Hi everyone. Thank you. I know it's evening and thank you for joining the session today. Today we're going to talk about dynamic large scale Spark on Kubernetes with Argo workflows and Argo events. Before we kick off this session, I would like to check how many if you're currently running Spark on Kubernetes today. Yeah. How many of you are using Argo workflows today? For any type of right? Thank you very much. That gives probably you can take away some of the points from the session today. My name is what up onto. I'm a principal solutions architect working for AWS. I'm specialized in data analytics and Kubernetes. And today I have my colleague Ovidu. Hello everybody is very excited to be here. My name is Ovidu. I'm a container specialist solution architect at AWS. And I'm excited to have this presentation together with my friend Vara in front of you. All right. Thanks Ovidu. Without further ado, let's get started. Okay. Sorry. Right. Just a quick agenda. We're going to talk about, I give you a little bit of intro for anyone who doesn't know about Spark on Kubernetes by just introduction to Spark on Kubernetes. And then we move on to why you need why customers want to run Spark on Kubernetes. And, uh, and we discussed about some of the best practices for running Spark on Kubernetes. And especially, and today's talk is about, you know, when it comes to running a large scale Spark on Kubernetes, what kind of considerations on the best practices that you need to, uh, consider before deploying your workloads onto Kubernetes. So we'll touch upon those best practices. Um, and, uh, we also talk about other workplace and other events like, you know, some of these data pipelines that you're building, um, you need to ensure that these data pipelines are running on specific schedulers or workflow engines. And, uh, we will dive into the other workplace and other events. And finally, the demo, we prepared a demo that will showcase how you can actually create a DAG with multiple Spark jobs and trigger those Spark jobs using both Spark operator and Spark submit as well. So that's what we are planning today. So let's get started on the Spark on Kubernetes. So as you see on the slide, um, as you all knows Apache Spark is a distributed processing framework and which is mainly used for processing terabytes and even petabytes of data at a very large scale with both unstructured, even structured data for Apache Spark. It comes with set of libraries such as Spark SQL, uh, MLib, streaming and graphics is mainly for various data processing such as, uh, data processing machine learning and real-time data processing and a graph data processing. So these set of libraries that you can use to process various set of data and you can run Apache Spark on a standalone machine, such as your Windows machine or a laptop. Um, but to run Apache Spark on a distributed mode, uh, to process terabytes of data that is when Apache Spark needs a resource manager, such as Hadoop. You might be familiar, uh, Hadoop Yon and Yon is used as a resource manager to actually distribute that, uh, executors across these instances and run your workload. But then, uh, in 2018, that's when, uh, Apache Spark added a support, uh, for Kubernetes as a resource manager. That means you can actually run, uh, Spark workloads onto Kubernetes, um, now, um, you know, uh, with that version. That's when the shift started. Customers have started to think about, uh, you know, we want to migrate our existing workloads into Spark on Kubernetes now and how matured it is and because of all the scalability features that, uh, Kubernetes offers. So why Spark on Kubernetes? I think probably that's the common question that everybody asks and I know most of you know about Kubernetes because you hear, uh, Kubernetes, it's very powerful container orchestration tool, which you can, uh, use and leverage some of these features. One of them is dynamic scaling, right? You can leverage, you can run your Spark workloads, bursty workloads from zero nodes to 1000 nodes, uh, when you trigger the job and you can scale down when you don't want to run the jobs using cluster auto scaler or carpenter, um, using the auto scaling features portability allows you to actually write your spark job and containerize it and run it on any type of Kubernetes flavor of the cluster, right? So you can use, uh, EKS GKE or even on-prem Kubernetes clusters and should work and resource isolation. It's one of the key feature, uh, within the spark. Like you can define every single job can have its own CPU and memory definition. That way you are ensuring that your job doesn't take more than that CPU and memory that you allocated. So that gives you that resource isolation ensures that every job gets its own quota of the job. And it's a cloud diagnostic, right? So you can develop the script. And if you think about migrating onto from on-prem to more cloud native tools, like, you know, EKS or GKE, any other, uh, Kubernetes, uh, solutions, you can run it. Um, and the multi-tenancy, uh, one of the key feature about running spark on Kubernetes is the multi-tenancy, right? So you can have namespace isolations, multiple teams can share the same cluster and run the multiple workloads and running multiple versions of the spark. I think this is the one of the key feature. When you're using Hadoop, you might have that situation that you'll have to create a dedicated Hadoop cluster for every single spark version. But with Kubernetes, you can use the same Kubernetes cluster, such as EKS in this case, I'm talking about, uh, you can have multiple versions of the spark running on the same Kubernetes. That's a great feature. And the CNCF ecosystem. So now you want to monitor your spark jobs and you want to create dashboards and you want to, uh, you know, extract the logs and CNCF ecosystem comes with a lot of open source, um, add-ons, which you can leverage to, uh, build a good ecosystem on a full data pipeline. And cost optimization. So all of these features actually drives the cost optimization compared to other, you know, the traditional way of running on Hadoop, uh, a spark on Hadoop, just mainly the auto scaling portability and multi-tenancy. Right. This is a simple, uh, uh, command like spark submit. So it's no different from how you run on hood Hadoop. And if you see the master URL points to the k8s your API server. So if you want to run your spark job and using this command, you can run it on your local machine, um, and, and then points to the k8 server that can be any EKS or any of the Kubernetes cluster and, and the job will run. Let's, let's dig into how the job that runs on Kubernetes. So this is, uh, this is the internals of how you can run spark on Kubernetes. Like, you know, how it works when you submit a job. As you see on that, on the left hand side, we have Kubernetes control plane and the right hand side Kubernetes data plane. So when you submit your spark submit from your local machine or from air flow or any other schedulers and the job will be submitted to an API server and an API scheduler will schedule the driver part. That is the first step. Uh, once the driver part is created, but it's not showing there even there's a headless service also created along with the driver part and the driver part will request scheduler to request API server to schedule the executors. So, uh, how many of our executors and the scheduler will schedule those executors to, you know, and once these executors started running, uh, and these executors will, will make a connection to the driver using that headless service. That's how the driver communicates with the executors and send the task down to the executors and the jobs will run within individual executors. So that, that's how Spark on Kubernetes works, uh, pretty much, uh, the communication end to end. But now, as you see here, this is a YAML file. This is a Spark operator, but within Kubernetes world and everything that we want to run in a YAML declarator way, right? So what we have seen before is a spark submit is a simple JSON config with a CLI command, but rather you want to define your spark jobs in a simple YAML file with, you know, the job name. And as you see here, the driver codes and executor codes and number of executors that you need. So this simplifies the whole process of running a spark job. So I can just simply write this job and then use kubectl to apply and that goes and runs your spark job on Kubernetes, right? So how to, uh, get to this point and how do we actually, uh, um, you know, leverage Spark operator. Let's talk a bit more about Spark operator. So Google, uh, has created this, uh, new Kubernetes operator for Spark to simplify that process. So what it does differently from Spark submit, let's take a look at this one here. So you have the created two, uh, uh, CRDs, which is Spark application and schedule Spark application. And it comes with four main components. One is controller submission job runner and spark pod monitor and then mutating admission back up. So it's basically a controller, uh, which will, uh, look for the spark application object YAML that you have seen before. And then controller will ask the submission, uh, submission job runner to actually submit the job and submission job runner. What it does is it converts that YAML into a spark submit assembles into a spark submit and then submits to Kubernetes API server that's, which is similar to what you have seen with the spark submit. So even spark operators leveraging spark submit behind the scenes, once a job is submitted and spark operator does it in, uh, differently in two things. One is park pod monitor and the mutating admission by book. So once a job is submitted, uh, you might need some storage.  