Hello and welcome to the session. In this session we discussed the following question which says, find the logarithm of 0.125 to the base 2. Now we know that if we are given log of the number n to the base a is equal to x, this means a to the power of x is equal to n. This is the key idea that we use for this question. Let's proceed with the solution now. We need to find the logarithm of 0.125 to the base 2. We suppose let log of 0.125 to the base 2 be equal to x. Now using the key idea we get that this implies 2 to the power of x is equal to 0.125. Let us now simplify 0.125 that is this could be written as 125 upon 1000, 25 5 times is 125 and 25 40 times is 1000 and 5 8 times is 40 so this is equal to 1 upon 8 that is we have 2 to the power of x is equal to 1 upon 8 or we can write this as 2 to the power x is equal to 1 upon 2 to the power 3 since 2 cube is 8 further we can say 2 to the power x is equal to 2 to the power minus 3. So from this we conclude that x is equal to minus 3 and we had assumed x to be the logarithm of 0.125 to the base 2. Thus we can say that log of 0.125 to the base 2 is equal to minus 3. This is our final answer. This completes the session. Hope you have understood the solution of this question.


Want to learn about stocks, cryptocurrencies, NFTs, and the metaverse? Join RichTV.io Hi, how are you doing today? My name is Rich, your host from RichTV Live with our very special guest, the CEO of Pet Vivo Holdings, Inc. John Lai, how are you doing today, John? Good, how are you, Rich? I'm doing fantastic. Very excited to have you back on the show. And I have a few questions for you. My first one, John. Since the last time we spoke, the company reported second quarter of fiscal 2023 financial results. Can you go through some of the results for us ending September 30th, 2022? Yes, I think we did. We reported 235,000 approximately, which was quite a bit of growth from the same quarter a year ago. That partially reflects some of the sales we sold to MWI through our distribution agreement. So since then, I talked about a very good sell-through ratio or inventory turn since it was our first and getting follow-up orders. It's a very good sign. It's hard to judge because there was probably pent up demand as the product hit their distribution centers. So hopefully we can keep accelerating the pace of orders, which we think we will. And then going forward, we have our American Association of Equine Practitioners Conference in San Antonio this year, starting November 18th through the 22nd. That's the largest equine practitioner event, and we should be able to make a pretty good splash there with MWI. So we've been invited into the MWI booth along with some of our literature that will be there. And then we have our own booth and we're also doing two theater type presentations on our product and the different applications by Dr. Joe Manning on Saturday and Sunday. And we have a news release to that effect so people can look at the times. I'm sure it's noon central on Saturday and noon on Sunday. So we should see a good turnout of veterinary doctors and we have a bunch of high profile vets that will be in our booth to greet people and tell them their experiences in using our product spring with us, your cushion technology. Fantastic. John, can you also tell us about the 47 states you guys have opened up accounts with veterinary clinics? What is the potential estimated revenue from these accounts? Well, it depends on time frame, but obviously when we start grabbing market share, I feel we have the ability to really address the 4.8 billion a year market here in the United States in terms of osteoarthritis and other related types of treatments. Because our product is quite disruptive and it's showing very good efficacy in the field, we're excited about that as well as the other equine vets will be talking about the product at this trade show. So right now about 90% of our revenues are in the equine space. We just completed October 30th, the ethos small animal study that should get published sometime early February, which will be a pretty major catalyst from the standpoint of getting small animal adoption. Small animal vets like to see the data, even though we know it's safe because of the fact we did 145 human study, which is a lot more stringent than on the animal side. A lot of these corporate owned clinics require an animal study, they don't care how stringent your human study was. So we feel good about that. And also, given the fact these articles are probably going to get published in some of the top veterinary magazines because that's where they're targeted. We feel the data will be quite good. And also the clinicians have asked if they can present these at major conferences like the Western Veterinary Conference out in Vegas. That's like the second week in February. It's the largest veterinary conference on the West Coast and it's actually much more oriented to small animals like dogs and cats. And then we also have a cat study that commenced, and it should be done between six and nine months that will help us launch the feline spring product. Here at Rich TV Live, we love pets and we know that people love their pets and love their animals. I want to bring your attention to the symbol, it's PETV on the NASDAQ, put on your radar and put on your watch list. John, can you go through the cost of sales for us so the viewers can get a better understanding of the financials? Well, we really can't go into detail on that because it's under certain confidentiality with MWI agreement. But I can say as we scale and hit certain volumes, we will be north of 70% gross profit margin. And we also left the good margin for the distributors. It's nothing above what is an industry normals, but it's good and they have incentives to hit certain milestones to get bonuses. And they also have to hit certain milestones every year to maintain their three year exclusivity. And in the next three years, what would you say are the revenue goals for pet vivo holding zinc? Well, there are many wildcards out there for usage. We're finding out vets or using it in applications that we didn't put out there and they're having really good success. So just like human doctors, they do off-label use and we're seeing a lot of that. So that's a very difficult wildcard to grasp. But over the next three years, I feel we have the potential of grabbing at least 5% of the addressable market. Wow. Yeah. That's impressive. Now, I know you guys had a call going through the financials in November 10th. If investors wanted to see a replay of that webcast, where would they be able to find that? Where would that be available? I think it's on our website, actually. They have to sign in and give information and then they can view it. That's one of the, I believe, one of the requirements for Reg FD. So I'm pretty sure it's there because our law firm does all that for us, so I don't really pay attention. We just make sure we comply. But I'm pretty sure that's one of the requirements. So go to PetVivo.com and go to the investor section or new section. It'll be in one of those sections. It's amazing how we don't look at our own website. It's like we don't dial our own phone numbers. And you know, it's very, very true. Now, is there anything else you would want viewers and investors watching to take away from this interview today? Well, I think it's, you know, we're in a situation where everything is towards driving sales and customer acquisition, and we have a very good reorder rate. It's never 100%, but I'm going to say we're north of 90%, which is pretty unheard of in the industry. Very impressive. It is that disruptive. I mean, literally when you do the infection, it's in a very short period of time that you see the improvement in your dog or horse. And people, it makes people feel good. It makes me feel good when I see the stories come in of how we have basically saved their animals and allow their animals to have a much better quality of life. Well, animals are family members. You know, I see people take care of their animals as much as they take care of their kids. So it's extremely important what you guys are doing, continue to do the great work you're doing. And I know it makes a huge difference for animal lovers. What is the best way for anyone who has questions, potential investors or current investors to get in touch with the company if they have any questions? I would go to the website and email the info email and then somebody will get back to them. Like if they're, if they have a pet that's looking for a vet in their area, we usually recommend them emailing us or giving us their vet information and we can contact their vet to expose them to the product and maybe they'll use it. I mean, that's our obviously our first choice, otherwise we'll refer them to a vet in the area. Fantastic. Now I must remind everyone that Rich TV Live is strictly for information and education purposes.  If you're a pet lover, you're going to love pet vivo holdings. Once again, the symbol is PETV on the NASDAQ exchange. And thank you for joining us, the CEO of pet vivo holdings Inc, John Lai. Thank you for joining us today, John. Thank you, Rich. Always a pleasure. Love to invite you back if you ever have any big breaking news or anything you want to discuss in the future. We'd love to invite you back here onto Rich TV Live. And for those of you guys that are watching at home, if you're not winning, you're probably not watching.  Thank you for watching everybody. Thank you for your time, John. And for those of you guys at home, have a nice day.


