Shadowing Practice: Easy Migration from Postgres to Databricks Lakebase - Learn English Speaking with Video
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Hello, Spark fans.
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Welcome back to Advancing Spark.
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Brought to you by Advancing Analytics, your favorite data and AI engineering consultancy.
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I want to talk to you about Lake Base some more.
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So I did a video fairly recently just about what leg base is, how you get started with the world's most...
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Underwhelming demo of just kind of honor networks.
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I'm I It's really interesting because there's a whole bunch of people who are using LakeBase now
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and the most common thing is for apps I've built an agent, I've made a little chat agent, I've made a little kind of data entry app,
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vibe -coded things up, and I want to have good state management, and so I put it lake -based.
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Makes sense.
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and the thing that that misses is what if I'm already doing that?
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Perhaps aren't new.
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Databricks, whilst they've been innovating and building a load of crazy stuff, The whole idea of I've got an application, that's not a new thing.
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So what if you already have?
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an app database knocking around.
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You know, it would be nice if it just talked to Unity Catalog.
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I'd love to use the branching, the scale to zero, the instant restore, all of that good stuff.
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That sounds great.
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Can I have that, please?
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Well, how do you get there?
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So I ended a webinar earlier this week with Denny Briggs about how you migrate Lake Base.
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How you take an existing Postgres database and then just port it over, restore it into a lake -based instance, and then get going with it.
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So I want to do a bit of a bit of a run through.
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We had a couple of connection problems and I had deleted a lake base
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and left a soft deleted tombstone and it was annoying and it broke.
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So I just want to show you how it works when we validate things and we migrate things. and advancing.
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So yeah, that's the plan.
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So how do you go from a Postgres database into LakeBase?
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What are the gotchas you need to look for really quickly?
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But then how do we do it?
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How do we just hit a button and migrate and get it across?
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That's the plan for today.
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So, if you're new around here, as always, don't forget to like and subscribe.
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I will be talking about Dead Reye Summit very, very soon, because that is coming up right up in just two weeks.
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But, yeah, let's go have a look at this stuff.
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Start!
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Yeah, I've got a big old slide deck.
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I'm going to skip.
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We don't need slides.
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Skip, skip, skip, skip, skip.
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Ignore, ignore, ignore.
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Yeah.
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One, if you're migrating.
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If you're looking at OLTP, it's different to OLAP.
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Well, that's no shock.
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We know that.
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That is fine.
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And then there's this piece.
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Okay, well, it is Postgres, but it's not Postgres.
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I mean, that's not quite true.
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So LakeBase is a managed instance of Postgres, and like all managed instances, it has some things
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that they've put into the platform you don't have as much control as you would have
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if you were doing it by yourself.
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And so I want to run a couple of those gotchas first.
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Because you are inside a managed pooled some kind of state managed instance of Postgres.
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There's some stuff you'd be doing yourself if you've just built your own server
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and you're running it manually that you can't do in Lakebase.
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because you're giving away that control.
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in order to get all the nice auto scaling, parallelized separation of storage compute.
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So yeah, the things that you can control are slightly different So you don't have the shell, you don't have Postgres logs,
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you don't have to go and put a load of traces and scrapers around the Postgres logs to actually manage things.
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You don't have the various different authentication conf files you'd have to go
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because you're living inside of the Databricks control security ecosystem And You don't have as many things you can tune.
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So, observability, that kind of stuff.
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You can do your PG stat statements and some of the ways you build it manually.
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other things are just automatically gathered for you
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There's a whole bunch of telemetry that just exists in the system
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that if you've built that into your own existing Postgres app, you probably need to pull that out because some of those extensions aren't installed.
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So it is not just a straight PG dump, PG restoring life is going to be good.
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It is for the most part, but there's some features that are turned off because they're replaced by inherent Databricks functionality.
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That's the main thing that you're going to come across.
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So Those kind of gotchas come across these four areas.
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Session stay is a biggie.
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We'll talk about what that means.
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Auth types, it's not really that big, it's just they consolidate into two main types.
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Extensions might trip you up if you've kind of built your own ecosystem inside of a single Postgres database.