So mutating admission by book is responsible before the pod is persisted. It goes and creates the volumes and amounts the volumes and, you know, and ensure the pod is created with all the volumes and the spark, uh, spark pod monitor, this ensure the status of driver status, executor status, and keeps informing to the controller saying the job is running. So if in any case of the job is failed and the controller can restart the job. So that's how the operator works and which is more a simplified way than the spark submit. But underneath it uses the spark, spark submit, right? So, um, the scaling spark and Kubernetes cells, some of the common challenges that we, when we work with the customers, they come back and say, Hey, we tried spark and Kubernetes, but when, when it comes to scaling, when we go beyond 200 nodes and even 1000 nodes and we hit, we hit all sorts of issues and what kind of consideration we need to take and how you availability consideration, what kind of logging and monitoring that we need to build when we run spark and Kubernetes and choosing the right compute and storage and what kind of network configurations that we need to define. And do we need to use specific basket you list to run the job? So let's talk about some of those best practices that we talked about. Some of the challenge that we talked before, um, as you see here in this diagram that we have, um, VPC CNI, I'm not sure how many of you heard the networking within EKS. We use VPC CNI, um, uh, within AWS, but, uh, the similar networking will be, um, available in other cloud providers as well. So with the VPC CNI, we use a part networking. So, so when it comes to running spark and Kubernetes and you want to run 50,000 parts, you need 50,000 IPs. So, which means you might hit, uh, you might have heard, um, you know, we've hit IP exhaustion issues. So to avoid IP exhaustion issues and one of the main considerations, our recommendation would be do is create to, uh, uh, use a non-routable IP secondary side of range to create a two large subnets and each comes with a 65,000 IPs. So in this architecture, you can go up to 120,000 parts running on within the same EKS cluster if you want to run a large scale. And then the future, the next step is, Hey, um, we want to try IPv6 and now we secondary side of range is an option, but, um, this is something that we are looking at in the future where the customers move to the IPv6 and it's a lot easier to work with. So Kubernetes from version 1.23, it supports IPv6 and even spark 340 added a support for IPv6 as well. So all that you need to do is the configuration that you see here when you submit the job. And that allows you to leverage IPv6 and awards IP exhaustion. You have a, you know, uh, the large number of ports that you want to run within the same Kubernetes cluster. Right. So this is one of the common issue when it comes to the core DNS, like if you're running a large scale, uh, spark and Kubernetes and the first thing you might see an error, something related to unknown host exception. It's because, uh, the nature of the spark is a bursty workload. So you go and hit 10,000 parts and trying to spin up and trying to communicate with each other and, and putting a pressure on the code DNS. So to avoid that, we recommend using cluster proportional auto-scaler with the code DNS, which means when your Kubernetes cluster is growing, uh, and your code DNS spots horizontally scales to support that, uh, the last skill Kubernetes cluster. And then we also recommend using node local DNS caching, which is basically, um, uh, you know, having a cache in every single node so that it doesn't need to make DNS resolution called to the code DNS ports. Uh, it can refer to the local caching and reduces that amount of calls that makes the code DNS and improves the performance for the large scale Kubernetes clusters. And finally the pod anti affinity, just make sure that you don't run, uh, more than two, you know, more than one part within the same node. And these code DNS spots are actually spread across all the nodes rather than running everything in a single node. Right. So one of the key, uh, important feature here is, uh, the storage when it comes to the spark, you know, you think about storage and it's a, what kind of storage that we need to use. And we highly recommend using NVMe SSD based volumes, uh, because spark is a nature does a lot of shuffling, and NVMe based SSD volumes, as you see, this is part of the city instance itself. Like, uh, when user, uh, when the spark job runs, it goes and pulls the data from S3 bucket into that NVMe SSD. And these executors can, uh, you know, work with the data quite quickly without having to, you know, without having any latency. So this gives you high throughput, low latency, uh, you know, the, uh, performance for the spark jobs. But you also have another option where you can leverage the block block storage, which is EBS volumes as well. But, uh, the only difference here is you get a variable throughput because E, um, the volume is external to the EC2 instance. And you have, uh, and the EBS bandwidth, uh, Bay, various based on the EC2 instance that you choose, the smaller instance and the larger instance. And, but it also comes with other features like reusing persistent volume claim. Like when you wanted to leverage read, reuse the persistent volume claim is a feature within Kubernetes where if one of the node dies, uh, for some reason, um, you know, even if the node dies and the pod gets killed, you can still keep the, uh, the, the volumes that are connected to that old node and reuse those volumes with the new node that comes up. So basically it doesn't need to re-computed. It can use where it is started. Give it to me. Yep. Do you want to? Yeah. Thank you. Hey. Now, what options do we have when, uh, when we're running from the compute options, when you're running the Spark on the Kubernetes? When you are deploying the Spark on Kubernetes, the executor can be scheduled on the spot instances and the driver on the, on demand instances. And they, this in, scheduling the executors on the spot instances, these enables the faster results by scaling and executors running, uh, on it. And there is also another way because, another reason, because if a po, if a driver pod, it will be run on the spot instances and the spot instances gets terminated, then the, the whole application fails and have to be resubmitted. While if they, that's why we recommend that the driver should be always installed on the, on demand instances. The executors though, even if the spot instances get terminated, the resiliency from the Spark, the driver will create a new executor once the new spot instances it is, uh, it is being created. And like this, and like this, you achieve the, uh, also the, the cost efficiency and, uh, uh, within your cluster. While running Spark on the, uh, on the Kubernetes, you can experience challenges with the scalability and performance. But with the carpenter, the Spark clusters can quickly, can be quickly scaled and dynamically. And in the same times you, uh, and the workloads meets the, the needs that they want to, and in the same time you achieve the resource availability and the cost efficiency with it. And Carpenter is coming also with some, uh, features, improvements to workload consolidation, deprovisioning, no termination, and further improving the efficiency and the cost savings. Apache Unicorn that you will see actually in our demo, uh, it's a batch scheduler purposely built for the Kubernetes uh, and usually used into, uh, big data and ML workloads. And it is particularly useful for Kubernetes with applications like, uh, with the features like application aware scheduling, where it is recognized the users, the applications, and the, the queues, the scheduling according with the, uh, submission order. It has also gang scheduling for the pod placement. And you will see, uh, you will see this also in the demo and a hierarchy queues for built in for, uh, optimized Spark performance. Um, for the logging, Fluentbit, uh, Kubernetes filter allow you to enrich your log files with Kubernetes metadata. And if you are using the filters, you are seeing the, the field, uh, Fluentbit filter, uh, extra filter config, uh, you can get the metadata, uh, from with, uh, with Kubelet instead of, uh, querying the API. It's very specific, very useful in when you have large customers because you don't put pressure on the API anymore. Getting into the Argo workflow, uh, this is a DAG orchestrator. The workflow engineering is the best way to run Kubernetes, um, uh, because it is built for Kubernetes. All right. It is very, uh, popular and very useful when you're running, uh, uh, ETL workloads or training jobs and you can orchestrate deployments and then using in your DevOps and, um, CD pipelines with Argo workflows. And the Argo workflows, it can be integrated with Argo events and Argo events will, will fetch any event source, uh, whatever it is, Kafka, Slack, uh, web, different web hooks, you name it. And you'll see also in, in our example, when you'll have like an external, external source that will trigger an event and consequently an workflow and a job. And this is actually what I'm going to show you to you in the demo today. So in the demo, we created an Amazon EKS cluster and installed the Argo workflows in the Argo events in their dedicated namespaces as you can see on the screen, Argo events and Argo, Argo, Argo workflows. In the same time as an outside source, okay, we create, we, we have an Amazon SQS queue that is created to receive requests from users. Okay. And the, and then SQS event source that you're gonna, you'll see it into the Argo events namespace object, okay, that is set up to fetch the events from that, uh, SQS queue. That event can come from any, like S3 put object notification, not necessarily from users. Yes, which triggers. This is only for the, uh, that we choose for the demo purposes. The sensors runs and waits for the certain condition to be made. Okay. And we will go through the, how the sensor is built. And when the, the message is received by the event source, uh, the sensor sees it and creates an workflow and the workflow through the