2017-11-09

Aiven PostgreSQL plans are now larger and faster with local SSDs



Aiven PostgreSQL plans are now larger


As part of our ongoing efforts to improve our service according to client feedback, we've increased the disk space for all Aiven PostreSQL plans. 

Depending on plan type, we've increased disk space anywhere from 12% to 75%. Check out the table below to see improvements according to plan type:


Plan Previous storage Improved storage
Startup/business/premium-4 50 GB 80 GB
Startup/business/premium-8 100 GB 175 GB
Startup/business/premium-16 200 GB 350 GB
Startup/business/premium-32 400 GB 700 GB
Startup/business/premium-64 800 GB 1000 GB
Startup/business/premium-120 1200 GB 1400 GB
Startup/business/premium-160 1600 GB 1800 GB
Startup/business/premium-240 2400 GB 2800 GB



New Aiven PostgreSQL service plans will come with the increased disk space while existing service plans will receive the increases during their next upgrades.

We can also provide even greater disk space upon request. If you're interested, feel free to contact our sales for more information!

For those of you who are running your PostgreSQL services on Amazon Web Services (AWS) and Google Cloud Platform (GCP), the news is even better.


Aiven supports local SSDs in AWS and GCP


In fact, we are the first DBaaS to offer such support. 

Specifically, we will use PCIe NVMe local SSDs starting from our Startup/ Business/Premium-8 plans in GCP and Startup/Business/Premium-16 plans in AWS.

So why is this important?

Our initial tests have demonstrated up to a 400% increase in performance when using local SSDs over network-based SSDs. 

To learn more, view our benchmarking presentation that we gave at this year's PostgreSQL Conference Europe.

Currently only AWS and GCP have support for these but we hope to extend it to other cloud providers in the future when they have the required support.

We will be following up later on with a longer form blog post that will go into more detail. 


Increase your PostgreSQL performance with Aiven


We are constantly striving to be the first to offer features and updates that will markedly improve the performance of the services you run with us.

That is why we made sure to be the first to provide local SSDs in our GCP and AWS plans, as well as release production-ready PostgreSQL 10.

If you are a current client, we'd like to thank you for working with us to improve industry standards. If you aren't a client yet, give us a try!

Trying Aiven is free and comes with no commitments. Start your trial today and receive $10 worth of test credits.  


2017-10-19

Aiven talks shop with Paf at second Apache Kafka meetup

In August, Aiven created an Apache Kafka meetup in Helsinki to discuss hot topics surrounding Kafka.

Due to its popularity, we decided to make it an ongoing event and held our second meetup yesterday.

This time, around 30 people attended Lifeline Ventures' office in downtown Helsinki and there were two presenters:
  1. Niklas Nylund of Paf, an operator of slots, lotteries, poker and casino games as well as betting both online and in casinos.
  2. Heikki Nousiainen, our CTO
First up was Nylund to discuss the ins and outs of how his team brought Kafka in to act as a data bus for their real-time analytics needs.

Kafka reduces the spaghetti  


Paf collects Change Data Capture events from databases and sends the events to Kafka, which are then consumed and imported into Kudu for analytic work.

Although Kafka possesses its quirks, Nylund is satisfied with how Paf has been able to use it to streamline their architecture.

Or, as he vividly put it, "Reducing the spaghetti."

Check out his presentation slides here to get a thorough understanding of Paf's integration process from beginning to end.

Kafka Connect simplifies integration


Second up was our CTO to discuss Kafka Connect framework, and using it to transfer data between Kafka and other systems, in this case
PostgreSQL and Elasticsearch.

Using Python code to interact with the services, Nousiainen was able to demonstrate how easy it was to push and pull data between Kafka and the external systems.

With a large number of available connectors from the Kafka community, integrating Kafka with other systems can be quite straightforward.

This in turn allows quick benefits and migration towards real-time stream analytics with Kafka-centric architecture.

Check out Nousiainen's presentation slides to get a better idea of the use cases for Kafka Connect.

Join the next Kafka discussion  


As the transition from a monolithic to microservices architecture continues, the use case for integrating Kafka as a streaming platform will only strengthen. 

This is evidenced by the increase in attendance of our events where many developers, be they users of Kafka or not, are gathering to learn more about what Kafka is, its use cases, and best practices for implementing it.

We are planning another Aiven Kafka meetup for December/January timeframe, so join the Helsinki Apache Kafka Meetup group to get the details when we finalize the plans and we'll see you soon!