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There's just a different way you do it inside of Databricks.
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And quotas probably won't trip you up, but it's worth being aware that there are some quota size limits on the platform. So, Session stay.
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The main thing comes down to because you are going through this kind of session pool.
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If you've built a lot of things inside Postgres that are
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Managing state within a single session and then you've told it to scale to zero
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when it comes back it's going to have a different session state Kind of makes sense.
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So if you're using temp tables, If you have a big, long -running process that you've paused for someone to respond to in the app,
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And then actually it's turned off in the meantime, because you had it scale down to zero in one minute.
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And they come back.
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Well, they're going to come back and find that there aren't things.
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The temp table is not there anymore.
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The prepared statements are gone.
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One of the classics that a lot of people use Postgres for is doing big, long migration wizards.
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and if that's using advisory locks to manage the state of what it has and hasn't done yet, but then that goes away and there's a different system waiting for approvals and things.
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When it comes back, those locks may have disappeared because the thing's turned off.
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So if you've got anything that's looking at the actual state context, Just be careful about how you're doing the scalability and how you're looking after scale to zero.
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Because if it turns off, yeah, you're going to save some money, but you're going to lose your session state.
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So just manage that.
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Think about the separation of what you're managing inside Postgres, what's being managed by the app, and actually how you link the two together.
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Is the app sending false keeper lives, in which case you're not going to send the scale down to zero, Or is it basically a dead state, a syncretist is going to push it back again, in which case, if it does scale down,
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you just need to re -architect things literally.
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All it is, it's just you might want to design some stuff slightly differently if it's going to turn off.
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No shock there.
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Extensions are probably the one that's going to cause the most gotchas in terms of what people currently use it for.
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There's a bunch of things that people build into this stuff, which you wouldn't necessarily do when it's inside of Databricks.
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So we will went through some of them.
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There's a whole bunch of extensions that are pre -installed in Lakebase that you don't need to worry about.
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So the only issue is if you're using some of the stuff that isn't pre -installed, and most of the time it's pre -installed because
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It's not pre -installed because actually there's a different data rich feature that replaces it.
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So take PG -Cron, for example.
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That is, if you're like me from the old days of SQL Server, instead of your SQL agent, that is your temporal scheduler.
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run this every hour, run this every day, it's a cron job, right? that doesn't exist, that is not being implemented inside of LakeBase,
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it's not an allowed extension But if you're doing that inside of Databricks, Well, you'd run it via DataBix jobs.
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DataBix has its own scheduling system.
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And actually, if you had a little cottage industry of things scheduled inside your Postgres database,
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inside Lakebase, well then that's not going to be visible to the rest of the database ecosystem that's not ideal
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So it's that kind of thing where we tend to see those extensions are enabled.
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So yeah, PG -Cron just pull them out and replace them with database jobs and workflows the kind of foreign data warehouse, kind of the links.
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I mean, Yes, you would do that via either automatically using the Lake Bay Sync into Unity Catalog,
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and then just running queries on there or using things like foreign catalogs and You can run a cross database query, cross database query inside Databricks from Unity Catalog,
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pulling together things from different lake bases.
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That's perfectly fine.
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You wouldn't do it inside the lake base.
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So just a slight difference in terms of where you think about things.
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If you have a little operational
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job that needs to kick off inside your lake base that needs to do a foreign database query, you need to think about actually how you're going to architect that because that is not enabled inside a lake base.
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Time scale DB? and they've not really enabled it for that kind of workflows.
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There's different areas of Databricks where you would build that kind of real -time analytical querying.
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And then anything where you're kind of building out your own random systems using programming languages where you've got PL Python 3, PL Perl, you're building out Perl,
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Java, Python, all those extensions.
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No. Certainly what we've found is the majority of times people use those things.
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It tends to because Well, the SQL functions didn't exist back in the day to do that and actually they do now
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but people haven't gone back and refactored all of their legacy code.
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It's quite rare you actually need to do that.
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especially because you can just lift it up into the app layer for that kind of stuff.
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or you've got all the rest of Databricks, which is very programming language heavy. So...
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Like it's an architectural layer separation of going, no, no, lake bases should just be pure database things.