This is a really fun chance for us to dig into databases. Here's the part where I tell you, I am definitely going to post the slides on my site tonight. You're going to go check on my site tonight tomorrow, next week, next month. In about three months, you're going to get tired of pushing refresh and you'll email me and I'll promptly reply in six months and say, I'm definitely going to post them on my site tonight. And I'll never post them at all. I've ran into this scenario enough that I take the complete opposite approach. My slides are on my website right now. You can go to robrich.org and you can see these slides on my site right now. Let's go to robrich.org and click on presentations right here at the top and here's databases in the microservices world. You can get to the slides that we're about to look at right now. While you're here on my site, click on about me and you'll get to this page and this is some of the things that I've done. I'm a Microsoft MVP. I'm a friend of Redgate. I'm an evangelist for MemSQL. AZ GiveCamp is wonderful. AZ GiveCamp brings volunteer developers together with charities who otherwise couldn't afford software services. We start building software for them Friday after work. Sunday afternoon, we deliver that completed software back to the charities. Sleep is optional caffeine provided. If you're in Phoenix, come join us for the next AZ GiveCamp or if you'd like a GiveCamp in your area, reach out to me on Twitter or by email and let's get philanthropy installed in your area too. Some of the other things I've done, SQL search control basics, mine is chapter eight. That was a lot of fun to write. I worked on Gulp in version two and version three as a core contributor and one of the things that is particularly cool is .NET ROCKS. I replied to a .NET ROCKS podcast episode. They read my comment on the air and they sent me a mug. So there's my claim to fame is I have a coveted .NET ROCKS mug. So let's dig into databases in the microservices world. Talk about this guy. Application architecture. Let's start here because this is kind of fun as we look at the mechanism of microservices. When we look at microservices, we can kind of think of it in this way. We started with spaghetti. We moved to lasagna, end-tier architecture. And ultimately we ended up with ravioli microservices. I really enjoy this analogy and here's the as you grab the slides from robrich.org you can click through on this purple link to get to the article that describes this spaghetti and lasagna and ravioli mechanism. It's a really, really cool analogy. Looking at it differently, we used to have monoliths here on the left. And in that monolith, we had all of the pieces of our application. In this case, we've separated it into an end-tier application. But ultimately it's all deployed in one piece. And we deployed it in one piece because deployment was hard. We'd get on a conference call at, I don't know, through in the morning on Friday night and we'd keep deploying until Sunday afternoon when we got it right. And it was hard. It was really hard. Because it was hard, we didn't do it very often. Well, fast-forward to today and now we have containers that makes deployment really easy. It's cake to spin up a new system to get content forwarded from the Internet. Deployment is no longer hard. Because deployment is no longer hard, we can architect differently. We can architect as microservices here on the right. And so these microservices, each one is responsible for a very small piece of the application's functionality that makes the code a lot simpler. It makes the program really elegant and it makes it replaceable. If we have a change in business logic or in business need, we can swap out a microservice with a different microservice that accomplishes the task better. Or add new microservices to accomplish new tasks. And each of these microservices then can scale independently. So we don't need to have one big box or many big boxes to accomplish our website. We can have lots of little pieces horizontally scaled to accomplish the needs of our content. Now here in this, we have a user interface and the user interface will probably hit a load balancer that will then proxy off to each of the microservices or we may have microservices running on subdomains. But then the microservices as they need to reach out for data, they will reach out to other microservices to get the content from that microservices data. So as we look at microservices, they do one job. They're easy to deploy now with containers and they can scale independently of other content. If we need to scale up the shopping cart, we don't also need to scale up the login system and the monthly reporting system. They're easy to replace. And in particular, one of the tenants of microservices is that each microservice could own its own data. Now the beauty of having the microservice own its own data is that if that data needs to evolve, if that microservice needs to get replaced or adjusted, then there's no concept of, well, who owns this piece of data and can I change it and do I need to get all of the approval processes? That microservice owns that data and as the microservice evolves, the data can evolve too. Well, we set out to build microservices and we want them to own their own data but usually we fall back into a pattern like this where we have one monolithic database. That database that we used last year or 10 years ago or a generation ago and we have our microservices but they all talk to the same database. Bummer. Well, why do we do this? Well, well, there's a lot of reasons, but provisioning the database is still hard. There's a lot of content in there and when we lose a database, then that's traumatic to the organization. We've just lost data. We've lost customer records. If we lose a web server, yeah, we can prop up a web server. The website may be down for a little bit. Said another way, what will make the news is losing the database. It won't make the news when you lose a web server. So databases are hard. Ultimately, the DBA's job is to make that database secure and accessible. And so ultimately, the DBA is likely going to say no when we say, hey, can I have another one? Yeah. DBA's job is to enforce safety within a data structure. So it kind of makes sense. I'd hope that we could get a DBA to focus less on guarding and more on facilitating in a safe way. But that's another conversation. So ultimately, the DBA is pushing back. He doesn't want too many little databases because did we remember to secure it? Are they all backed up? It's really easy when we have lots of little databases to forget that basic database hygiene. I guess we didn't back up that database and completely lost all of our customer data. That's not something that we want to hear. So to avoid that, we don't create these little databases. We have one monolithic database. So now what do we do about it? Well, if we must use a monolithic database, we can treat it like lots of little databases with bounded contexts. And a bounded context is a methodology within an application to be able to carve out that portion of the database that is necessary for this particular task of work. When we think about a bounded context, we could look at a shopping cart. Well, a shopping cart needs access to the product catalog. The shopping cart experience probably owns the product catalog portion of the database. All of the product descriptions and all of the pictures, but they don't need to own the customer record. They don't need to own HR data. They only need to own the product catalog and the shopping cart, the orders produced. By comparison, the fulfillment system doesn't need to worry about what is the product description and the pictures at all. The fulfillment system's goal is to ensure that the content is delivered, ensure that it's shipped. So they own things like the shipping address and the order status, updating the order status to mark that it is shipped. They may own some billing details. Was it billed or perhaps that's a separate system, the billing system that owns credit card information and an order's payment status. A bounded context allows us to carve up our monolithic database into lots of little pieces. And now we can treat those little pieces as if they were, let's claim the term, micro databases. Now in time, hopefully we can get to the concept of micro databases, lots of little databases where each, hopefully we can get to micro databases where each microservice owns its own database. But in the shortest term, a bounded context is a great way to go. How do we get then to micro databases? Well, here's that micro database mechanism where each microservice owns its own data. Now we still have those two concerns about who owns the backup and who owns the authentication story. So to get to micro databases, we need to do interesting things like a mechanism to enroll a database in an authentication and backup strategy. We need that automation. When we have that automation, we can move to micro databases. We can get to that point where we have lots of little databases that accomplish lots of tasks. A micro database, it's owned by one microservice and other microservices that need to get data in this database will call that microservice to get or change data within this database. So how do we do joins in a micro database? Well, this database may publish events to a service bus. Now a service bus is really interesting. A service bus like Kafka or a similar type of thing is a mechanism for communicating between disconnected systems. The beauty of a service bus is that the publishers can publish content into that service bus and they don't need to get that data consumed right away. Simultaneously, a consumer can grab data out of that database whenever they're ready and might even be able to back up and reread some of the data out of that service bus. So if we have a service bus between our microservices, then those things that need to can publish events into the service bus and those things that need other data can subscribe to the event in the service bus. We call this event sourcing where we'll publish events into an event store and then we'll consume those from another store. So how do we do joins? Well, we listen to the events published by that other microservice and we keep track of the content that we care about. At that point, when we're ready to join, we own all of the data that we need to join on. So here's another good example. So who owns the customer record? Well, if we're in micro databases where each event where each microservice owns its own data, well, do all of them own the customer record or do we go back to a monolithic database where the customer record is one big piece? So for example, should the shopping cart own the customer record? They're responsible for creating an order based on a customer. They'll likely verify the shipping address or should billing own it they're the ones that need to own the credit card information or should human resources? Human resources has lots of well, secret data about the user and it needs to be kept within HR's boundaries or should sales own it? They're probably the first person that saw the customer but in sales, there's very different validation requirements. For example, a lead or a prospect, they may only have a name and a phone number. They may not have a company address but for order entry, we need to have a full shipping address. We probably need a phone number. So if sales is responsible for the customer record, do we enforce sales to enter in all of the data before we begin? Well, now they're starting to use, I don't know, notepad or Excel to keep track of their leads and duplicate databases is always fun. So who owns the customer record? Well, we probably want to create a new microservice that is the customer record and that customer record would only be the primary key and the account number, maybe the company name but after that, all of the rest of this data is specific to each microservice that needs it. So the shopping cart can call into the customer microservice and say, hey, I've got this customer ID, is this a valid customer ID or I've got this account number, is this a real account number? The billing can go ask the customer microservice, hey, I've got this credit card