Spatial Network Big Databases. Queries & Storage Methods

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If there are two regions on a continent, the backup data remains on the same continent. Because there is only one region in Australia, backup data from the Sydney region is also stored in Asia. MySQL First Generation instances: Instance data and backup data are stored in the continent where the instance resides. PostgreSQL instances: Instance data is stored in the region where the instance resides. A zone is an independent entity in a specific geographical location where you can run your resources. For example, a zone named us-central1-a indicates a location in the central United States.

By default, data in MySQL First Generation instances is replicated, and the service is distributed across multiple zones to provide fault tolerance for zone outages. For MySQL Second Generation instances, fault tolerance across zones can be achieved by configuring the instance for high availability adding a failover replica. The high availability configuration is strongly recommended for all production instances. For more information about zones, see Zone Resources in the Compute Engine documentation.

What are the limits on storage? For information on storage limits, see Quotas and Limits. How is my data replicated? Second Generation read replicas use asynchronous replication. PostgreSQL instances provide a high availability configuration and read replicas. For information about failover, see Overview of the High Availability Configuration. Your data is encrypted using the bit Advanced Encryption Standard AES , or better, with symmetric keys: that is, the same key is used to encrypt the data when it is stored, and to decrypt it when it is used.

These data keys are themselves encrypted using a master key stored in a secure keystore, and changed regularly. For more details, see Encryption at Rest in Google Cloud. Google encrypts and authenticates all data in transit at one or more network layers when data moves outside physical boundaries not controlled by Google or on behalf of Google.

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Data in transit inside a physical boundary controlled by or on behalf of Google is generally authenticated but might not be encrypted by default. You can choose which additional security measures to apply based on your threat model. For more details, see Encryption in Transit in Google Cloud. For more information about read replicas, including use cases for each type, see Replication Options. To restore to a backup you can use the Google Cloud Platform Console or the gcloud command-line tool.

For more details, see Restoring an Instance. To restore a MySQL instance to a specific point in time, you use a point-in-time recovery. For more information, see Performing a Point-in-Time Recovery. They are charged at the backup storage rate. Binary logs use storage space not backup space , and are charged as storage. Binary log space counts toward the storage used in an instance. PostgreSQL instances: The most recent 7 automated backups, and all on-demand backups, are retained.

For more information about instance storage pricing and instance rates, see Pricing.

File Extensions and File Formats

You cannot decrease the size of the storage of your instance. You can also configure your instance to automatically increase its storage capacity when space is running low. Learn more. Note that this will force your instance to restart, which causes a short period of downtime. You can also specify whether an instance gets updates earlier or later than other instances in your project. Since your data is replicated in multiple locations, the interruption to your instances is typically a few seconds to a few minutes. First Generation instances configured to follow an App Engine application may also be restarted in a new location in order to minimize the latency from that application as it moves.

This may involve a short period of increased latency, and typically a few seconds of unavailability. We recommend that you design your applications to deal with situations when your instance is not accessible for short periods of time, such as in a maintenance shutdown. You can test the behavior of your application to a maintenance shutdown by restarting your instance , which has the same effect.

In general, we recommend that you use only short-lived connections as well as use exponential back-off for retrying rejected connections. For more guidance see How should I manage connections? Note that for MySQL instances, the amount of time mysqld has to shutdown is capped to 1 minute. If the shutdown does not complete in that time, the mysqld process is forcefully terminated.

This incurs a longer startup time because the InnoDB storage engine does a crash-recovery before the server is ready to start serving queries. The time for a crash-recovery to complete depends on the size of the database; larger databases require more time for recovery. When a new version starts to be rolled out, a note is added to the Release Notes. Take part in the discussion of this post on LinkedIn. Why not take de-normalisation to its full conclusion?

Get rid of all joins and just have one single fact table? Indeed this would eliminate the need for any joins altogether. However, as you can imagine, it has some side effects. First of all, it increases the amount of storage required. We now need to store a lot of redundant data.

What about other storage mechanisms?

With the advent of columnar storage formats for data analytics this is less of a concern nowadays. The bigger problem of de-normalization is the fact that each time a value of one of the attributes changes we have to update the value in multiple places - possibly thousands or millions of updates. One way of getting around this problem is to fully reload our models on a nightly basis. Often this will be a lot quicker and easier than applying a large number of updates. Columnar databases typically take the following approach. They first store updates to data in memory and asynchronously write them to disk.