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And if that's the case, to look at what you're doing with that code so
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if you do have a really really heavy load of
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that kind of extension that kind of extra custom code built stuff
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that's not so easy to migrate because those extensions aren't enabled.
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Also the Pub /Sub stuff.
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So if you're expecting people to be able to subscribe and pub all within there, that's kindle into the session state, kindling to extensions.
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You wouldn't do that inside LakeBase either.
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Yeah, it bubbles down to essentially using either OAuth, which would generate a token for you, And that token has a one -hour time to live.
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So you just need to make sure whatever app you're building that's integrating into Lakebase, either it uses the, it's a Databricks app,
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which has got its own Lakebase integration baked in with the token kind of revamp, or you're building an app separately,
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you just need to build in the time of how long until it forces a recheck out of that token, because tokens are going to expire after an hour.
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That's fine.
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That's a very, very standard integration.
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Or it uses postcode password, which isn't ideal and we don't No one recommends ever.
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So if you're doing things that have certificates and you're doing some things in the slightly more esoteric, one of the many different ways you can set that up in Postgres,
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you just need to change how you're managing that security when you migrate to Lightbase.
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Thank you.
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And then finally, yeah, the limits.
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So the raw lake -based limits, again, these are lake -based limits as of when I put this stuff together probably about a month ago.
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And again, this may change.
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Currently per lake -based project, you've got about a 16 terabytes storage limitation per instance.
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You can look at what that's going to be.
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4000 max concurrent connections.
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Although if you have a synchronized table, that's going to just squat on top of 16 of those.
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So if you've got 100 synchronized tables, that's going to take a fair chunk of your connections.
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You should have an idea about how big your leg base can be.
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The majority of times we've seen people using it, especially for your business apps kind of thing, get nowhere near these kind of things. So...
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Yeah there are some limitations.
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Thank you.
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all right
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so we're gonna skip through how you run it i mean
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how do you run a migration you check it check what you need to do
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and validate it at the start you decide any limitations
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and compromises you rehearse it and you test it and you make sure it is
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Absolutely perfect and spot on when you migrate it over.
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And then you do a cut over and it all works happily and you go off to the pub.
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tends to be how we run things.
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That's more for the webinar, not for here.
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This is what I want to talk about.
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I want to talk about how we actually run it.
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And at that stage, we can get rid of some slides and actually just talk about a little accelerator that we built.
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This is kind of like the way most people are building migrations these days is Because...
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clawed and all that kind of stuff and made it so much easier to build these things.
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I built this out originally since the demo.
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And then actually it works so well we've been using it
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and actually just using it to run migrations and we're building out.
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Pulled out my old demo environment just to show you guys a cut down version of how it looks.
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I'm going to start off going to get this started going.
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So essentially I've got this
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this CLI kind of environment and what this does it's got like four demo Postgres databases in there.
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So it's going to spin up like four fixtures for me, a nice clean one.
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So just basically a bog standard, nothing special copy of Postgres with a sample database in there.
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It can take the same sample database and then install some features
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and use a few things that just aren't quite compatible that need a few fixes.
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Those kind of fixes we can do automatically.
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There's some things you can get, oh, you've got a certain user assigned to a super user.
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That's all going to be the Databricks super user.
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We can just fix that through as we migrate.
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There's then some someone which uses some features that you actually kind of quite easily switch over from.
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And then there's one that's got a ton of things that just simply do not work.
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So step one is actually just to understand what's going on.
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So we built a validator that's got basically a config file.
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That's got what the current compatibility level of LakeBase in there.
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We can do essentially a scrape out of Postgres, compare the two and go, what features is it using?
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What's it not using?
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What does it actually look like?
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So if I just pull that over into here, I'm going to pull it up a little bit so you guys can see everything.
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And I'll bring it up.
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Thank you.
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So if I just run this validate step, Now normally we'd validate it just for a certain database.
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In this case, I'm just running all the validation reports.
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Indeed, that's the kind of thing I'm expecting to get, right?
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So what's.. how clean is it?
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Is it just to play supported?
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supported with a few manual changes, supported, but you're going to get some degraded performance.