detail, is this customer still active? Can I still create this purchase for them? Human Resources has all of their data within their Human Resources microservice but the customer ID is still in the customer microservice. Because we have this mechanism where each microservice owns its own data, then each one can define what customer means to them. To the customer microservice, a customer is merely an auto-incrementing primary key and an account number. Everything else is stored somewhere else. Sales has their microservice that has a foreign key off to this customer microservice and they have all of the lead and prospect information. Shopping cart has all of their shopping information, all of the order history and billing has all the credit card details. We've created a schema where we have microservices that have maybe overlapping-bounded contexts but each of them still owns their own data with no overlap into any other data. Using event sourcing, we can get different data to different microservices or if the billing service does need to call out to the customer microservice, then that's totally fine. So micro databases, we've reached it. We've gotten to the point where each microservice owns its own data. We have enough automation to be able to enroll these databases in authentication and backup strategies as soon as they're created. So we know we still have that data safety but we also have a user experience, an architected system that will scale horizontally rather than needing to scale vertically with very large database servers. We've gotten to, let's coin the term again, micro databases. This is really cool. Microservices require micro databases. If we have the automation to get there, then it can be really, really cool. Well, the other thing that's really interesting here now is when we have micro databases, each database doesn't need to be the same kind of database anymore. That's pretty cool. So some microservices may be able to store data in a form that is better suited to that database. So let's take a look at that. I love this quote. They don't all have to be the same database vendor or the same database type said by me. Right now. Okay. So because they all don't all have to be the same type, let's look at some of the database types that we now are able to choose from. Well, relational, a SQL database, a document database or a no SQL database, a graph database, key value, time series, search, object or blob store, file store, column store. And let me introduce you to new SQL databases as well. So first up, with each of these, we'll look at the characteristics, the pros and cons, best use cases for this particular database type, and some example databases that will get you to Google search terms. I'm definitely not pitching a particular database in this section, but when we look at examples, that will help you to be able to Google it. Okay. First up, relational databases. What I love about relational databases is it's a great default. It's a really easy mechanism to understand. In a relational database, we have rows and columns. You can think of this like an Excel spreadsheet. With a spreadsheet, we have rows and columns. One of the big defining characteristics then of this is that each column has exactly the same kind of data in it. I'll have a state column in that column. I'll list each state or province. I'll have a phone number column, and I expect that to only have phone numbers. I'll have a created date column, and I'll expect all of the values in that column to be dates. One of the things that makes it a relational database is the SQL engine. And this SQL engine uses English-ish like queries, and so we can kind of get those SQL queries. This SQL engine has been tuned for generations to be really compatible and really efficient. So as we look at relational databases, we know that they're going to be pretty fast and pretty easy to use because they have this consistent SQL experience. One of the things that's particularly cool about relational databases is ACID compliance. Let's take a look at ACID compliance. Atomic, consistent, isolated, and durable. Now I love this talk here where they talk about all of the particulars about ACID compliance, and I'd invite you as you grab the slides from roberts.org to click through this link and read this article. ACID compliance suggests that all of the data either succeeds or fails together. We're not going to get part of the update through and part of it not. We're not going to lose some of the data. All of it either gets through or all of it doesn't. By comparison, we have eventual consistency. In eventual consistency, we'll push it out and in time it will get to be okay. But if we save and then quickly read, we may get the old data. An example of an eventually consistent system is ordering something at Starbucks.  Once they finish preparing the thing, they'll come back and they'll give you your order. A similar thing is like a bank statement. If I go to my bank right now, I may or may not have the charge that I just made at Starbucks. But at the end of the month, I'll have all of the details of my statement there. Is eventual consistency sufficient or do I need asset compliance? That's definitely a question for your organization and probably for your microservice. Does this microservice need eventual consistency or asset compliance? So here's those details of a SQL database, rows and columns, one big server, tables of consistent rows and columns, asset compliance and SQL queries. One big server. So typically because database engines were built before multi-threaded CPUs were a thing, most SQL database engines scale vertically. I can't really put two machines together and tell them you're one database. That's kind of a bummer because to scale up, I just have to keep buying bigger and bigger boxes. Eventually, I may get to the point where I run out of box or I run out of money to buy that box. Relational database is usually being one big server. That could be a pretty big bottleneck. So next up, oh, so here's relational databases. Their strengths are in a strong schema. I can do table joins. I have that very fast optimized engine. I have that compatible familiar syntax. On the downside, I must scale up that one big box and ultimately, I probably will normalize my data to third normal form to be able to reduce redundancy in my data and ultimately to reduce database storage. To that end, I may choose to use an ORM to be able to get at this data easier, an object relational mapper that does that heavy lifting of turning my objects and my application into rows and columns in my database and back. It'll take my code-based queries and turn them into SQL queries and return those results. So we've got relational databases. Ultimately, the best use case for relational database is probably a good default choice. If you're in a monolithic database scenario, your database is probably a relational database. Here's some examples of relational databases that'll get you past that Google search. Next up, let's look at no SQL databases or document databases. I'm going to call them document databases because pretty much anything that isn't a relational database is technically a no SQL database because it's not using that relational SQL. But in particular, when we say no SQL, probably what we mean is a document database. Now, the beauty of a document database is that I can take this JSON blob and I can stick it in my database as one piece of data. Instead of tables, we'll probably talk about collections and we probably mean the same thing. The interesting thing about these JSON documents though is that they don't all have to be the same. Each document can include or exclude any fields that they need to and I can nest content within this record. They're ultimately optimized for reads and so I may choose a schema that is, well, a little bit redundant to ensure that I can read really quickly. A great example might be a news site where I need to read everything about that news article in one shot. So I'll put the author name, I'll put the author's details, I'll put the title and body content and I may put the first two comments in this single document. So that I read by ID, I get back that entire document and I render that page in one go. Now, most document databases are distributed across many machines. That means I can use commodity hardware there and so I don't need to scale up to one big object or one big machine. That is wonderful. But because of that, they'll set aside asset compliance for eventual consistency. Is it committed when I only write it to one machine or is it committed when I've distributed that to all of the machines? Do I need to wait for quorum across all of the cluster to be able to decide that that data is committed? Different NoSQL databases here will make different choices there, but ultimately that's kind of the major difference here. SQL databases are one big box and NoSQL databases are JSON documents on many boxes. So when I'm looking at a document database, it is distributed. I have no schema, which means that I don't need to care for it. I don't need to migrate it. On the downside, I have no schema, which means my application probably needs to enforce different things. Like my database probably does have a schema in the mind of the architect who built it, but now I'm just not telling the database about it. So now I have to do things like, well, what if I read data that doesn't exist? What if I'm trying to do a where query, a filter on a field that doesn't exist? So no schema can kind of be a two-edged sword there. Ultimately, because I'm outside of SQL, we may kind of try to layer a SQL-like language on top of a document database, but I'm outside SQL. I don't have that really, really fast tuned engine. So we end up with SDKs for each language, and that SDK may have JSON-like query syntax, but it's not SQL. Or maybe it is SQL, but now how do I talk about nested things? That can get a little bit weird in document database. One of the big downsides to document databases is each collection is its own island. There isn't really a concept of joins in a document database. Now maybe that's okay, but the way we avoid joins is we denormalize our data. We have duplicate copies. That does make writes more expensive because I need to update all of the copies. But again, in document databases, we're optimizing for reads. So here's some examples of document databases. Document databases might be a great use case for a product catalog or a news site, a CMS, something where I need to get the entirety of the thing really quickly. And the fact that I'm storing it in a little bit of a weird scenario or I don't have ACID compliance straight away is okay. So next up, let's look at graph databases. Graph databases are really cool in that they focus not only on the things, but on the relationship between the things. Here's an example of query into a graph database that focuses on the relations between people and the company they work for. Now I can do things like friends and friends of friends that really easily in graph databases and not only that relationship, but I can hang data off that relationship as well. Relationships then are directional, which is pretty cool. So graph database, how is that different from a SQL table with two foreign keys? Well, yeah, I could kind of start to fake a graph database with SQL, but it's that query syntax that makes it really elegant. The concept of being able to join across those relationships in a really elegant way. And so to that end, graph databases are really tuned for those relationships. So on the upside, it's really fast to be able to query into those relationships to keep track of those relationships. On the downside, it's made that choice at the expense of other things. Now it may do those other things okay, but its focus is on the relationship. So if you need asset compliance or you need really tight SQL or you need joins, then a graph database may not be able to perform as well as SQL database in that context. So a graph database, here's some examples. Graph databases are great for social networks because social networks really focus on those relationships. Messaging apps where I need to keep track of all the messages between people. Maybe a recommendation engine might