When creating dimensional models on Hadoop, e. Hive, SparkSQL etc. When distributing data across the nodes in an MPP we have control over record placement. Based on our partitioning strategy, e. Have a look at the example below. This is very different from Hadoop based systems. One strategy of dealing with this problem is to replicate one of the join tables across all nodes in the cluster. This is called a broadcast join and we use the same strategy on an MPP. As you can imagine, it only works for small lookup or dimension tables.

So what do we do when we have a large fact table and a large dimension table, e. Or indeed when we have two large fact tables. In order to get around this performance problem we can de-normalize large dimension tables into our fact table to guarantee that data is co-located. We can broadcast the smaller dimension tables across all of our nodes. For joining two large fact tables we can nest the table with the lower granularity inside the table with the higher granularity, e.

Modern query engines such as Impala or Drill allow us to flatten out this data.


This strategy of nesting data is also useful for painful Kimball concepts such as bridge tables for representing M:N relationships in a dimensional model. Storage on the Hadoop File System is immutable. In other words you can only insert and append records. If you are coming from a relational data warehouse background this may seem to be a bit odd at first. However, under the hood databases work in a similar way.

They store all changes to data in an immutable write ahead log known in Oracle as the redo log before a process asynchronously updates the data in the data files. What impact does immutability have on our dimensional models? SCDs optionally preserve the history of changes to attributes.

Graph database

They allow us to report metrics against the value of an attribute at a point in time. This is not the default behaviour though. By default we update dimension tables with the latest values. So what are our options on Hadoop? We can simply make SCD the default behaviour and audit any changes. If we want to run reports against the current values we can create a View on top of the SCD that only retrieves the latest value. This can easily be done using windowing functions. Alternatively, we can run a so called compaction service that physically creates a separate version of the dimension table with just the latest values.

These Hadoop limitations have not gone unnoticed by the vendors of the Hadoop platforms. Based on the number of open major issues and my own experience, this feature does not seem to be production ready yet though. Cloudera have adopted a different approach. It gets rid of the Hadoop limitations altogether and is similar to the traditional storage layer in a columnar MPP. Having said that MPPs have limitations of their own when it comes to resilience, concurrency, and scalability.

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When you run into these limitations Hadoop and its close cousin Spark are good options for BI workloads. We all know that Ralph Kimball has retired. But his principle ideas and concepts are still valid and live on. We have to adapt them for new technologies and storage types but they still add value.

Teach me Big Data to Advance my Career. He frequently speaks at conferences. He is a traveler between the worlds of traditional data warehousing and big data technologies. Uli is a regular contributor to blogs and books and chairs the the Hadoop User Group Ireland. What do you need to convert? Is dimensional modeling dead? Why do we need to model our data? Why do we need dimensional models? Data Modelling vs Dimensional Modelling In standard data modelling we aim to eliminate data repetition and redundancy.

If the country changes its name we have to update the country in many places Note: Standard data modelling is also referred to as 3NF modelling. So why do some people claim that dimensional modelling is dead? As you can imagine there are various reasons for this. The Data Warehouse is dead Confusion First of all, some people confuse dimensional modelling with data warehousing. About the author.

Spatial Network Big Databases. Queries & Storage Methods Spatial Network Big Databases. Queries & Storage Methods
Spatial Network Big Databases. Queries & Storage Methods Spatial Network Big Databases. Queries & Storage Methods
Spatial Network Big Databases. Queries & Storage Methods Spatial Network Big Databases. Queries & Storage Methods
Spatial Network Big Databases. Queries & Storage Methods Spatial Network Big Databases. Queries & Storage Methods
Spatial Network Big Databases. Queries & Storage Methods Spatial Network Big Databases. Queries & Storage Methods
Spatial Network Big Databases. Queries & Storage Methods Spatial Network Big Databases. Queries & Storage Methods
Spatial Network Big Databases. Queries & Storage Methods Spatial Network Big Databases. Queries & Storage Methods
Spatial Network Big Databases. Queries & Storage Methods Spatial Network Big Databases. Queries & Storage Methods

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