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It's you've got a compromise baked in there.
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There's manual fixes you need to do, or there's an absolute blocker that cannot be worked around.
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You need to re -architect how that works before you can migrate.
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That's kind of the level of things we're just trying to get this idea of.
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Basically, what are we dealing with?
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So if someone rang us up and said, "Hey, I want to do a migration," It's where we'd start, right?
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We just run this script, run that scraper and go, Does it work?
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Does it not work?
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It's a major rework.
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Yeah, it's going to need some major rework.
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Let's have a look at what that markdown looks like.
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We're going to leg base, going to PG -17.
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It's...
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Got a load of problems in there.
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Oh, it's not wanting that.
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There we go.
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So we can see the things it's got, the blockers.
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Yeah, it's got a Pub /Sub in there.
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It's trying to do some replication.
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um that's not going to work those things are not enabled not installed in lake base There's a few things that yeah, it's just going to rewrite super user -owned objects.
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That's all fine.
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But the real thing is what we're going to do with those bits that it's got at the top.
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I hope we can have a look at a clean version.
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Well, it's a lot easier.
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So we can see, yes, the security role learner we need to change over.
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Everything else is just a straight rewriter security.
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No other things that we're actually going to lose perfectly fine.
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If we go and have a look at the minor fixes, probably the one that we'll run with, Yes, so that does have a few degraded things.
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Degraded is all about the security granularity that we've got and the public session caches.
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It's just slightly different.
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the way it manages caching because again caching is all around the state
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and management and when you're in a shared buffer pool
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so we can do the separation of storage compute it's just gonna work slightly differently um so yeah
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essentially we're fairly good there so we know at least the clean
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and the minor fixes we can just migrate those we can
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just go yeah pick those up push it over deploy that over So I've got a DeadRex workspace up here.
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I'm in my nice shiny lake base persona switcher.
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So I can see all my lake bases that I've currently got sitting in here.
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I'm just going to try and run a migration wizard, say.
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Just copy over.
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Just push it straight over and see how it goes.
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and this is the thing that broke for me
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because i had deleted it just beforehand
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so i'm trying to do it with the same name i'm going to grab
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that pick it up they run from the the last version of the report which is one i've just done
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Go ahead and push that across.
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which is essentially going to go and actually pull some things out, basically do a PG dump and a PG restore behind the scenes, make some changes of what we're actually doing as it goes,
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create a new database project.
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There you can see my old project is a ghost deletion that's sitting around.
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We're going to get demo fixes too.
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Fine.
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That was a fix I hadn't put in previously.
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So it's provisioning out, it's building out the schema, it'll then do the data upload, and then do a quick bit of validation to make sure it's gone across successfully.
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And then we can go and have a check.
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And again, this is how most migrators are working these days.
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You can actually start to build these things really effectively, really easily.
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to go It's a lot easier to migrate something from A to B than it used to be.
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There goes the schema has gone across.
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We're now doing a data push across, so we should actually be able to go and see
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We've got our leg base over here already or is it kind of currently building it?
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We will see!
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There we go!
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So demo fixes number two has gone across.
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We've got that.
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So it's currently turned on.
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We can see it's currently active.
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It's pushing that stuff in.
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There we go.
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Ah, perfect. So...
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Project's created, schema's created, data's created.
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It's done some verification.
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Easy as that.
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Takes a couple of minutes.
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I bet again, for a fairly small database, it's not crazy.
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We're going to have a look at the tables in here.
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We should be able to see we've got a bunch of stuff that has been migrated.
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There we go, and this is just the.. is it pager?
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straightforward Postgres sample, it's the adventure works of Postgres World.
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Just going, I've got a whole bunch of data.
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You can see how quickly that's gone across, how quickly it created, given how recently we actually set this project up.
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That's all ready to go.
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If we go and have a dig into it, you can see constraints are across, indexes are off across all of our various different things have been pulled across.
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It's not just a straight scrape the basics and pull it.
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That is a full migration of all the complexity, everything that was in there.
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pushed from A to B as a quick CLI check.
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And that's how it should look.
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I mean, yeah, you can put a shiny app on top of a little progress bar and that kind of thing.