work for a graph database because it's keeping track of relationships between products. Next up, key value databases. Now a key value database is really cool in that given a key, go read a blob of data. Often the data is JSON or a big text blob, although ultimately what's interesting about key value databases is the value is anything. We could think of this as a SQL table with a primary key and a binary blob value, those two columns. So whatever I choose to write in that binary blob is kind of irrelevant to the key value database. I just write it based on that key and when I want it back I go ask for that key and get that value back. This is really great for those key value type of experiences, those by ID type of experiences. These can be great for caching servers if we need it. I can store different data in each of the values because to the database it's just a binary blob. On the downside, I can't really query for anything but ID. If I need to do a where clause on content within the value, for the most part I have to read all the keys, deserialize that data in whatever way I make sense for what data I wrote there, and then look at that data and see if it matches. Not really an efficient way really an efficient mechanism. If we need that kind of query into the related data we probably want to lean on a different type of database. But for those paces where I really need to search by ID and I need to match on that ID really quickly and just get that data straight back out key value database can be perfect. So key value databases here's some examples of some key value databases. They're best used for caching, for configuration, user session data. Anything that is that big binary blob of stuff that I just need to access based on a key. Next up, time series databases. Isn't it cool that we have these micro databases so that we can explore all of the different types? Here in a time series database we're looking at different portions of time. We'll create time windows, time buckets. Now what's interesting if I think of an Excel spreadsheet I assume that there's only one piece of data in each cell in that spreadsheet. Well in a time series database I can create a window that is a week or a month or a day or an hour. And in that cell I have all of the data that matches. What's then cool is I can query into that window to get interesting things like min and max and average and count and that can be some really cool analytics to be able to create those windows, understand the quantity of content within those windows and to start to search for trends within those time windows. Time series databases are great when I have very time specific data. Perhaps I have sensor data coming from a mobile network or another type of time series data where I have lots of events that need to jump into my data store. It's not great when I have more generic data because well since we've set aside that SQL engine it's really tuned for this time series thing not for all the other things. Here's some examples of time series databases and it's really great for that IoT monitoring or real-time events. I might choose to grab real-time events for mobile devices or sensors maybe stock trades or click streams. Those are great for time series databases when I need to know did that event happen within that time window what was the min and max or the quantity of events within that time window when I need to handle that data in bulk in that way time series can be great. A text search database we can think of this like Google or an Elk stack. A text search databases focus is on finding matches within a document. Given a text document we'll go pre-catalog all of the pieces we'll identify all of the words we'll be able to then reach into this with an inverted index to be able to identify matches within that document. I'll type a query it will go find a match and it will show me which documents or where in that document is the match for my query term. Now in time we may pair this with a natural language processing engine so that I can ask it questions more so than just search for terms and that's a really good line extension for this. The text search database itself isn't that natural language processing engine but pairing those two is a really elegant solution. Text search databases are great when I'm trying to do that find the documents that contain this thing or find the spots within this document that need this thing. It's not great for joining on things it's not great for relationships between things because it's really focused on that finding the content within the document. One of the things that's a major barrier here is stop words. In English the words a and and on you know those words that don't really have meaning on their own so I may choose to eliminate all of these stop words from my index so that my index is significantly smaller. Now this is very language and culture specific so if I have an English set of stop words and I'm storing data in other languages then those stop words don't really make sense or maybe a word in English is a stop word but not in another language. A great example is I may choose to not index a because you know a by itself has no meaning but what about when I talk about the street avenue a? Well now the a is really important did I create a stop word that was a that just completely dropped it and now all that I've indexed in the street name is avenue. Did I shorten avenue to av and add that as a stop word because you know av usually doesn't have anything at that point this record has well nothing to match on stop words are really fragile within a text search database. The text search databases are great when I need to go find the content within a document. Here's some examples of them. It's great for matching documents or matching content within a document. One thing that's interesting about Splunk is that Splunk will actually put in the schema later so I can just quickly ingest all the data and then I can analyze it later and put in a schema so that I can start to wear on different columns or content. Next up an object store or a blob store or we could think of this as you know a file system. I'm not going to say file system because you know that has a very specific connotation. S3 isn't a file system or is it? The key is a path into that document and the value is well whatever I choose to put there it's really great to think of it as a file system. You know at that point I may not have folders per se but I do have documents and I may choose to create a naming convention that nests them in these simulated folders. That's pretty cool. These object blob stores are great for very large things for videos for file backups system backups. They're not great for when I need to query inside those big things. If I want to do a where on an object store I basically need to open up each document deserialize it in whatever way makes sense for that and then search that document using a different mechanism. So maybe I open up each backup and now I need to unzip it into place and then or maybe I open up each media file and I go looking for images within each frame or now you can see that where's an object stores are pretty fragile. So object stores are great when we need to store big things. We probably don't want to put a database backup or a video file in a cell in a relational table. Suddenly our database is you know two or three five times the size but we do want to upload this to maybe our NFS file share or to another blob store where we can keep that object as an object just as a binary blob in a lot cheaper mechanism for storage. Here's some examples of object stores. Next up column stores. Now column stores are really innovative adaptation to SQL databases where instead of storing the data in rows the door this data is stored in columns. So in a traditional system here on the left I would have all my rows and each row would have the entirety of the data associated with that piece of that row. In a column store I'm going to create a different type of index. I'm going to create all of that data in columns. Now using an example of you know countries in the world or maybe states within the country there's very few and so I can get a really good compression on this. If I'm looking for all of the states within the country there's you know maybe a dozen or a few dozen and I can make a couple of entries in this thing and it becomes a really well compressed index. Now that's really cool for doing analytics because I can reach into that column store and I can pull out the values that I need. I can quickly get to that section and grab all of those values. Column stores are great for faster seeks and for bulk reads. By comparison row stores are usually better if I'm doing transactional data where I need to do lots of random reads and writes because lots of random reads and writes aren't really the job of column stores. Here's some examples of some column stores. Ultimately column stores are great for analytics. If I need to do analytics on data bulk reading and bulk analysis column store can be a really good way to store that data efficiently and query it quickly. So next up let's talk about new SQL databases. A new SQL database is a new type of database that focuses not just on SQL and ACID compliance and all of those pieces but also adds with it distributed. One of the big barriers of SQL databases was they were architected when one machine, one thread was the name of the game. So with a new SQL database we may choose to in this case we're looking at a mem SQL architecture. We have a few aggregator nodes. We'll submit our queries to one of these aggregator nodes and it will go reach into leaf nodes to get the data out of the partitions. So the data is partitioned into lots of different commodity hardware and so now to add more capacity to our database we just add more leaf nodes and more aggregator nodes. At that point now we can get much more performance from a larger database without having to buy yet another big box. We can think of this as SQL with an SLA. Whatever you have the spinning wheel where you're waiting for that query to respond a new SQL database is probably the right choice. New SQL databases they are much cheaper to scale. On the downside because my data is stored in partitions to do something like a join may require data moving across the network. Now normally all of these nodes are going to be close to each other so that that network latency isn't a major bottleneck but that's one thing to think of when we're looking at a new SQL database. So new SQL databases here's some examples. New SQL databases are great when I've outgrown my SQL database when I just can't buy a bigger box or when I need to store more data or when I get that spinning wheel of doom where my queries are just taking too long I can get to that spot where I can partition my data across databases partition my data across nodes and be able to get at that content in a very multi-threaded mechanism. So one of the things that's particularly cool then is a lot of databases are looking less towards being a specific type and more towards being a hybrid database. I'm primarily this database but I'm also going to do that as well. So for example a SQL database that adds JSON types or a time series window or adding time series window functions to a relational database or adding a two column relational table to create a key value store within a relational database. We can see how we can take a regular database and kind of make it work in other environments with just a few tweaks. A JSON data type in the database is a great example here because I can create what is effectively a no SQL database embedded in a column of a SQL database. Now I still get joins across all of my relations but I may still have interesting query syntax into that JSON block. I can treat it not just like a big string in a column but like JSON data that can be really really cool. So as we look at this as you're grabbing your camera to take a screenshot of the picks this is a great one to grab. Here's a review of all of the different types of databases that we talked about today. One line two line description of that database and when you would want to choose that database as the database for this microservice. Now keep in mind because we're using microservices I can choose lots of different databases to accomplish the task provided that I enroll each database in that mechanism for getting the authentication and the backup in place and at that point then my microservice owns that data. Relational data, document data, graph data, key value data, time series data, text search data we can find the correct data store that matches these. So here's our architectures. We looked at that monolithic database underneath our microservices and if we must go there a bounded context is a really elegant way to solve this. Create a bounded context that outlines that portion of the data that responds to that is that this microservice owns and that bounded context can keep track of that portion of the database. As we get to that level of automation each microservice can own its own data store and we can get to that spot here on the right where maybe they're different databases in the same database server or maybe they're completely different database servers to keep those data sources very isolated.  