Spark operator, um, uh, create a Spark job and, uh, in the Spark team, a namespace. Obviously, Vara mentioned earlier the multi tenancy. So you can do like that in separate namespaces. It allows you to build that end to end data pipeline using Argo workflows and, and you can drive those data pipelines using Argo events in this case. And you have Spark operator Prometheus and Unicorn sit in the, within the same communities cluster. Um, okay. So yeah. And now I am going to jump to the demo. Okay. As you can see here, all right. We can pick off the demo first and then we can talk about yes. I will, I will start because it takes two minutes for the, for the job. Okay. And then I'm gonna, uh, I'm gonna tell you exactly what I did. While all we do is, uh, triggering this job. And it's basically, uh, just simple YAML configuration that, uh, you can define the whole DAG, uh, in a YAML file using Argo workflows. And you can build the most complex Argo workflows similar to you might be using our airflow, um, for, uh, scheduling your data and ML jobs. And now Kubernetes, uh, Argo workflows is a very lightweight workflow engine is more powerful that you can build your end to end data pipelines and include the ML pipelines part of the data pipelines and, and use Argo workflows as your, you know, native engine to run your jobs. So, and all that you will be doing is defining, um, uh, simple YAML files. In this case, there is a sensor YAML file that we do is going to show you, uh, in a minute once the job is triggered. Allow me to trigger the job. And then we'll come back to you and then show you to what exactly what I did. So we have SQSQ created, um, is going to send a message, uh, to an SQSQ to invoke that pipeline. One second. If to make sure that I have the variables in place and yeah, it's all good. I didn't copy the whole part of it. That's make it more interesting. Yeah. So, um, I think when it comes to the production that you have this so the message from the, and now I can increase it a bit again. The message from the SQS was sent and we should have a workflow triggered. Yeah, it's here. I can see that on the web UI, which is the Argo workflow web UI here. If you can explain that a little bit. Yes. And that's the sensor and need to lock in again on this one, I think. Yep. One second. Argo workflows provides a really nice UI that you can expose that you are using engine X and are backed by any application load balancer or a network load balancer. Um, and set up AD authentication with your own LDAP configuration to make it more accessible. So our, our, uh, our event source is a SQS spark workflow. Okay. Was met dependencies for the, for the sensor and created a spark workflow. And this one, it is just happening right now, as you can see. Okay. So make it a bit bigger for you. All right. Okay. And the workflow has several parallel jobs. So one job, it was the, some simple job, hello, world job. And from this to, from this job, okay, another two jobs has been created. And at the end, a bigger one. And you will see that in the, uh, in the same time, because I'm going to move to the CLI, when the pods are being created into the spark teammate namespace. And also in the same time, the carpenter will provision the nodes for the driver and also the spot instances for the executors. Behind the scenes, it is using unicorn as a gang scheduling. Uh, it's using carpenter to scale your notes. So basically you have a empty cluster, just a Kubernetes cluster running. And once you trigger the job, uh, which is all the workflows, and it goes and spins up the nodes and runs your spark job and then scale it down to zero. Okay. And this is the event source, okay, that is running into Argo events namespace and is looking for the, uh, events that are to fetch from Amazon SQS. And then this is the sensor and I'll go really briefly through with you. And the sensor has is triggering the Argo workflow here, as you can see, and this one has several parallel jobs, one, two, hello world jobs and one spark operator job, more complex jobs. The jobs in the, in this park operator jobs, they have the driver that needs to be installed with the, uh, through the carpenter on on-demand distances as you can see in here. And in the same time, it is in the unicorn will take, take care about the scheduling. And in the same time, the executors with the executors will be installed on the spot instances. And the same is happening for the bigger job. But because I measure it and it takes exactly two minutes. Okay. And I think I'm already talking for two minutes about this thing. So we should have these things happening here. So if I keep, if I click that, it gives not viewer. Yes. All right. So these are the, the first three nodes. Okay. They are the core nodes where the system is running. And the carpenter created one node on-demand node for the driver and two additional nodes where the executors are running. Okay. If I'm going to, if I'm going to watch this into the spark, uh, spark team a namespace. Okay. I see that the first driver, the, the first driver into this workflow, this one from here spark operator, uh, PI job has, it is running already and the executors are being created on the spot instances. Okay. While the second job, the spark operator taxi job is waiting to finish the spark operated PI job. Okay. And then to start running, running again. And that the executors from the, from the first one is terminating and the, the, the, the last job is being created as well. Yeah. I think we are near to the time. Uh, sorry, we have took, uh, longer than that. But if you have any quick questions, uh, we can take a couple of questions if you have. Yeah, go on, please. One question. And then, sorry, they can, can they find you somewhere? Yeah, yeah. We'll be standing outside. So that's a good question. Um, there's a lot of customers are experimenting IPv6 at the moment. Uh, it's because even though spark submit supports, uh, there are other components which does not have support for IPv6. So it's mainly validating what other tools or add-ons that you want to run on Kubernetes, whether that supports IPv6 or not. Say for example, spark operator, uh, does not support IPv6 because of that reasons customers are waiting until that support is available. So, so we're thinking about that's a feature, but once we have that, you know, we are going towards that.


All those things are all together in the same issues of should we spend more energy on facing the end of the month or the end of the world. This is a difficult issue. How much should we do for the future? Okay, there are many people who believe that the future will be brighter today, that we will have more wealth, that we will produce more goods and services and therefore future generation will be better off than us. So when we decide to invest for those generations, in fact we increase intergenerational inequalities and that's one bad aspect of sacrificing the present for the future. We ask the poor, the current generation to sacrifice consumption for the benefits of people who potentially will be much wealthier than us and therefore that would be compatible with the common good only if the benefits of those investments are large enough for the future generation. The problem with this general way of thinking of economics comes from the fact that we make the assumption that we will be more prosperous in the future, that we live in a growing economy. It has been true for the last two centuries but it's not necessarily true for the future. So one of the big things I did in my research over the last few years is to examine the role of risk and uncertainty and so that's why in our society we penalize investment that generates benefits for the very distant future and in the real world we speak about discounting. We discount the future, we penalize the future just because it's a future and the problem is how by how much we should or we do discount the future is the critical question I'm examining in my research. One application of this technology is in terms of when we apply that to climate change. Climate change is we must, we are asked to make sacrifice in the short run because the energy transition will be costly for the benefits of reducing emission and the reduced emission will be mostly beneficial for the for future generation, generation who will live on this planet in in 50 years, one century or two centuries from now. The question is how much should we be ready to give up in terms of purchasing power today to reduce emission by one ton to simplify when you eliminate one ton of CO2 emission today you reduce damages by something like 1,000 or 2,000 euros in 50 or 100 years from now and so the question is what would be the energy transition efforts that we should be ready to give up to do in order to eliminate those future damages. This debate can be summarized into the price of carbon. Typically we think that should be something between 50 euros per ton of CO2 and 200 euros per ton of CO2. I believe it should be something like 160 euros. That's one way of translating your question which is do we do enough for the future into a number I mean a number like a discount rate or like a level of the price of carbon and so when I say 160 euros per ton of CO2 I mean we should do all efforts of reducing emission that cost us less than 160 euros. If we think that's not enough we should increase the price of carbon. All those things are all together in the same issues of should we spend more energy on facing the end of the month or the end of the world. When one difficulty with this whole issue is whether people will accept to make those sacrifices and whether the politician will be ready to bear the political cost of imposing those sacrifices to the population. Anyway this is the kind of thing I've been studying with colleagues here over the last five years with the assistance and financial support of the Agence Nationale de la Recherche which I thank for their support.