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But as a developer of Songkink, we've got a Postgres database.
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We need to plan a migration project.
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This is going to take us months and months to months to get it over.
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I mean, honestly, it might do if you have huge amounts of custom code using some of the extensions like PLPython3u.
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But if you're just using straight Postgres and you've got a load of data and it's about, I need to get the data from A to B.
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So easy to get it migrated and set up inside a lake base.
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Now, I mean...
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Slight bit of fish -eatishness there.
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Obviously, You're going to need to run a load of testing.
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We've got some validation reports and kind of testing matrices and that kind of stuff that we'd run on top of it.
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You need to plan a cutover.
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So yeah, you can hit go and say, does this actually work?
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Into a branch, delete that branch, make sure it's working.
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Maybe you copy it over into a branch You put a copy of the app onto it, make sure that works, keep the two apps dual running and then switch over when you're happy things are going.
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The fact in like basically you've got the various branches gives you the ability to do test migrations
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and change reworks and test some stuff.
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push it a few times, do an elegant cutter, when you promoted one to production.
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Life's good.
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So yeah, lots of ways you can do it.
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Lots of...
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Yes, I've migrated the nice, easiest version to show how quick it is.
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But this is stuff that used to take weeks.
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to get put over.
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You can now, if you're looking at Lakebase and you've got a Postgres database already, So easy just to...
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Spin it up, try it out, see if it works.
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what are you gonna lose in terms of pushing it giving
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it a go testing it kicking the tiles in it and going oh
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this is really fast and it spins up from zero in less than 500 milliseconds
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means you can have a really really tightly coupled compute profile
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to your usage profile of who's actually using the apps and things they're using them for.
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just makes sense to me so that is all i wanted to show you
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that is how we go about migrating lake base from uh an existing managed progres into lake base proper Now, if you're doing it from something else,
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If you're doing it from SQL Server, maybe you're doing it from a different object store, you're doing it from a different managed postgres elsewhere.
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Maybe going from Oracle and you're like, "Well, I want to switch over to Postgres and I want to move it into Lakebase."
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There's a few other validators that we do as a pre -step.
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You essentially do a syntax translation first into a default postgres
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and then push it via the same tool just so we can keep those components working.
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That's how we go about doing it.
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loads of different ways you can do but Don't let the fact that you'll have to change some things put you off, because it is so easy to get you into there.
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All righty, that is all I wanted to run through as always.
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Thanks for joining me.
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Don't forget to like and subscribe and yeah check it out because it is impressively fast and scalable.
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I like it.
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Cheers.
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Bu dersin kelimeleri ve konuşma notları
Bu C1 seviyesindeki konuşma dersi “Easy Migration from Postgres to Databricks Lakebase” videosuna dayanıyor. En çok tekrarlanan kelimeler: postgres, lake, database, base, inside. Bu videoda gölgeleme çalışması için 339 cümle ve 4250 kelime var. Konuşma bölümü 21:07 sürüyor. Konuşmacı hızlı konuşuyor, dakikada yaklaşık 201 kelime; bu yüzden birbirine bağlanan ve zayıflayan sesler duyacaksınız. Kelimelerin %83’i İngilizcede en sık kullanılan 3.000 kelime arasında; geri kalanına başlamadan önce bakmakta fayda var.