When we get to that level of automation then it's really easy to upgrade that database where we don't have to go check with all of the other people that might be reading and writing to this because we know that this microservice is the only one tagged with this data. Micro databases we looked at each of these types of databases and the beauty here is that we don't need to choose only one. We can choose the one that best serves the task. Another magic question well how do we choose? Here's the other slide as you grab your camera grab this slide too or head out to robrich.org and grab these slides there and you can get this content straight away. Here's my recommendations and also recommendations as advised by Gartner. Ideally you would use a database as a service. Now a database as a service is great because they'll keep track of all of the backup and restore pieces all of the user authentication pieces, all of the uptime pieces. Never get woken up again if your database goes offline. You've outsourced that to your database as a service provider. Use automation if you're using more than one database server because that automation can ensure that you have consistent backups consistent restores consistent user authentication rolling secrets whatever you need that automation can provide that. By default choose a SQL database. SQL databases have that asset compliance. They're compatible. You can get a really good generic and general mechanism built into a SQL database. And then once you've got that solved for the other microservices where different data is best served by a different data store then grab a different data store that exactly matches those needs. Now you may choose a different database type based on the needs of that data and ensure that it's a really good fit because you are by choosing that different data store sacrificing more generic things. Now you've specifically chosen that because you've specifically carved out that portion of the data that's needed for this microservice or perhaps a microservice needs more than one type of data store to be able to store different types of data. This is a lot of fun to review all of the different types of data stores and in particular to review mechanisms of getting from microservice from monolithic databases to micro databases. What are your questions? Well, first of all, thank you for that because that was really awesome to get that overview and to lead up into the MemSQL story and the new SQL databases. I think that was a really nice thing. But we've got one question coming in from the YouTube feed and you may have already covered it but just to repeat, what are the downsides to using new SQL over SQL? The overhead network latency and performance? Yeah, definitely great question. When we looked at new SQL, a new SQL is that SQL engine but not constrained to one box. And so we get a whole lot of benefits of a cheaper way to deploy. We can just add more commodity hardware to get more capacity to our database. On the downside that one concern there is potentially network latency. We do need to coordinate between multiple boxes to do this. Now, arguably in our SQL databases, we're kind of doing similar things as we're trying to negotiate between threads or between simultaneous queries. So in a new SQL database, we'll likely get better performance in spite of that potential network coordination efforts. And so I think you did a good job answering the question earlier. So if you go back to your recommendation slide just for a second, if you could just pass back here. One of the things that is interesting to me is this new database as a service. And as an old school person, I should date myself with being a DBA for SQL service, SQL database back in the dark ages. So I think the emergence of database as a service and how it simplifies things for developers. So being able to attach to the service and let the service provider manage and the distribution of the service and the security and all that. Can you talk a little bit more about how that has changed things? Yeah, definitely. When we look at databases as a service, that's like one of the big things is, well, knowing that the data is my critical piece, I really don't want to let go of that. I really want to own that ownership of my data. And I put it in a cloud and feel okay. Well, when we look at just comparing, I don't know, our office building versus a co-location facility, as we move to a co-location facility, we've chosen to outsource providing electricity and building security under the premise that that co-location facility is better at that. As we move then from a co-location facility to a cloud, our clouds, our public clouds are going to be much better at physical barrier security. They're going to be great at that. We've also chosen then to also outsource backup and restore. That's one of the things that usually bites us is, well, was last week's last night's backup okay? Most cloud database vendors will allow us to continuously back up. So, you know, there isn't a big burden of time where we need to stop all the things and back up. And also, it's point in time restore, maybe point in time restore to a parallel spot where we can now diff the data that we had together with the data that we used to have and find any abnormalities. Those are great capacities with a cloud database. Additionally, most of them offer, you know, lots of nines uptime at which point restore based on hardware failure is no longer a thing. I worked for a company where their server room was in the same room as the water main and the air conditioner. And everything else that wasn't people. And there was one interesting morning where they learned that the air conditioning vent had nicely leaked into the top of the server rack. Oops, that doesn't happen in a cloud database vendors mechanism because they're keeping track of all of those pieces in really elegant ways. Now, it does mean you need to let go, but I would argue as you think through those types of things, the things that you've chosen to outsource, you can now focus on solving the business needs that you have, not on solving for little things that you don't care about. Definitely. I've been in those server rooms and seen that that scenario play out badly as well. So there's another question coming in for regular applications that are ported into cloud environments. Which database would you recommend? He's currently running Oracle aiming for Postgres for licensing reasons mostly, but is there anything you would personally recommend? This is one of the touchy ones. As an advocate for MemSQL, I need to show you how easy it is to get started with MemSQL on Red Hat. If I go to the Red Hat marketplace, I can just type in MemSQL, and here's that MemSQL database. From here, I just click Go and provision that database into place, and it'll keep track of MemSQL, that new SQL database across all of the pieces so that all of the pods in my Kubernetes cluster, in my OpenShift cluster are accounted for. And that's really cool. It's really, really easy to get started there. Ultimately, which would I choose? I would argue, what is the perfect size coffee mug? There you go. I think the other thing is it's matching it to your use case as well. I think that really makes a thing. There's no generic answer for this. I definitely will have you back, and we can talk about some of the MemSQL use cases for ML and AI and all kinds of other things that I've seen you guys doing work on. But it really depends on what you're putting in that coffee mug. And which side the handle's on, or how many or no handle, what it is. I mean, it's really about the use case. And so there's not really a good one-off answer. But I do think easily to be deployed, easy to maintain scales well. Those kinds of considerations are part of the first order of things that I look for. And definitely, we'll have you back to do a deep dive on MemSQL sometime soon. And I really appreciate you taking the time today to answer these questions and to give us this tour de force on databases today. That's definitely. This has been a lot of fun. I definitely learned a lot. I think one of the things for me is there are so many choices, and there are so many different use cases that it's really nice to see them all laid out this way. And so thank you very much. This is a wonderful briefing, and I can't wait to have you back again. So thanks, Ron. My users have other questions that we didn't answer here. Reach out to me on Twitter at Rob underscore rich. Grab these slides right now at robrich.org. And let's continue this conversation because this was a lot of fun sharing this knowledge with you. I'd love to continue learning with you too. All right. We're going to definitely get you back on. This was very, very useful. So thanks a lot. I'm so glad. Take care, everybody, and we'll talk to you all soon, probably tomorrow. So all good. Thank you, everyone.


Join me and my roommates, Dr. Ed, Nurse Natasha, Gary Gray, and Hans the Butler as we talk about all things horror.  A kid's movie? I thought it was a documentary on Genital Lice!  They don't make them like they used to! No, they don't make them like they used to, and that's what inspired this episode. But first, a little backstory. See, I told ya, nostalgia bullshit! Actually, no, Little Monsters wasn't one of those movies that I had on repeat on the VCR growing up as a kid. I liked the movie and all, but it wasn't one of my favorites growing up. And actually, I totally forgot about it until recently. Is this the one where Fred Savage kidnaps his brother and forces him to play video games? No, actually, that's Wizard. California! And a while back, I actually decided to watch Wizard because I hadn't seen it since the 90's. And I haven't seen a paycheck since the 90's! And do you think Wizard stood the test of time? Not at all, it aged very poorly. Anything with both bridges aged poorly. So, after Wizard falling flat on its ass, I got to wondering, how does the other 1989 movie starring Fred Savage, Little Monsters, hold up? He did two movies and a TV show in one year? Sounds like child slavery to me! I was expecting it to be even worse than Wizard and was I dead wrong? It actually holds up very well. They don't make them like they used to! After the movie, I came away thinking, man, they just don't make kids movies with balls anymore! Kids movies with balls? I don't know what you're watching, but I think it's illegal!  They don't make them like...  So that's why we're talking about Little Monsters. It's a forgotten movie, which is a great example of how children's movies of the 80's seamlessly combined adventure, horror, scares, dark humor and values seamlessly. And at the end, let's ask the question, should they still make movies like this today? YES! For those of you who haven't seen the movie and a recap for us 80's and 90's kids, Little Monsters starts off with the Stevenson family moving into a new neighborhood and a new house. It's a real fuckin' dump! It's a fixer-upper. Our main character, Brian, played by Fred Savage, hasn't made any new friends at school yet, but he has made an enemy of the school bully, Ronnie. A red-headed bully? How cliche! You might also remember Devon Rattray as Buzz and Homerloon! Strange things start happening around the house. Brian's dad finds melted ice cream in the cupboard. He then backs over Brian's bike, which has been mysteriously left behind in the garage. Of course, he blames all this on Brian. He knows Brian was up late watching TV because he left his peanut butter and onion sandwich behind! Yeah! Peanut butter and onion sandwich?  