out here. Seeing some more of these stocks in that vein trying to make some lows. Okay. What do we have out here? Okay. Quick look at GDXJ. GDXJ. As we look at gold at 913. Probably got your low in. I don't know how much there is to the upside now in gold, but I think that little pattern out there probably made it. Probably not a bad deal. You got a little doji out here. It's going to take some work to get this thing going a bit. But yeah, I think you're okay probably setting up for a retest of the $50.29 high in the GDXJ. Going back to the email circuit. Let me get rid of this. Come on. Okay. Question about the XLF. Forget to hear. Is this done at an island bottom? I don't know what you're talking about in the island bottom. Not really. No. You just had a low. You're back into the trading range. Pattern is not all that bad. You're going to have seven interest rate hikes they're telling you today. You're back into the trading range. If you're long it, I think you stick with it. If you're not, if this thing could pull back to $37.50, I think that would be one of the probably the financial on this sector, probably going to be the hot sector of the second half of this year. And you know, we're pretty far along already into March. So probably not that long, but generally in these types of things, you're probably talking about the real action and movement starting in May and going across the summer and the next fall for financials. As long as they keep on running, the interest rates higher.<PHONE_NUMBER>, 913 on gold last tick. Let's take the crew here real quick. Dollar index 98, 78. And crew, what do we have? The 74, excuse me, 94, 7. We'll be back. Sharpening your skills as an investor is like getting better at playing a musical instrument. 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Options continue to be... and look a little bit bullish out here. I don't know what seven interest rate hikes will lose. To bullish, of course, after the bell of the night, Lenard, and that's probably going to tell us a lot about employment going forward, but keep an eye on that. Up 37 points. Let me update this real quick just to make sure. At 43 points. Crude's down at $1.50. Gold's, of course, been bouncing around the lows here. I'm going to leave you with this. I talked to a friend last night that is very deep, deep, deep in the carbon business for fossil fuels. He told me a couple of things, and he told me that this weekend, too, and that is if you're going to buy tires for your car, buy them today. If you're going to need oil for your car, for oil change this summer, buy it today. If you're going to need anything that's associated with fuel, buy it now. He says we're going to have a summer of those things being incredibly hard to get, especially if you have anything like airplanes or motorcycles, all of that stuff. Apparently the guys that make most of this, when the fuel prices started to rise, backed way away and didn't order their stuff. They didn't want to get caught with a bunch of stuff that was going to be very expensive if the fuel prices came back, or at least crude prices came back down. Try and remember what it was. There's a barrel of oil in every tire or something. Can't remember what the thing was, but it's big. Get it now. Anyway, I don't hear anything else on the other side. I should be hearing my music play on the way out. But that's it. Appreciate it. We'll see you tomorrow on St. Pat's Channel.


What has government done to our money by Murray and Rothbard? read by Jeff Regenbach chapter 1 introduction Few economic subjects are more tangled more confused than money Wrangles abound over tight money versus easy money over the roles of the federal reserve system and the Treasury over various versions of the gold standard, etc Should the government pump money into the economy or siphon it out? Which branch of the government should it encourage credit or restrain it? Should it return to the gold standard? If so at what rate? These and countless other questions multiply seemingly without end Perhaps the babble of views on the money question stems from man's propensity to be realistic That is to study only immediate political and economic problems If we immerse ourselves wholly in day-to-day affairs, we cease making fundamental distinctions or asking the really basic questions Soon basic issues are forgotten and aimless drift is substituted for firm adherence to principle Often we need to gain perspective to stand aside from our everyday affairs in order to understand them more fully This is particularly true in our economy where inter-relations are so intricate that we must isolate a few important factors Analyze them and then trace their operations in the complex world This was the point of Crusoe economics a favorite device of classical economic theory Analysis of Crusoe and Friday on a desert island much abused by critics as irrelevant to today's world actually performed the very useful function of spotlighting the basic axioms of human action of All the economic problems money is possibly the most tangled and perhaps where we most need perspective Money moreover is the economic area most encrusted and entangled with centuries of government meddling Many people many economists usually devoted to the free market stop short at money Money they insist is different. It must be supplied by government and regulated by government They never think of state control of money as interference in the free market a free market in money is unthinkable to them Governments must mint coins issue paper define legal tender create central banks pump money in and out stabilize the price level etc Historically money was one of the first things controlled by government and the free market revolution of the 18th and 19th Centuries made very little dent in the monetary sphere So it is high time that we turn fundamental attention to the life blood of our economy money Let us first ask ourselves the question. Can money be organized under the freedom principle? Can we have a free market in money as well as in other goods and services? What would be the shape of such a market and what are the effects of various government controls? If we favor the free market in other directions if we wish to eliminate government invasion of person and property We have no more important task than to explore the ways and means of a free market in money