Bu videodaki önemli kelimeler
Videodaki en ileri düzey 15 kelime, telaffuzu ve anlamıyla:
| Kelime | Telaffuz | Anlam |
|---|---|---|
| token isim | /ˈtoʊkən/ | jeton |
| gotcha | /ˈɡɑ.tʃə/ | yakaladım |
| compute fiil | /kəmˈpjuːt/ | hesaplamak |
| cache isim | /kæʃ/ | önbellek |
| query isim | /ˈkwɪɹ.i/ | sorgulama |
| ecosystem isim | /ˈikoʊˌsɪstəm/ | ekosistem |
| subscribe fiil | /səbˈskɹaɪb/ | abone olmak |
| architect isim | /ˈɑɹ.kɪˌtɛkt/ | mimar |
| schema isim | /ˈskiːmə/ | şema |
| scrape fiil | /skɹeɪp/ | kazımak |
| synchronize fiil | /ˈsɪŋ.kɹə.naɪːz/ | senkronize etmek |
| workflow isim | /ˈwɝkfloʊ/ | iş akışı |
| bake fiil | /beɪk/ | fırınlamak, fırında pişirmek |
| wizard isim | /ˈwɪzɚd/ | büyücü, sihirbaz |
| authentication isim | /ɔːˌθɛntɪˈkeɪʃn̩/ | kimlik doğrulama, kimlik kanıtlama |
Duyacağınız deyimsel fiiller
| Kelime | Anlam |
|---|---|
| turn off fiil | kapamak |
| come across fiil | karşıdan karşıya geçmek |
Tekrar etmeye değer cümleler
Videodan, günlük konuşmada yeniden kullanabileceğiniz kısa ve tam cümleler:
- Can I have that, please?
- What are the gotchas you need to look for really quickly?
- I mean, that's not quite true.
- We'll talk about what that means.
- Basically, what are we dealing with?
Bu videodaki dil bilgisi
Konuşmacının en çok kullandığı yapılar, videodaki sözcüklerin aynısıyla:
| Yapı | Videoda |
|---|---|
| Present perfect continuous have/has been + -ing — önceden başlayıp hâlâ süren bir eylem | they've been innovating · we've been using |
| “Used to” used to + fiil — artık geçerli olmayan eski bir alışkanlık ya da durum | used to be · used to take |
| Present perfect have/has + fiilin üçüncü hâli — geçmişte olan ama şimdi de önemli olan bir eylem | I've built · they've put · you've just built |
| Edilgen yapı be + fiilin üçüncü hâli — kimin yaptığı değil, ne olduğu önemli | aren't installed · are turned · being managed |
Dikkat edilecek telaffuzlar
Konuşmacı you're, you've, don't gibi kısaltılmış biçimleri 146 kez kullanıyor. Bunları duyduğunuz gibi kısa söyleyin.
- “sh” ve “zh” sesleri: cache /kæʃ/, validation /ˌvæl.əˈdeɪ.ʃən/, limitation /lɪmɪˈteɪʃən/, authentication /ɔːˌθɛntɪˈkeɪʃn̩/, deletion /dɪˈliːʃən/
- Uzun kelimeler — vurguyu doğru yere koyun: validation /ˌvæl.əˈdeɪ.ʃən/, limitation /lɪmɪˈteɪʃən/, ecosystem /ˈikoʊˌsɪstəm/, manually /ˈmænj(u)əliː/, accelerator /əkˈsɛl.əˌɹeɪ.tɚ/
Bu videoyla nasıl çalışılır
- Videonun tamamını konuşmadan bir kez dinleyin ve bilmediğiniz kelimeleri not edin.
- 0,75× hızla başlayın, cümle cümle tekrar edin ve kolaylaşınca normal hıza dönün.
- Kendinizi kaydedin ve orijinaliyle karşılaştırın; token, gotcha, compute gibi kelimelere özellikle dikkat edin.
Gölgeleme Tekniği Nedir?
Gölgeleme, başlangıçta profesyonel tercüman eğitimi için geliştirilen ve çok dilli Dr. Alexander Arguelles tarafından popüler hale getirilen, bilim destekli bir dil öğrenme tekniğidir. Yöntem basit ama güçlüdür: ana dili İngilizce olan bir sesi dinler ve hemen yüksek sesle tekrar edersiniz — konuşmacıyı 1-2 saniye gecikmeyle takip eden bir gölge gibi. Pasif dinleme veya dilbilgisi alıştırmalarının aksine, gölgeleme beyninizi ve ağız kaslarınızı gerçek konuşma kalıplarını eşzamanlı olarak işlemeye ve yeniden üretmeye zorlar. Araştırmalar, telaffuz doğruluğu, tonlama, ritim, bağlı konuşma, dinleme anlama ve konuşma akıcılığını önemli ölçüde geliştirdiğini göstermektedir — bu da onu IELTS Konuşma hazırlığı ve gerçek dünya İngilizce iletişimi için en etkili yöntemlerden biri yapar.