You know, I've always wondered how that would taste. Hans, whip me up a peanut butter and onion sandwich. Yes, coming right up! And now I will show you how to make a peanut butter and onion sandwich! You put the peanut butter on the bread, then you cut up the onions, and put them on the peanut butter, which is on the bread, and you have yourself a peanut butter and onion sandwich! Thank you. Geez. When heavy on the onions, it's actually not all that bad. Don't expect a kiss-good night from me! Brian's younger brother, Eric, played by Fred Savage's real brother, is convinced that there's a monster under his bed.  Needless to say, he didn't make it the night and slept on the hide-a-bed.  I remember hide-a-beds. I was sleeping on one and Ed folded me up into the couch. My body's never been the same. Now convinced that there is indeed a monster under Eric's bed, Brian devises a plan to trap the monster. He saws the legs off the bed and uses parts from his broken bike to construct a breakaway bed. Every kid in the 80s is a mechanic or a genius inventor. That night, Brian catches the monster and finds out that light actually melts it when his dad opens the door to the bedroom to see what the raucous was. Brian instantly befriends the monster whose name is Maurice. Maurice takes Brian down to the underworld through the portal underneath the bed.  It's a monster mash never, never land on steroids. When Brian gets back to the real world, he's faced with some real-world problems. His parents are getting separated. And Maurice is having his own problems in the underworld. Problems is right. I think this guy has Tourette's or something. We are then introduced to Snick, a huge loudmouth, chain-smoking monster who implies that Maurice was actually supposed to lure Brian to the underworld and keep him there so that eventually he'll turn into a monster himself. I always seem to fall for the tall loudmouth type. Maybe because they always have such a big... Buzzer But Brian's already turning into a monster which he finds out when he's exposed to the light and his arm melts. In order to trick Brian back to the underworld, Snick kidnaps his younger brother, Eric, knowing Brian will go after him. And they play Super Mario Bros. 3 in California? California! Brian then gathers up Eric's best friend, his new love interest, and even the school bully to mount a rescue.  They couldn't think of a better name for the character than Boy? They become journeyman electricians and rig up these big huge light suits to defeat Boy and rescue Eric. But all the portals home are closing and they're running out of time to find one before they're trapped there and turn into little monsters forever. What? They don't make it back home? Of course they do, but not before Brian and Maurice have a real heartfelt goodbye and then they take the last portal home. A bum's caught on the beach? I guess it's a bed. I've passed out worse places. The movie sounds pretty fun and charming, right? Well, it is, but it's also dark as hell. So let's take a bit of a deeper look. Ooh! I'll go get my endoscope! First thing you'll notice is how creepy the music is right from the opening credits. If you were to judge the movie on that alone, you would assume it was a horror movie. Or a cheap Danny Elfman ripoff! The acting in this movie is fantastic and I totally forgot how good friend Savage was. He's solid. All the kids in this movie are solid and nothing wrecks the movie more than bad child acting. And this is where we make a reference to the Shining Miniseries.  Seeing Daniel Stern in a serious role is really refreshing. He does a great job of playing the hard-ass, tough-loved dad. He also did the voiceover for Fred Savage's older self in the Wonder Years. Let's not forget Howie Mandel's magnificent performance as Maurice! This was probably a lot of kids' first introduction to Canadian comedian Howie Mandel who plays Maurice. This has to be the most obnoxious performance ever! Who's now famous for being a judge on America's Got Talent. He also voiced Gizmo in Gremlins and created and voiced the TV show Bobby's World. Imagine what would happen if you got Robin Williams and Howie Mandel in the same room with the 8-Ball of Coke in the 80s? The Earth would have been plowed into a black hole! Now let's talk about some of the messed-up things in this movie that you just don't see in kids' movies anymore. All the pranks they pull would have given kids some nasty ideas!  Heh heh heh heh heh. Brian bans expensive golf clubs over Maurice's head and they put peanut butter all over the receiver of a telephone. Imagine if you pulled that prank today with a cell phone! You'd probably get curb stomped.   Monster piss isn't half as bad as it sounds. All that stuff is beyond harmless pranks. It's funny, yeah, but it's downright cruel. This movie really does encourage anarchy.     The movie also suggests that a 12-year-old kid staying up late watching a Playboy channel is an acceptable pastime. These kids have awful party mouths! Back in the day when kids not even in their teens yet would curse multiple times in a movie. They don't drop any of the big ones, but what's an 80s kid's adventure movie without some strategically placed S-bombs?  And let's not forget the big line of the movie. Who puts pits in my apple juice? Eh, eh, classic. And the crass humor doesn't end there.  If he has a nice tushy, then he has a nice tushy. There's also some morbid moments like when Maurice is melting and begging for help. It's creepy!  Then at the end, Maurice torches Snick with a flamethrower.  And the final act of this movie gets really creepy thanks to the off-putting performance by Frank Whaley as Boy. Brian's walk-up, these creepy flight of stairs to confront Boy is actually really scary.  The effects are very, very good. They are essentially 80s horror practical effects. The monster designs of Maurice, Snick and Boy are great and kind of terrifying. But all the other monsters in the underworld... not so great. It just looks like they raided Mr. Dress-Up's tickle trunk.  If they are as heavy as mine, then that's a good description. Another thing that makes this movie really strange is almost every character is a hothead with a really mean streak. Brian and his dad are constantly bickering and screaming at each other. Brian's parents are fighting behind closed doors.   None of these characters should be likeable, but for some reason you do like them because they are relatable and the acting is so realistic. The ending of this movie is sad and depressing. And it doesn't end on a very positive note. Brian has to say goodbye to his best and only friend. It's really sad and Howie Mandel does a great job of dialing back his performance for one moment in the movie where he delivers this really heartfelt goodbye. There's also some seriously dark subject matter in this movie. Right up my alley. This movie deals with loneliness, divorce, bullying and tyranny. And if you want to dig deeper, I think it gets even darker. The underworld could be seen as one big metaphor for addiction. Brian becomes addicted to the underworld and the carefree lifestyle that he can have there. So much so that he begins turning into a monster. The same that anyone else does when they become dependent on drugs, alcohol, gambling or any other vice. If that was me, I would have stayed there. Sounds like a great time. So lastly, let's analyze the name little monsters. But there aren't any little monsters. Exactly. So who are they referring to? It's not Maurice. It's not Snick. It's not boy. They are referring to the nasty kids. Yes, that's my thought. Exactly.  Let's face it.  All of us at some time in our childhood have been little monsters. But if we're lucky, we smarten up and learn from our mistakes.    It's okay to have a Sorian brandy and collapse a black hole every once in a while, but you need to know when to draw the line. So underneath this strange at times frightening, morbid kids monster movie, there's some great morals and real life issues that still matter today. So even though it's a product of its time, it still stands the test of time. I think it's a wonderful little movie. It delivers some laughs and scares and takes us to some real dark places, which is why we're talking about it today. They don't make them like they used to. And I'll leave you guys with this question. Should they still make them like this? Kids movies with violence and cursing that encourages the destruction of personal and public property or are movies like Little Monster's best left to the 80s? Any movie that expands a child's mind is a good thing. If you did not get into Miss Jeff and Adventure as a kid, then you're missing out. And this movie encourages Miss Jeff and Adventure.  I want to see a kid's movie where they shoot up and smoke crack. They don't make them like they used to, but they don't need to. We can always go back and watch them all the oldies. My thoughts exactly.


Welcome to Access to Trader, the number one community for those who are committed to taking control of their trading in order to achieve success, profitability, and longevity. Thank you for joining us. Here's Dan Shapiro to help you find your edge, master your process, and own your future. Hey guys, good morning everybody. Welcome to another weekend update show of theaccessotrader.com. Well, weekend wrap-up show. Hope everybody is having a great weekend. So let's talk about it, right? Let's talk about the market action from last week. A few things were notable and one thing I really want to touch upon going into this week that's going to be incredibly important. Number one, understanding basic technical analysis. Again, that was kind of the theme for year and year and year. Again, markets come, markets go, but your foundation is always going to be your ability not only to read a chart properly without having to ask 30 people their opinions because charts are really not subjective. As much as you can have an opinion, a chart is really going to play out very, very specifically whether you like it or not. So for example, on Friday, again, the stock to its community is pretty funny, but one gentleman said there was a pivot on Tesla to the downside and I said, well, if it breaks this pivot, it's going to go lower. And this person says, well, it's not going to fall. Well, because why you don't want it to fall? Again, reality and your reality completely different. So the ability, the most basic minimalistic ability to read a chart is going to be very, very important. But also understand that every great market, and I don't care how many years you've traded, somebody will eventually run into a really great market. Obviously, the internet craze was the shining star. I've talked about it numerous times and for all you guys who are lucky enough to trade that during that period of time, you kind of know the gist of it. This market that we've been seeing now for the last several years has been untouchable. The closest thing I've seen to the internet craze, and it took me a long time to really acknowledge that, but when you see retail money really coming in very, very aggressively. Again, I don't want to use the Robinhood platform as the point of retail money, but again, it's very, very hard not to. But when you see retail money flow incredibly getting aggressive, no matter what the news is on the table, no matter what the global ramifications. Again, think about what we've gone through just in the last first six months of the year. We've seen a global pandemic. We've seen pretty much a halt in the economy. Most of us were literally on house arrest for what, three, four, five months. So we've seen incredible unemployment. We've seen anything that could possibly riots, anything possible that could go wrong in 2020 in a year from kind of the mainstream point of view. We've seen it, but yet the market continued to really get aggressive. But unfortunately, and this is just the reality. I'm not trying to be Debbie Downer. I'm not trying to put rain on anybody's parade, especially on a beautiful, beautiful Sunday summer morning.  