I'm Rusty Komori and this is Beyond the Lines on Think Tech Hawaii. I was the head coach of the Punahou Boys varsity tennis team for 22 years and we were fortunate to win 22 consecutive state championships. This show is based on my books Beyond the Lines and Beyond the Game and it's about leadership, teamwork, and creating a superior culture of excellence. My special guest today is a very successful leader and general manager of Noi Tai Cuisine and Waikiki. She is Tawaii Tawadee Ford and today we are going Beyond Exquisite Dining. Hey Koi, welcome to the show. Sawadee ka. Thank you for having me today. Now your first name is Tawadee but everyone calls you Koi, right? Yes, right. So it's like a pass for everybody to remember my first name on that Tawadee. Koi is my nickname on my family. My friend called me that and instead of Tawadee. Yes. And Koi, can you tell me a bit about your background growing up and then how you ended up becoming the general manager in Hawaii? Sure. So I'm growing up in Thailand in Bangkok study there. My family is middle class. My dad is CEO on the big company, private there. My mom is teacher. So when we study computer side, finish on the computer side, but I'm working on my career only three months and just turned to marketing, turning to secretary of one company, which is Five Boss and they sent me over to work in Los Angeles. And I feel like, oh my God, this is good.  Mom call me what time you're coming home, anything like that, because I'm the youngest daughter in the family on a Thai culture. The daughter is like a very kind protecting. You cannot go out to play. You cannot go anywhere longer than, you know, coming home early like that and say, well, state is nice. Mom don't have to call me. So when I go back home, I'm just like, no, mom, I want to go back to study more in state if possible. So I just like, okay, mom just like really can say, yes, I will do it. I love mom and dad and came here, which is 20 years ago without taking their money and starting find a job, you know, in San Francisco. And I got the first job is dishwasher. On that time, 20 years, which is you have to do three things, dishwasher, not the washing machine, because that's a restaurant I'm working, they don't have it, that's the washing machine. And all the way grew up, everything positioned in the restaurant, I know, and to go up to the Hawaii, I live in San Francisco about 18 years. And my husband, which is we met in San Francisco Airport while doing I'm working there, like so many when you see each other in the first and that's right, that's my that's your something like that. Yeah. So the first day and we just like, okay, we fall in love. So he came from he forms Hawaii. And, you know, I'm visiting Hawaii, I like it, the culture is more the same Thailand. And then the time which is he have to come back to work, I just like, you know what, I still love San Francisco.  So, yes, I came to Hawaii with him like three years ago, and starting like, looking for the job, which is like, Oh, well, what I'm going to do, but a lot of restaurants in Hawaii, anything like that. But one of the CECO in Noitai, just like, you know what, we need help, you want to do helping us and say, Yeah, sure. Why not? I'll try to help what I can. So that's why I become to work with Noitai, like I'm starting from host to food run to bartender to server and become asset manager and manager right now. Yes. It's a lot. Sorry. Yes. Koi, it's absolutely amazing, you know, what, you know, how you started in the industry as a dishwasher and to make it all the way to general manager now. And the other week when I came into your restaurant, I felt so happy to be able to meet your husband and your son. And you have such a beautiful family, they're so nice. And they must really love your cooking, right? They're lucky. Yeah, they love my cooking. So when we had home, I have to prepare like four or five dish for the Thai food for my husband. But my son is just like, Oh, mommy, your chicken is the best. Your fried chicken is the best. You know, we, we come to the restaurant because they love Thai food. My husband, um, they claim the Asian, but he's, you know, they claim the Asian. That's why he can eat Thai food a lot. Yes. Well, they're, they're absolutely fantastic. And, and Koi, you know, I absolutely love your food at Noitai. I need to have the lobster tom yum. I need to have the curries, among others. I mean, the dishes are absolutely incredible. Now, why, what is it that makes Noitai restaurants so unique? Okay, so people thought one thing is about Thai food, it might be state food, you know, easy to find, but the Thai food is, it's not easy to cook. Some curry is hit a long term to cook like maybe an hour to breaking on the what it called a sauce of chili place, the coconut milk to break it. It's all the herb in there. So Noitai cuisine, we serving as the Royal Thai cuisine, which is we have to be the good on the food and beyond and customer service. That's why it's a goal for us and make a Thai success. Well, it's, and Koi, it's like there's needs and wants. I mean, I actually need those dishes. I mean, it's so, it's so good. I crave it. And, and I love how, you know, you do some of these wine pairings with those dishes. I mean, it just really makes a great experience. Yes. So the thing for me is, because I'm from San Francisco, and I into the why, which is like, you know, when I first met my husband, my first day is in Napa, we on the Y testing together. And we're just like, Oh, I learned a Y from my husband and learned a Y from the work, what I worked before in San Francisco, right. And then I was like, Hey, we should be adapt with the Thai food because Thai food have a lot of fairer. Why is to have a lot of fairer on that also? How is become to be together between Thai food and why to be matching not like you come to the high restaurant. I don't know what I drink, maybe a beer. No, we have to want to expand out to everybody to know. So why can be good with Thai food, sake can be good with Thai food is not only the beer or cocktail, you know, like that. And I personally like to drink and find a way which is how the smell on the Y, the grape and what is texture color on that is acknowledged. It's a never in. Well, I absolutely love it. And I'm looking forward to my next dining experience there. And, and Koi, you've had a lot of celebrities that have come to your restaurant, I mean, including President Obama and shark tanks, Robert Herjavitsch, I mean, and many others. How does that make you feel that the celebrities come into your restaurant and they just really enjoy your your dining experience? We, we're very happy to serve everybody and is make sure we so proud when they happy to dine with us, you know, so when they reprise, which is me, like, you know, after it is good, a customer service is good, you know, which is that that's a goal for us. Go for me, go for everybody already. Maybe we exciting when we have like a celebrity coming or sometime we don't know is who that person that looks similar and we find out, oh my God, they want to come into a to join with us without telling who they are.  Yes, exactly. Exactly. Like a big surprise for us. Now, Koi, I want to ask you, you know, during the pandemic, what did you do to adapt and adjust to really try to survive during the pandemic? Yeah, we have to shame we lost a lot my staff in the front of the house, you know, and we have to adapt to let every staff to be working with us. We changing from dying to go order to deliberately order to and when we can open the restaurant, we got a support, we got the support from the all the local people. And during a time, the Roy in Hawaiian was a lot helping us on the what they call that the rain rain control rain for us. And also is the main office in Seattle, helping us to promote we have Pia Marketing Mona Wood helping us to expand what is people know us so much about know about us in Hawaii. So that's we try our best to stay on, you know, to make sure people know we want to serve everyone we try to help people, which is, you know, in the first when we close this island, no money, no working in the industry or what everything we wrote a price to help people, which is in Hawaii to can afford to try to tie our food on that will just become with a bento 10 dollar menu before they love it, because at least they can spend like $20 for the whole day for the meal on that. That's with helping, you know, on. Yes, and that's the reef coming to its refresh, not reverse return to us after pandemic happening after that, people just like, Oh, I tried this restaurant already, I want to come back to eat again. Yes. Well, that's that's so good.  And now you guys are thriving, but you're you have an award winning restaurant. I mean, you have such a great team that you guys all work well together. Now, how does that make you feel when you win some of these awards? It's a little proud.  And I want everyone to come to the restaurant as they want to be at home and get served, you know, so like, how do you want to get served when you go out to eat? That's the way you have to serve like to customer like that. The food is fantastic.  I eat the food in the restaurant every day. I make sure the food is right quality to come out to customer. But sometimes I have to say I'm apologize. Sometimes it's not so busy. We try to get the order out. But sometimes it's missing anything like that. But we try our best to get to get to same party every food to come out. That's what it. Yes. Now, Koi, you know, how you mentioned earlier that you have worked in every position in the restaurant basically and and that that really allows you to have respect from all of your team members because they know that you understand every part of the business that they're dealing with that they're experiencing. Now, that's that's really valuable, right? Yeah, that's very valuable because, you know, I start from a dishwasher, go up to helper in the kitchen, go up to the chef. When I'm done, go out to the bus boys serve on a server become do bookkeeping, grocery shopping to know how it's cost for the food is and then, you know, running out as a man as a manager in small restaurant being 20 years in restaurant business. I work only for a restaurant in in my turn 20 years. So each restaurant is just like, okay, the way you work. So we don't want you to go anywhere, anywhere. So I respect all the worker to work because the same thing I did because I've been past before. I know what they go through what they had working a day, everything like that. So that's why I just like a feel, okay, I have to get my heart to them to feeling them to support them earn the trust and earn they like, you know, keep them with me to make sure it's like, you know, I trust you, I be on your side, I understand what happened to you. I understand. I try to help what I can see right now every portion in the restaurant like last time you you came to the restaurant and you say, where is it going? People just like, oh, she might be in the kitchen. So that's what I have to like, okay, let's go through with that any position we need to fill up.  