It's just the reality.  It's just reality. And then at some point it starts up again. The most amazing part of this rally is, I think the most opinionated rally this market is because it's been hated so much. And I think a lot of the fund managers really created this hate and really created this fuel back to the upside because they started shorting the market very, very early, quote, unquote, on the dead cat bounce. Because logically, it was the right thing to do, right? Think about this. The global pandemic and the world stops and the economy stops and everybody's 45 million plus unemployed, global cases of this incredible, horrific virus, people dying all over the place. It makes sense. It makes total sense. And at some point that fuel to the fire got amplified because the market wasn't going down.  It doesn't make a difference what you call it. Again, you could put lipstick on a pig.  So the idea that this type of market was going to go on forever was a little bit naive. It's like me turning around in 2000, the middle of 2000 saying, wow, this is the greatest market ever, right? The global internet is growing. And this is going to be the greatest, greatest life ever that we could all live because this market's never going to go down. Yada, yada, yada. Internet craze only lasted for about 18 months. So the first order of business, when you're looking for any type of, let's just call it gravity, okay? I don't want to put more into it than it actually is. But any type of sign of gravity is a couple of things. Number one, the stocks that ran up, they stopped going up. And I know it sounds very, very simplistic, but think about it. You know, if Amazon has gone up over and over and over again on upgrades, on price target increases, and then Amazon, for example, the buyers get tired, the upgrades become very, very kind of almost like benign, okay? And the stock stops going up because of an upgrade. And then you see kind of an exhaustion chamber started forming. And next thing you know, you start having a rolling top. So that's a very clue. I'm just using Amazon as an example. You could use anything. You could use Apple. You could use any stock. I'm just using Amazon as an example. The second thing obviously, and I think this is as important as kind of reading market sentiment, is technical analysis, right? Technical analysis, the idea that technical damage is around the corner is very, very important. So for example, this past week, you know, we identified the blow off top, okay? We talked about this on for Tuesday into Wednesday session. We identified that. We identified the rollover. And now we're kind of at the point of we saw a Friday session. And Thursday going into Friday, we had this really aggressive rally in the last like 45 minutes. And I went from literally from Thursday at around 2.30, 2.45 in the afternoon to being 99% sell buys to Friday being, well, I think I like the market. I think we're going to go higher just because the way the market completely really bounced off this rising support. And the next thing, you know, we started talking about in the live webinar, futures are up for Friday, futures are up Friday, you know, a little bit pre-market. And I turned around in the morning strategy and I said, well, these charts are kind of a mess. And again, one of the benefits, again, being in the live webinar, you know, we're constantly playing devil's advocate. It's not just well, roast a couple of glasses, let's see what happens. And I started talking about morning strategy that, you know, it's almost like the question for Friday session was, well, did the market really rally on Friday or was programmed buying the culprit, right? The kind of the catalyst to get the markets to get the structure at least from the macro view positive again. And we got that answer very, very quickly. It's almost like the question, what happened, you know, came first, the chicken or the egg. And I was very confused Friday morning. Like during morning strategy, for all you guys who obviously get the email every night, you just go through the webinar recording just in the first 20 minutes. And I said, I have no idea what to do here. Like I have no idea just because how confusing it was because I didn't know if the run up on Friday was literally artificial insemination, okay, or was this real buying to kind of, well, kind of start this next up turn again. And we got that answer very, very quickly, okay, incredibly quickly.  Like it was gone. The first 200 points that ran up on Thursday night was gone in the first five minutes. So now your question was, was there going to be another successful test of this rising wedge and why is this rising wedge important? This is kind of where the catalyst we go into this week. You can see it with your naked eye again. And I say this to new traders, you don't need to understand every moving part of technical analysis. But as long as you have the eyeball test, right, and you can look at a chart, even again, even if you don't know what you're talking about, even if, you know, don't know what you're looking for it. If you could identify similarities, okay, that's one step closer to you becoming to having, having a really good gauge and how to really read a chart properly. And if you notice what's been happening in the last three, four months is this big rising wedge, right? This is all rising sentiment, rising support. Every single time, right, we, we were about to hit technical damage we balanced, right? Hit technical damage we balanced.  We were about to close under and then we balanced. And here we are again. Okay. And here we are once again, looking into the barrel. Well, at least the bulls are looking at a barrel of a really loaded gun. Now, before you turn around and say, yeah, buy the dip, buy the dip, buy the dip. Again, remember, buy the dip only works until it doesn't, right? It's a bull market fantasy that once it goes into bear mode or to sell mode, buy the dip doesn't exist. Okay. Again, I have said this for years. This is the only business in the world. When things go on sale, people don't want to buy. They think they do. The idea sounds great. It's the idea that somebody ran with buy the dip, right? Bears never learn buy the dip until they get caught on a multiple wave of tremendous selling. And this is the first close that I could remember because remember every close prior to this was above this rising wedge, right? This is prior, right? This is an engulfing candle. It held. It held. It held. It held. It held. And it bounced that day. This is the first close that we've seen now in the last, probably in the last four months since all this started that this is a make it a break. And again, I can't emphasize how important Monday's trading session is. Okay. This is a day that literally, okay? And again, you could turn around and say, Dan, you're crazy.  I'm not. I'm really not. You can think that. And if it makes you feel better watching this, I've never really turned around and say, well, this is a make it a break it short term sentiment. But this is kind of, you know, this is kind of is. And if yesterday's sell-off, a Friday sell-off gets confirmed here, okay? You have literally eight to 10 points down in the QQQs. And it's just not in the NASDAQ 100 that you've obviously seen a lot of really decimated charts, especially on Thursday into Friday. And they're very, really, really close. They're forming incredible amount of technical damage. I'll go through some of the charts for you. But if you go through the other indexes, and again, I'll use the spy instead of the SPX. But, you know, the spy, again, is sitting on rising support. Again, think about this. This is the last time we hit rising support. We bounce really hard. This is the first time we closed right on rising support. Now, again, really aggressive technical damage doesn't happen until we lose this 298. But you could see it. You could visually see it. The low here right here is 296.74. Any close below 298 on the spies, it's not good, okay? Again, you could romanticize it any way you want. You could make excuses again. Technical analysis is not a subjective tool. These are the facts, okay? You could either make necessary steps to put yourself in a position to win, take necessary steps to put yourself in a position not to lose, or get the hell out of the way. You only have three choices. You can't just sit there and hope it doesn't happen. If you look at the Russell, right? If you look at the Russell, you could see it with your eyes. It's held now this rising, it's held now literally this rising support three times. The more times it tested, obviously, there's a very high probability it's going to break us. So, you see this area here, this 136 on the IWM. Again, it doesn't look that bad until, right? Until it breaks 136, then you have this measure potential to this 134 area. Again, it doesn't sound that bad, but think about if it loses this last rising support here, where the technical damage to confirm. So, you have to really understand where we are in the spectrum. Again, maybe it might not happen this massive, massive wave of another selling tomorrow, but again, at least prepare yourself.  We know who we are.  There's an inverting hammer. Protect yourself. Do whatever you need to do.   So, you have to put ourselves in a situation to say, well, again, maybe I shouldn't look at the breakout stock. Maybe that's not where the value is. Maybe I shouldn't look at the stock that's up three weeks in a row that's starting to put in lower highs now for two, three days. Maybe I should start looking at stocks that did rally for the last two, three days, that didn't put any type of fight when the futures spiked on that program buy on Thursday. Maybe I should start looking at the charts that are starting to really test multiple levels of support. That's where the value is. Again, in this business, it's a relentless business. There are no Mulligan's guys. Once your money is on the table, it's gone. If you're wrong, it's gone. It's just the reality. So, you have to be prepared going into tomorrow's session. So, if you look at a lot of names that had really, really big run-ups, right? You're Amazon's of the world. Okay, right? Amblow off top. You had that program buy. That program guy lost it really, really quickly. 2712, now it's on the 10 day. Again, if you believe the 10 day moving average is the birth of the trade to the upside, well, what do you think is going to happen if the 10 day gets confirmed to the downside? Again, it might not look that bad, but again, 2690 to 2600 is still 90 points. That's a big, big deal. When you look at Facebook, for example, and it had this really, really nasty candle on Friday. What do you think is going to happen if this 50 day moving average gets confirmed? Again, maybe it doesn't look that bad if you're a longer term investor, but from the short term, going from 216 to 207, it's not the best thing in the world that you could possibly see. Even the video that had this really, really big run. Look how close it is really. Look at the rising wedge. Here's the rising wedge. Bounce, bounce. What do you think happens in the video if it closes below this rising wedge, this rising 20 day? This thing has 20 to 30 points of downside. Again, these are just a few examples of what might happen. Your job today, or your job this evening, or wherever the case may be of you looking at charts, your job is to make sure that number one, if you are heavenly invested, knowing this information in front of you, and again, you could take it or leave it. Again, that's up to you. We're all adults. You're allowed to do, you're allowed to do with your money, whatever you want.  And again, can this all happen? Absolutely. Can I be wrong? Again, I'm wrong every single day. But again, it's okay to be wrong theoretically. Just do not be wrong financially. For example, if we do get a gap up for whatever reason on Monday, we do get a gap up tomorrow, and you knowing this information is possible, this is your time to really understand what your game plan is going forward. If you are fully invested again, what is your course of action? If you want to stay fully invested, do you start putting on hedges? Do you start shorting some of the ETFs? Do you start buying one of these creative derivatives like the SQQ, whatever the dogs, whatever they are, do you start putting yourself in a position that if all hits the fan, you are protected. Again, whether it's net short, whether it's fully per hedged on your long position. But again, do something proactive.  So you have to really take course of action. Now again, is this going to happen? Again, we don't know. All these things need to confirm. These are all opinions. These are situations that if they do happen, yes, I believe in technical analysis and I believe this will happen. But again, we can't forecast this happen. We can't anticipate this happen. We need to actually see it and then make adjustments to get very, very aggressive to the downside. So very aggressive trading week. Again, action is unbelievable. The action has been really, really good. You've seen a lot of things kind of the rail, a little bit of the enthusiasm. Number one, you're seeing aggressive cases in COVID. I think Florida had like 9,000 cases overnight, which is a lot. Which is absolutely a lot. We started seeing a lot of states that initially didn't have the massive outbreaks that we saw in New York, New Jersey, especially in New York, New Jersey, that are starting to get very, very aggressive states like Texas, obviously states like Florida and Georgia that did open up a lot earlier than everybody else. So obviously that is putting a wrench into this rally as well. Even again, I believe that anybody in office is irrelevant. I do. I also believe that a lot of my friends growing up, they're very, very passionate about politics. I'm not.  Again, it doesn't make a difference who's in office, but having said that, Joe Biden and a lot of polls started seeing a bump. Even some polls he actually had a lead. So that kind of freaked the market out as well. Again, this is not pro-Trump. This is not against Trump. Again, for me, I'm right in the middle. I respect the office no matter who's in it, so it's not a political stance one way or another. It's just kind of me just telling you the facts. So that shook the market as well. So I think going into this week, number one, we need to see how any early selling gets handled. Okay, the buyer's solution. And there's a very, very aggressive defense, especially on the 20-day rising moving average. This is going to be something incredibly important, kind of setting the tone for the week. Again, any close on the indexes under the 20-day moving average, especially on the cues, it's going to be bad. It's going to be absolutely bad. So we have to pay attention, especially in the first two hours of the day. Friday's session, very, very aggressive. You can see here, and this is kind of my thought process. I literally said, I have no idea what's going on. I think I said, I think I like the market to the upside based on Thursday's close. But again, the charts were a mess. I go, the charts are a mess this morning. Everything's in the middle of the range is still tight. So naturally, I started putting in pivots to the upside, right? And then I started putting pivots to the downside after we completely collapsed and gave back Thursday's move within the first, you know, literally five, ten minutes of the day. So I started putting in charts to the upside, upside, upside, upside, right? They all put in their initial moves. There was no way, even near a confirmation to the upside, obviously, because everything pulled very, very aggressively. So again, it's a pretty basic thing in trading. And this is where we talk about not painting yourself into a corner. Okay, you always have to be very, very nimble to switch sides, switch bases, switch buys very, very quickly. Again, it's not the market we need to see. It's the market we have in front of us. So it's incredibly important to kind of go with the trend and not, you know, not fight against it or kind of make excuses. So these were all pivots to the upside, nothing confirmed upside, not even close.   Let's start looking to the downside. And we did, right? So here's the first one. I didn't have a locate on this thing, unfortunately, but it was a hell of a flush for all you guys who did get it. Congratulations. I know if it builds below 30, can flush. Here is I know have this really, really monster big gap up monster run for the last like three, four days. Here is the 30 pivot. Here is the 30 pivot here right at this area right here. Here's the 30 pivot. And this thing just got just destroyed. It went down like, like five and a half, six points in that first candle. So if you did catch it, congratulations. Incredible. I mean, just incredible, incredible move. Unfortunately, I didn't have a, I didn't have a locate. Netflix 460, if it builds below, can flush. And KLA obviously never got to the 74 level. 460 on Netflix, if it builds below, can flush. Netflix got just destroyed. I mean, just absolutely destroyed. Here is the, you know, here is the 460 level, right? Excuse me. Here is the 460 level right here and just demolished. Just destroyed. I mean, this is again, it shows you the aggressive nature of this market. Again, by the dip only works when there's a, you know, when there's a, there's a rose colored glass effect that everything's all happy in the world. Look at me. Look at the move. I mean, this is a 30 point move from top to bottom on Netflix. Got just murdered. Tesla 974, 970, last two areas of sports that builds below can fall hard. And if you look at Tesla, Tesla is literally one day away from getting really aggressive technical damage. So here's the 974 area when, you know, went all the way down to this nine fifties level. And again, this is literally one day away, you know, from getting technical damage. This thing confirms this 20 day moving average is just kind of like mirroring every other ETF, major ETF. This is, you know, this thing has 70 points in it. Again, if the market starts selling, you have two, three weeks of selling, why can't Tesla fall down 70 points? Again, for me, I don't care. I trade Tesla to the upside, to the downside. For me, it doesn't make a difference. But again, logically, if you believe this stocks trade from supply to supply and stocks trade from demand to demand. Again, there's nothing in between the 20 day moving average in the fifties. So there's a lot of room to the downside.  Take on the way down, Netflix destroyed. Yeah, you know, 948, 950 is the bottom of the range. DraftKings nice move on DraftKings as well. Again, you know, you've seen all these, you know, like when, I remember when Rudy Gobert, I think it was the first NBA player to test positive for COVID. The NBA suspended their season within, within 15 minutes. Okay. Now they have this plan and it does feel, again, listen, I am the last person who's going to say anything bad about any sports to open up. Man, I'm dying for sports. If you're like me and you love sports, the NBA, the NFL, hell, even baseball at this point. You need sports in your life. It's just, it's just so necessary. So I'm the last person to say, hey, they shouldn't open up. Okay. But what you're seeing from the first test that was positive to kind of where we are now, I think out of the 22 teams that are set to open up and the season is set to open up, I believe on July the 30th. Right. I think this is the first, the first game of the NBA. Now you see 6% of the players that are participating test positive and how the market, how the world has changed in four months. Now, you know, they're just kind of force feeding. And I get the television money. I get all that, man. I get all that. You know, again, you could see how the almighty dollar is much more important these days than the overall health. And again, I don't want to get into the whole discussion. It's, you know, it's, it's a big flu. Okay. Let's, okay. Let's whatever you believe, I respect your belief, respect mine as well. But again, at the end of the day, look how things change in four months.  And again, if there is just one doubt of, you know, one of these leagues opening up, these things are going to get hit. So we took, you know, we looked, we looked at this 34, 15, 34 daily. Any close below starts next like down. Here is DraftKings. Right. So it took out this 34, 14, 34 level. I traded down to like 30 to 60 ease. Again, it should get, you know, it should get down to this 30, 30 area. I still like the downside of this as well. DraftKings was a nice move there as well. And that's it. I mean, that's it. You didn't need a lot, right? You need a lot. I think the biggest problem with Friday's session was, of course, there was some aggressive action to the downside with Tesla and Netflix. Roku as well. I forgot to, I apologize for all you guys on the Twitter feed. I forgot to put Roku. I forgot to put Roku on the feed. There was a 23 break that went down as well. The problem with Friday's session was, number one, we had to figure out if the buying from Thursday was real or the buying was program driven to kind of set the floor, which obviously we got our answer in the first five, 10 minutes of the day. So, and also the problem on Friday, if you found yourself either getting chopped up or kind of confused, you got to write to me because the first big, big move came on one candle. The second big move to the upside came on one big candle and then everything kind of went kind of sideways to the down for the rest of the day, causing kind of a pretty ugly 3% move for the Dow and the S&P to kind of end the week with a 2% down for the NASDAQ composite. But I think more important is the market continues to be really, really good for all you guys who don't trade on the short side. And again, I understand it. Okay, I understand it because again, it took me years and years and years to kind of really get comfortable trading from both sides of the ledger. So, I get that part. But the one thing you do have to understand is God gave you two arms, two feet, right? Two eyes, two ears. Market God's given you both sides of the market to trade. So, you could be equally progressive and proactive in both. And again, especially if we do start confirming the 20-day moving average on a lot of these indexes, again, you have two choices.  Or you can participate to the downside, which obviously, again, you can do so by buying bearish ETFs to the upside, obviously shorting the market via equity or buying puts. But the last thing you want to do, and this is kind of my piece of advice, especially for new traders, when the market has technical damage, there are no breakouts, okay? When I hear, when I see people talking about break, especially in March, when I saw the market completely just getting destroyed day after day after day, and people talking about breakouts, it's a novice thing to say. There's no professional trader going to sit there and say, the stock is breaking out when the rest of the market is imploding. Again, you might catch a trade once, you might catch a trade twice, but if you're going against the tidal wave, eventually it's going to drown you. So be very, very careful what you do if we do, if we start putting in technical damage. For all you guys who are joining us in the live webinar this week, please get there at 9 a.m. eastern time. That's when Morning Strategy kicks in. If you are joining us on the Twitter feed, which is a perfect place. If you've ever tried to wonder if the pivots are for you, that's the perfect place to start, especially if you can get into the live webinar. For the rest of you guys, have an awesome week. Enjoy your life. Again, we don't get a mulligan, right? Just the same way when you put your money on the table, you don't get a mulligan to get it back.  Love your family. Learn to smile, and God bless you all. See you all tomorrow, guys. Congratulations for putting in the time to take control of your trading. You're one step closer to owning your future and achieving the success you desire. Want daily trade ideas directly from Dan? Straight off his personal watch list? Unlock our free PS60 Vault, where you'll get nightly updates on pivot opportunities we're watching for the next day's session. Click the link in the description to get started today.