Still dishwasher. My first job. I still do that right now too. No, and you're right because when I came in the last time, they said that you were in in the back cooking to really help with what was happening because basically every table was taken and and you were just, I mean, you do it all. Well, that's what make a restaurant running. So you have to can feel every position you can to helping them to get successful from, you know, on business they're doing the work they're doing done. You cannot like, oh, I don't want to do that or single cost with a single point to everybody. You do it and you will they will see it's like, oh, she do it. Well, we have to do it too. One of my boss teaching me, you're telling me and say, hey, remember one thing is you have one head, two hands and two legs. You can do it. Different person can do it the same way too. Yeah. No, I like that you lead by example if something needs to be done. I mean, you do it and that sets the standard for all of your team members to want to do it. And Koi, I want to ask you besides COVID, what are some challenges that you deal with with your business? Oh, yeah. So challenge for us is how can we make person staff to be on the same team challenge to make everybody is professional and food cost in Hawaii everybody know is high food cost for us. How can we do to make a food cost is like, you know, low down and still serve on a good quality for customer, you know, and we're looking for a coflat working with the corporate to making succinct food on the name of brand name of restaurant business, you know, and creating culture between front of the house staff and background house staff have to be in same team together. That's what, you know, we have to do together to make succinct food on a business. Yeah. And it's it's a never ending job.   When you when you create this up and you solve the problem, this is another problem coming up another challenge coming up for you to to create to learn to to learn and to solve the problem. That's that's what it is, you know, on a business. Uh huh. Yeah, you're you're so right about that.  How how would you describe your leadership style? How can I subscribe leadership style. So I earn trust for everyone. And I play a communication have to be sure between back in the house on the house or everybody. Anything have to be adapting to work like this person coming as a host, but she good with something else like a food brand. We have to exchange that on this person when I be bartender, but they're good with customer service. We have to moving people around. So I, for my thing, I would say like, you know, I do trust with them and then team trust the team, give them a fair, you know, fell.  I mean, that that's for sure. I like how you said trust and and really you're what you're saying too is you're trying to put your people in the in the right positions to really make use of their strengths, right? Yes, that's right. Sometimes it's happened like, you know, we not we cannot find we just thought we can they can do that. But when the time coming, let's say, okay, I'm good on a on bartender, but the time coming, they not really go faster or doing way the way is maybe have to say, hey, let's try to be server, you can talk to the customer is the way better for you or another position. Sometimes I I still thinking my job as a manager, I want to go back to be server because I think I'm good on that and be working for that for 18 years. I'm good to sell customer like, can you try that? Why like you and try to, hey, try that, try these kind of food is matching with that. That's everybody is, you know, different, you know, different on the opinion different on the way they they got scale on that. And Koi, I want to ask you about my books. You have both of my books now and what are some concepts or principles that really stood out to you in my books? Important in communication means full, you know, full means on that working hard or come, especially miss communication and meet understanding, you know, that's very important on that. If you communicate with one people and they go communicate to another person. But what the first your communication to the second person get lost in translation, that is not support. It's not the way they are. Sometimes when you write it down, and when you talk, it's different, you know, that's make me misunderstanding and make a problem make is drama, everything. So for me, I would love to be direct to the person when I want to talk to not keep to like, Oh, talk to that person for me. I know is they might miss communication on that. Yeah. Yeah. And also concept, as I say, so you on concept on adapting teamwork, you know, under team work, you can do what's the goal for each other and the skill for each other with a different on that. Yes. I love hearing that you brought up the four misses. I mean, because that's so important to avoid those four misses so that you can, you know, continue on to achieve the goals that you want. And, and yeah, and Koi, what do you what do you feel some of the greatest leaders do because we've all been on teams before and we was good or bad. What do you feel some of the greatest leaders do on that is I think you have to show them what you can do. And by example, you know, that's what I think it should do and make sure you stop understanding your team is understanding any questions just asking to make sure to be clear. But that's a communication. Oh, that's, you know, thank you. No, I like that because you're, you know, you're saying that, you know, I mean, to have the open communication going both ways and then to really lead by example, right? Exactly. The lead by example you do, if you lead by example to say, why can't you do that? What about me? I can do that too, right? I'm just like, I don't think this is a good one. So we have to, we have to go, you know, we have to follow together. Yes. Yeah. Now, Koi, professionally or personally, what's a big adversity that you dealt with in your life? Oh, my God. You know, when we first married, we, I miss Carrie. That is so bad for me. But it's tired of a year to get right now, I have to pray for my son Owen. So that doesn't make me so happy right now.  It's from the family and parents, you know, and friends to helping me to get that. But Miss Carrie with the first child we should be really wanted, it's hard for me, so hard. Yeah. But I'm grateful right now. I'm great, the best child in the world for me. Yes. Oh, he's, he's such a great, great boy. And thank you for sharing that because there's so many other people in the world that, you know, have dealt with situations like, like you have. And Koi, I want to, I want to ask, what, what's the best advice you ever received? You know, as my parents, my dad is CEO on company. And he teaching me when it grew up, we already see how is dad dealing with all his worker, all his staff. Mom have to be helping dad on coordinate together, which is okay. Enjoy to bring the people come over to get them to know them. And my parents is still advise me on a business job on, including my husband also, you know, we talk on the work a lot on that. We know what we should deal with, how we're going to deal with the thing, you know, one time my dad came to visit me in San Francisco five years after I'm living in San Francisco.  We have everything for you. You have a house, you have a car, I find a job for you.  And that's just like I'm saying, wow, I'm surprised that because I'm only one daughter and you're for you. And he say, you have to go, you have to do it. I trust you, I believe on you, you can do it. I have to say, right now, my mom's so proud of me, which is what I do, what I do. I'm successful on a business right now. Like I told you, this is you can do it, you can do it. Yeah, I think they saw, you know, how hard you work and the potential that you have and, and you know, for them to really keep pushing you forward like that.  Um, you know, I will say, what an important lesson I learned, I learned how to be strong, how to solve the problem myself, how to think forward, not think backward, think about future, think about positive, not negative or anything.  Well, fan change that on the, in the, what we're doing, try to do the best we can, you know, on that learn the problem before how we're going to solve the problem in the future if happen like this, what is we going to do? You know, remember and do that. Yeah, because like you're saying it, it's a mindset and we all have a choice to really choose to be positive or choose to be negative, right? Exactly. Exactly. That's right. So we can choose to be negative or positive, but if you choose to be negative, it's in your mind and you just like, I cannot let it go. If you think positive, okay, fine, we'll do it the best next time, we'll do it better.  I would say, yeah. I agree with you because I always tell, told my players, hey, let's only do things that's going to help us win because there's so many people that do things that help them lose. And you know, it common sense is, hey, let's only do things that help you win. And Koi, I want to really thank you for joining me on the show today and really sharing your insights about why you're a successful leader and why Noi Thai cuisine is thriving. So really thank you for taking time today. No problem. Thank you so much for having me though. Thank you so much. And I'll see you soon. And thank you soon. Yes. Thank you. I'll see you soon too. Thank you, Koi. And thank you for watching Beyond the Lines on ThinkTak Koi. For more information, please visit RustyKomori.com and my books are available on Amazon and Barnes and Noble. I hope that Koi and I will inspire you to create your own superior culture of excellence and to find your greatness and help others find theirs. Aloha.


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Then without further ado, Lauren is going to present this afternoon. Lauren is from the National Academy of Science and she's going to talk about from flood risk to flood resilience. So Lauren. Thank you. Thank you. Can everyone hear me? I'm told I have to stay in a certain zone here. So I'm Lauren Alexander Augustine from the National Academy of Sciences, Engineering and Medicine. That is the whole name. It rolls right off my tongue and I run the programs on risk and resilience there. And so I know this morning we talked a lot about flood risk and flood hazard and I'm going to talk a little bit about connecting all of that to flood resilience. So we have questions. Let me know. Al tells me we're not short on time so we can be nice and relaxed. Okay so this is a talk in three acts and you guys can keep track of how much longer you have to sit there and listen to me based on one which act we're in. So just some introductory stuff that you probably will already know how we frame resilience and then by extension how to frame flood resilience and then part three some next steps we're taking and knitting all of this together and that's a pretty exciting big project we have in mind. Okay so we'll get started. Like I said part one start your clocks. So I am a wetland hydrologist and so for the longest time when I was just a child in graduate school this was my full worldview of a flood and I did my PhD dissertation work on the forest and wetlands of the Chesapeake Bay and we were always on a river because that's where a flood occurred right and then later well this was actually earlier but you get the point you start thinking about big rivers flooding. 91 Mississippi is behaving itself 93 not so much but this is what we learned about about in terms of flooding. We get a little bit more sophisticated and FEMA says absolutely right it is a river that floods and when it floods you're either in or you're out of a flood zone. This is stuff that made sense in my narrow geometric worldview. Then I open my eyes a little bit and it turns out that there's other kinds of flooding like the ocean sometimes comes on to the land in different ways and we have coastal flooding and this is a FEMA map obviously and even the Great Lakes are considered coastal flooding right and then wow now I'm working at the National Academies as a full adult. Katrina happens I'm like oh this is coastal flooding. Now we have a coastal event right and then New Jersey hits Sandy hits few years later and then a few days ago we had another version of coastal flooding on a set of rivers North Carolina right and then you start to look at other kinds of floods because at the National Academies we have to be fulsome in how we take on these things and you start to get blue sky flooding it's not even raining and it's flooding sea level rise and nuisance flooding and in Annapolis not far from where I live but where Victoria lives it these days of being wet when it's not even raining are increasing. Two years ago Ellicott City in Maryland just south of Baltimore had this enormous rainstorm just sit on top of it and so now we have extreme precipitation adding to our flooding mix and when I say extreme we're talking about six inches in two hours this is a lot of rain in a small area in a very short period of time and just as a little editorial comment Ellicott City was a mill city and so it was designed to channel the river so that the mills could grind a flower or whatever they were grinding so when you get this kind of rain in the extreme situation in a place that's meant to channel water to be quick and powerful but that was built a hundred years ago that hundred year infrastructure does what it falls apart and Ellicott City had a really bad July of 2016 and too bad for Ellicott City they had a really bad August in 2018 two years later then there's other kinds of flooding this is stuff that has nothing to do with the river at all it might come up through your basement it might come through a pipe not a river overflowing but it's still a flood FEMA doesn't call it a flood but if it's your basement you know it was a flood so we could ask questions all the time like how big is this problem and what do we do about it well the hint on this big question we really don't know so this question that David asked earlier about what are we going to do what's the action need to figure out how to answer this question once we figure out what size this problem is and we can start to design solutions for moving on but what we do know is that the shape of the impacts of floods are changing they are evolving they are becoming much more complicated and they touch many more different parts of life like older people people are living longer and when they get to a certain retirement age if they have enough money where they move to the coast rate now we have people who are who might not be easy to move around living in coastal communities and they're older we're gonna talk a lot about how much money these things cost but it's expensive people are moving to the coast there's all kinds of stuff going on so these risks are not separate they are connected in certain ways and they are evolving so we're almost done with the introductions all right the Academy is those of you who don't know we are not part of the federal government we were chartered by the president Lincoln way back in the Civil War but we work really closely with the fence but we are an independent nonprofit and I always have to say that because then people think I'm from the NSF and they ask me for a grant we never give out money so just to get that straight okay what's that what did you say I'm a sink we'll take any money you have to give on by the way our core belief at the Academy is that we are trying to use science to the best of our ability and our collective ability to benefit society that's it right and so I do this in the risk and resilience space for those of you interested act one is now done okay all right so in terms of framing resilient who here wants to be resilient see all the hands go up see just like see this is what always happens everyone's like yes and then I say well who knows how to become resilient everyone's like well it's a little harder I'm like oh but we have someone here who knows but this is a harder question right what is it it's really fuzzy I'm gonna spend the next seven hours talking about what resilience is I'm I'm not gonna do that but this question these two questions together people want to be resilient and don't know how to get there and so we're trying to fill that gap in 2012 we produced this report this report's now getting long in the tooth but some people where I work say it's an old book good to use whatever adage you want it set out these four kind of pillars if you're gonna do resilience gotta do four things and know what the risk is you have to understand your risk you have to be able to communicate your risk before you can manage your risk there's a bunch of stuff around risk you can't do it alone FEMA's great and they're really good when things really hit the fan but it's not a FEMA problem you know if it's your basement it's your problem I can tell you that or your county or your state or your company so it's about partnerships and there's a lot of stakeholders involved and I'll talk a little bit about that if you're gonna invest in something you probably want some guidelines as to whether you're making progress this is around measures or indicators of resilience and then the fourth thing that came out of this 2012 report was it's about sharing information and this last one when the report was written in 2012 it was about sharing data so a lot of the stuff that we see around the walls here and the work that we've done since it's about sharing stories lessons learned past approaches all kinds of information all right so apparently this has some animation to it because this is not the slide it's supposed to look coming out of this people came to me and they said we totally want you to help us implement this report and I was like listen we're the national academies all we do is give advice you can take it or leave it but we're not doing anything with it and then the president said Lauren you're gonna do something with it so we created the resilient America program oh wow there it is there's a map and this schematic I was corrected many times this is not a map so schematic call this my happy map and you can see that if you are a federal agency and you're looking out over the continental United States you have a lot of stuff going on right so we picked four communities for the resilient America work and we worked with those communities for four to five years we'll ask four to five years on building their resilience Seattle the central central Puget Sound region Tulsa, Oklahoma Cedar Rapids Iowa and Charleston South Carolina I want to say a couple things about Charleston because when we started this whole process we thought people were interested in natural disasters it turns out they weren't but you live and learn more scientists and part of the process of discovery and Charleston was selected because it is subject to 19 different natural hazards it's almost anything you can think of except for sandstorms and volcanoes that's it everything else can happen in Charleston so be careful where your vacation and so when we got to Charleston I told you guys I'm a hydrologist and so it hurricanes blah blah blah flooding came up and all the people in my round table and the experts we bring together were very excited it coastal people riverine people estuarine people everyone's like yes flooding in Charleston and it just turns out there's a lot of ways to get wet in Charleston but Charleston didn't care they were like we just don't want to get wet we want you to keep the wet stuff away from the way that we live our lives which is interrupting our economic development our tourism that was it and that was the big transition from the flood risk to the flood resilience work so when we frame flood resilience we also use four dimensions I do not have a thing with the number four these things out this way but there's this physical dimension of flooding which a lot of this is around the room there's this information piece of flooding how do you know how how are your data how are your maps how are your models there's a big piece about information which goes almost directly to risk communication right then there's a social dimension who gets wet how bad how much and then who doesn't get wet there's a big social piece here and then the last one is about decision-making and when you're talking about flooding it goes all the way from the individual all the way up to the global flooding partnership so who's making decisions and for whom and it's not as straightforward as it seems that's a big body of work I'm gonna touch on these just a little bit going forward but the big question that we ask at every step is what is the role of science in these dimensions so physical dimensions we have so much information on physical dimensions our meteorology is great anyone here in here a meteorologist anyone here helping with thank you meteor it's great we get little alerts the rain's gonna come at 243 it'll be light though you know it's just great and we use it everyone uses Steve Zuberk from NOAA gave us this extreme precipitation oh it's extreme this is also a part of our physical dimension of flooding as is infrastructure as is aging infrastructure and this is a sound wall in Houston Texas now what do you think this sound wall does when it rains in Houston Texas it doesn't just trap it it channels it right so some of these building choices one and on an unintended consequence for another this is a stolen image I want to thank the New York Daily news for letting me borrow it without permission this is Baltimore and it's a sinkhole in Baltimore infrastructure some of it's massive this is also Houston the great project braze is big US Army Corps project five hundred million dollars of federal funds some number of that is gonna be matched by state funds and it's still not done not gonna be done for another four or five years started in 1974 so what's the role of science for this physical dimension this these two maps are are great so this comes out of flood map this comes out of a FEMA flood FEMA site but this uses USGS data NASA data and I think NOAA data all of this goes into this so here's the role of science how do we take these different layers of things are important topography and and elevation and water level and bring it together because this tells a different story than just a topo map right social dimensions let me just tell you that people take flooding personally and we were in Houston which has anyone here from Houston before I start talking about Houston our Houston has an amazing hubris when it comes to their engineering talent and they should they should be very proud of their engineering feats and we had a guy who was head of public works talking about how great their infrastructure was in Houston and we had this group of you know NGOs and whatever and they were just like your infrastructure doesn't help us and it was almost a fistfight I've worked at the Academy a long time I've never had to break up a fixed this fight I was worried that day so the social dimension this question about who gets flooded is huge it is just huge Chicago anyone here from Chicago here from Chicago the big tarp project right what part of Chicago from north side so the north side that served first right back in 1976 South side of Chicago where black people live is still not served and these people live on the south side and guess what they are furious they're mad today this was the spring this fall I lost track of time is recently that's me actually right there but I'm furious huge project interesting project it doesn't serve all equally the social dimensions are huge this poor guy he's having a bad day but then look at who doesn't flood right so this is a house in Houston it's a sizable house nice green lawn perfectly manicured and look it's for sale and what does it say didn't flood not here now it may not be true but that neighborhood or at least that house didn't flood so what's the role of science here in this dimension anyone here familiar with the social vulnerability in vex in index so v right well we have lots of smart people and people who know so v back and forth and what they did for us in Houston here they said okay listen we're gonna do some interviews we're gonna bring in the social science piece of this and we're gonna layer what we actually heard with the census tract data that's the under that's the undergirding of so v and we got personalized social vulnerability indexes for some of these select cities this when we gave it back to that stakeholder group in Houston it made them feel a lot better they're like I can get this now what it doesn't do is it doesn't go to the parcel level so they can't find their house but that's also where science can come in all right so we can say I don't think I have a slide on this but I go all over the country and the people who are most impacted tend to be poor they tend to be brown tan black they tend to not speak English as their native language they tend to be elderly or mentally ill disenfranchise are the ones most most impacted and how do we know this question of how do we know is where we need all the scientific grit that we can get we need all of the grist in this as well to answer this question because once we start making these assertions someone's gonna be like I don't like this answer it seems expensive it seems like you're calling me a name I don't want to be called it seems like you're saying my property isn't worth what I think it's worth so how do you know this is the most important question I think that we have for the flood risk to become flood resilience and this gets the information stuff and I just got to say oh my god there's so much data and information on floods it's we are swimming in it no pun intended and the hardest thing is sorting through it what's meaningful what's useful what's right what's wrong so no one knows what NFIP everyone knows what NFIP anyone think NFIP is perfect as is all right me neither but in many ways it's the best we have and the reason it's the best we have it's because it's the most consistent across the country there are holes in NFIP there are inequalities in NFIP there are problems with NFIP but at least the problems are relatively consistent from Maine to California and so my caveat on this slide and so if you can read these numbers the darkest red is from five billion to ten point six billion on the bottom for this ten year period and this is just the floor this it can't be any less than this and it is certainly more than this but no one knows how much more okay so this is one guess at at the information we have if you want to look at Houston after Harvey these numbers come down substantially 134 million right but it's just one city so it's still pretty big number our information tells us there's a line and you're either on one side of that line and you're safe and you're gonna be drying on your insurance or you're on the other side and you are wet and you're in really big trouble and you need and you need insurance so this is information we have it's just not necessarily the information that that we need this where is this this is this also Houston this is also Houston so what I like about this and it doesn't have a legend and for that I apologize but can you guys see the different colors there are three colors on this map there are the green dots there are the blue dots dark navy blue or maybe dark purple and then like the color of wine those dots the green ones are in the SFHA in the special flood hazard area the red ones are not this is Houston Texas and so you can say all you want about Houston but the Houstonians know this no matter where they live they know they're getting wet but they're buying flood insurance which is why so many of their payouts for the NFIP are outside of SFHA but what this also shows is that the firms the flood insurance rate maps that we use as floodplain maps from FEMA they don't really it's not quite it's almost arbitrary right so that's the information we have again Houston are you in or you out does it matter everyone got wet so all these dots all this money here's what's not included in that uninsured losses fluvial flooding is not really a part of it this is about daylighted rivers and coastal processes so if you're talking about you're on top of a culvert if you're talking about you have a basement you have seepage problems if you're sewage combined sewer stormwater system overflows that's not flooding that's not in those those claims right overland flow anyone lives in a flat place or a dry place or a boulder or Phoenix you had overland flow that's not in that seepage if you don't have a basement probably not your problem but when water comes from below that's not flooding combined sewers stormwater systems if you live in a city it's over a hundred years old you probably have combined stormwater systems right for those of us from the east coast that's most of us and what's also not included is the people who is in the who who might be in the way of that water the poor the brown the tan the non-english speaking and the older people so that's what's not included when I said that NFIP payout is like the base it is the absolute floor so here the picture is just simply incomplete we don't know how much information we don't know the flooding picture not the United States which is really kind of interesting when you consider of all the natural hazards you flood every year at seven to nine billion dollars every year and we don't know much about it that's where we are so the role of science here is to help create a more complete picture is to complete that puzzle who's going to flood when how badly how deep how much is going to cost what they should expect what's driving it etc. All right the last part of the framing of Act 2 is this decision-making and I am going to just be very brief on this because this is a whole hour-long lecture on its own but when it comes to flooding there's a role for everybody everyone has a responsibility it's barely a FEMA problem it's barely a NOAA problem it's like it's all no man's land so it's feds do have a role state agencies have a role local government I mean everyone has something private sector etc. which is just like herding cats okay so you want to say we need flood mitigation work who's let the game we played earlier you know I was at a table and someone's like okay I paid the preparedness charge the insurance rate whatever and it didn't flood I'm not paying this right it's all over so that's that alright we're in the final section this is the shortest and so you guys can wake back up and pretend you were awake the whole time okay so next steps in taking action last year Harvey Harvey our friend came to Texas and he was very unwelcome as you know largest rainfall in US history in some places over almost 65 inches now in some ways Harvey did us all a great favor because if you can dump 65 inches on a city that really can't move the water to begin with and they can't clear a two inch rainstorm every single afternoon in August right in some ways it does a favor to raise awareness that if it happened there you can dump two feet of water where you live and that's still a problem and it didn't didn't do us a favor because then people say well that was a 5,000 year event and it's never gonna happen again and that doesn't help um total damage this is a fictional number but again it's probably the floor okay 125 billion I have no idea if that's right but it's make believe money at that level so what difference does it make it's a lot of money it hit Houston damages were driven by wind so it was a hurricane even though it wasn't a hugely strong winded hurricane my NOAA and my NASA friends can correct me on that if I'm wrong but surge was big the pluvial flooding was big and the river flooding was big there was water everywhere and then of course the infrastructure didn't really work and there were some reservoir problems and then more water came and so this was a disaster which is an absolute mess and the response was commensurate or I seen this but again I just can't get over how many colors there are in Houston more struck by the fact that almost everyone has insurance I get this worse older Colorado there would be so so many fewer dots fewer people but fortunately like no one buys it except for in Texas okay who was impacted now I want to anyone know what IA is individual assistance you know who gets IA who gets individual assistance you have to qualify but you have to be poor you have to have it be under a certain financial threshold so you think about that who got impacted there was a lot of people who were in this low income not indigent because they're not property owners right so low income property owners or renters qualified for IA this is a really sad picture for Houston all right so now the rain thankfully has stopped it's a year later here are all the stakeholders there is more than this but when you think about who does what you know you have everyone has to dig in their pocket and pay for something thankfully and the role of the federal agencies is that they pay the most that's the way it should be that's the way we're structured we can't handle it that's for the feds Army Corps FEMA HUD they take DOT they take the biggest ones that's where most of the public assistance goes to and then HUD is the big one for long-term recovery you guys already know that but there's a bunch of other roles to go along so this is where everyone in Texas is now and so now a program starts to take shape from all of that if you can believe it and we're calling it the pilot program on flood resilience in southeastern Texas only because we are that creative with our naming maybe we'll get a better name as we get started here so who what where why when I mean I just run through this really quickly the why what problem are we trying to address here two big things what Harvey demonstrated was that there was this the maps that they had were inadequate and there's no indictment in that statement I am not saying anyone did anything wrong I'm just saying that the maps they had were inadequate and so people were making decisions on outdated inaccurate or incomplete information and that created a whole different set of problems that's the problem we're trying to solve how do we get better information into the hands of people who have to make good have to make hard decisions in times of chaos right that's why this is in strip everything else bear this is what we're trying to do this is the role of science benefiting society okay the what what are we doing we want to get like I'm looking around the room and I'm so excited about all that you guys can do with maps and data because we want to get a more integrated way to visualize the risk hazards and the impacts and this goes to not just the physical stuff the physical stuff we have down pat but who's in the way can we take cdbg data community development block grant data which is tabular in form all the time can we turn that into spatial data because if you can if we can then we have a new level of demographics to get at who's in the way of this one census tract data only go so far because it's it's it's it's broad maybe even at the parcel scale here so we want to get new maps and other visualizations to be able to communicate the hazard the risk and the impacts of flooding and the southeastern Texas part is Houston Harris County Beaumont and Port Arthur those are the four places so desired outcomes and we haven't started we're starting in January we want new partnerships you guys are all scientists you already know that we get along in a bumpy kind of way and we like to talk to ourselves those who understand this best the federal agencies really get along in a bumpy kind of way so there's partnerships that need to be nurtured enhanced along the way that's really important at the right people and we just want to also increase the understanding of what these different dimensions can look like in south in southeastern Texas because what happens in southeastern Texas might apply to Florida, Alabama, South Carolina there's a lot here that's applicable to other places you want strong communication of risk around flood better foundations for decision making and there's a piece here about getting the information from the community as a scientist as a Harvard trained person I've been taught that you know we should we should tell people what they need to know because we know but there's a piece that we need to understand from people who have lived through the floods fear the floods where is the impact that we can't get that granularity so it's also about the information from the community as well as information to expected outputs these are outcomes and what are we going to do we are going to produce things maps and other visuals that get these four dimensions we were talking about really want to get at these different sources of flooding do the fluvial sources look different than the riverine ones and I can tell I can tell you right now the answer is yes how I'm not sure but yes impact and then we have to write some stuff so we'll have some few written reports along the way but we're looking forward to having an annual conference on this and it'll start in Texas and hopefully it can go other places you know here's an idea and I'm not advocating this but you know this is the kind of thing that we hope it can not look like but channel perhaps you know this is a parcel scale interactive set of maps it's like Zillow for your real estate but it's on flood so you know do you get flooded how many post flooding events this one's interesting because it's not about how many times it flood with the current owner it's how many times did that parcel flood and in Texas when the property changes over that number goes to zero right so you sell me your house flooded twice with you but I buy it's never flooded with me so someone comes to me and they say is your house but I say no never flooded flooded once oh yeah flooded once but now it's really three times and I sell it to Alan you know I mean so here is an account of how many times the parcel has played stuff like that I mean this is imperfect but wouldn't it be nice to be able to have that on your phone or something like that so we're looking at bringing in AI lots of layers of mapping remote sense data all kinds of stuff to build these kinds of tools so who's involved so the state of Texas came in with four million dollars of spendable money no anyone here work in a soft money environment or academia so this is actually like six and a half million dollars and they cut the indirect some give us for FEMA came in with a match and then we have other partners who are coming in so this is gonna be like a $10 million project this is non-trivial I don't know if you could do it for cheaper and then we're going to do it over about four years so that's who involved who's involved that's about the scale of it those numbers might change but that's about the scale of it and that's kind of the scale it takes four years is by the minimum amount of time so four years and we're beginning in January so stay tuned on that it is the end of chapter of Act 3 I'm gonna say thank you and take any questions oh yeah I know I mean listen that's just kind of showing us an example I'm not you're right there's I don't like what was it in 2014 and literally under the cover of darkness the Congress rolled back the bigger water so bigger waters change NFIP to go from they kind of took out the grandfather clauses it went to a risk based system right and then two years later Congress was like what Louisiana what no I mean it does exactly what you're saying no suddenly my property is not now it's not insurable and it does flood and if we have a risk-based pricing system what does that do to coastal Louisiana what does it do to New Orleans right there's a huge pushback and under the cover of darkness on a rider that was attached to a transportation budget I'm not kidding I know this because I got 17 calls at 8 o'clock in the morning the next day this is where they turned it back and it was called like the homeowners insurance fairness some what was it yeah right so absolutely what you're saying is right there's a lot of sensitivity around it I'm not exactly sure about the interactive piece on the phones but this level of layering of that kind of information it might not be available to the public it might just be available to first responders we haven't gotten there yet because we haven't really started but the end user piece is huge like who the partners will be who it's for is a really big piece but your points and excellent oh yeah it's fraught it's fraught there's no question that it's fraught that's right and on the privacy bit if that's in all in any way attached and if IP privacy stuff that's a whole that's a whole different vat of trouble so there yeah there it is fraught the trick is the parcel scale and maybe it's a neighborhood scale but the granularity of flood impact is literally I mean it's not it's just not uniform you know I mean so houses flood next to houses that don't so which ones are those and why is that I will be here tomorrow and that's right that's right so we should exchange cards I don't mind strangers at all but you're not like a creeper right just kidding I'm just kidding I was just kidding mom yeah we should I mean it does there and the thing is I think that this big project there is some pieces there are some pieces that might need to be created but there's mostly it's about knitting stuff together that kind of already exists and seeing you know what that seem what that seamstress work is at the at the nexus that I think is where we're gonna end up to and you're right there's there's tons there's there's a lot out there so I think that it's a matter of collecting integrating layering sorting so thank you maybe one more question and then we're gonna move on to the next part if there is another question I didn't mean to sit you all down okay then having done Charleston now looking at Houston what kinds of things do you anticipate being different think about the size is non-trivial the just the difference in size is non-trivial Charleston is 200,000 people this area we're talking about is 7 million you know I mean 6 million 7 million people that's non-trivial and then the other big difference is that FEMA's flood risk map software kind what is your name to Albert's point like they really want to improve that desperately want to improve it and and so there's a there's just built-in friendliness actually between the state of Texas and the federal agencies we're working with that we never enjoyed in Charleston that South Carolina was particularly suspicious of anyone from Washington and they got comfortable with us because I was like we're not part of the federal government and finally they were like well thank God but you know DHS is and FEMA is and NOAA is so no where they were okay with because they have a they have a office in Charleston so they're okay with me I mean but I think those two things will be very big differences and I think that this is the scale on the size and the freshness of Harvey as a memory art is the other thing you know I mean up until last week the biggest thing that happened in Charleston since Hugo in 1989 was an ice storm that took out a bridge you know they were like we had an ice storm it took out a bridge it took out a couple bridges and it was like a really big disruption but 1989 Hugo was like yeah I wasn't here I didn't live here I wasn't born you know there's stuff like that so I think that the that the that the front of mind awareness of flooding in Houston is always right in before Harvey it's right here all the time and I think that that's a huge difference than all right thank you very much Lauren my pleasure


Okay, so this happened today. Well, two hours ago at most. Anyways, me and my friend were looking through games on Roblox, and then a little while later He messaged me on Discord that he got a friend request from a weird person called Minasmole. He sent me an image of it for proof. Later, I got a friend request from the same guy. I freaked out and messaged him that I got the same friend request that he did. He did not believe me, so I sent an image to him. And when I did he freaked out as well. We were both very nervous to accept it, but then I said, let's accept it at the same time. And so we did. When we did, nothing happened, but then a few minutes passed and then he messaged me. Hi. Then I replied back saying, how did you find me and my friend's user? He did not reply until a few minutes and he said back, make you and your friend join my game. He obviously did not answer my question, but then I messaged my friend and told him to join his game. My friend listened. When we joined his place, he wasn't there. But me and my friend noticed that the place was a very old Roblox style place. Me and my friend were nervous, but we stayed in the place because we wanted to figure stuff out. We were still very confused because the old starter place was removed very long ago. But we still went along with it since we didn't want to cause any trouble. But then the game crashed. It gave me the chills for some odd reason, but still. I thought it was a glitch. Then me and my friend joined back. And when we did, he was there. Me and my friend questioned him asking him questions like, why did you tell us to join? And stuff like that. I wasn't asking him anything though because, yeah I just wanted to stay silent. Then he said, come into the house with me. And we did. Then he said, you are the ones I have been looking for. We were both freaked out and then he said, have fun. After he said that, he immediately left the game. And then the old house set ablaze. After that, me and my friends had our PCs crashed. And I turned it back on. I went straight to Roblox. My friend was already on as well. And then Menace Mall was offline. And that was it. Neither me or my friend has heard from him since. And we both hope that we do not. You can still join his game though, but I don't want to. Despite what just happened to me and my friend.