2017-10-11

Aiven is the first to offer PostgreSQL 10

Get Aiven PostgreSQL 10
We've got great news: PostgreSQL 10 is now available at Aiven on all major clouds!

That means that you can now access it on AWS, Google, Azure, UpCloud, and DigitalOcean clouds. Worldwide.

As with every PostgreSQL release, many of your older queries will simply run faster because of the many performance enhancements. But, why is PostgreSQL 10 significant?

As the 28th major update of the past 30 years, its primary focus is on improving the distribution of massive amounts of data across many nodes...let's look at some specifics.


PostgreSQL 10: the specifics 


Improved support for parallel queries 


Now, many more scan types are supported and can benefit from parallelization.

Depending on your query, newly added scan types such as parallel index scan and bitmap heap scan can speed it up immensely.

Also, merge joins are now a supported parallel join type in addition to other join types already supported in the previous release, such as hash joins and nested loop joins.


    Declarative partitioning support. 


    While you could create partitioning schemes by directly using constraints, inheritance and triggers in past versions of PostgreSQL...

    ...you can now use simple definitions to create your partitioning setup with PostgreSQL 10

    Even better, the performance of the new partitioning code is vastly improved over older methods.


      Hash indexes


      PostgreSQL 10 brings crash-safe hash index support that also performs far better than before. 

      Now, you can consider using hash indexes when your queries just need to check for equivalence to increase performance.


        Native logical replication 


        PostgreSQL 10 now brings proper support for logical replication in PostgreSQL itself. 

        Logical replication allows replication between different PostgreSQL versions, finally allowing for zero downtime upgrades to future versions. 

        You can also migrate data to and from environments where you don't have access to streaming replication.


        Easily test PostgreSQL 10 with Aiven today  


        With Aiven, you can create a full copy of the data within your existing PostgreSQL service as a new separate PostgreSQL 10 service.

        By forking, you can keep your existing PostgreSQL services as-is while testing the latest version for compatibility with your applications.

        And don't worry, it won't negatively affect the performance of your source service; it just provides an easy and efficient way to test PostgreSQL 10. So, let's get started.




        Cheers,
        Team Aiven

        P.S. For a full list of features please see the full PostgreSQL 10 release notes.

        2017-10-02

        Stanford SSI uses Aiven InfluxDB in high-altitude

        Aiven goes to near space with InfluxDB

        A new day in space development 


        Historically, space development has remained the sole purview of government agencies due to its astronomical cost: that has changed. 

        Today, new technologies are reducing those costs, allowing smaller groups with big ambitions to take small steps and giant leaps in pushing space development forward. 

        One such group is Stanford SSI, a project-based student group that covers everything from high-altitude balloon platforms, to cube satellites, all the way through to rockets. 

        In fact, they recently broke the world record for the longest duration flight by a latex balloon with the launch of SSI-52...a pretty big achievement for a group only established in 2013. 


        What does Aiven have to do with balloons? 


        These aren’t your everyday balloons, but sophisticated platforms carrying complex scientific payloads into near-space and over tremendous distances: the stakes are a little higher. 

        Stanford SSI uses its ValBal balloon platform to reach altitudes as high as 120,000 feet while testing cutting-edge electronics and mechanics. 

        Their tests produce a massive amount of data, data that needs to be sent back. Kai Marshland, their Operations Lead, explains it best, 


        “...we want to store a highly variable set of data, analyze it over time, and manipulate it with minimal latency...Aerospace demands the utmost in quality, and that’s exactly what Aiven provides.” 


        In short, Aiven’s capabilities provide an ideal fit. For our part, we think it’s pretty cool to test our technology in demanding, high-altitude research flights. 


        Aiven tests InfluxDB at the edge of space 


        For latest launch on September 30, 2017, SSI-59, Stanford SSI used our InfluxDB service for their Database as a Service needs. 

        The goal for this launch? SSI-59 will use the same ValBal platform to test lighter and more efficient mechanics and avionics that should increase the platform’s endurance. 

        But most importantly, it’s the communications system that they are most excited to test, which Kai describes as “Revolutionary.” Here’s why, 


        "Having high-bandwidth communications means that we no longer have to worry about recovering the payload over the vast areas ValBal can fly over." 

        For instance, it will allow them to fly a radar glaciology payload over Greenland to measure the thickness of its ice sheets and transmit the data it collects almost instantaneously back to where they launched the payload from...no more worrying about recovering the platform.


        We operate in the cloud, but reach for the stars 


        Just one of many, SSI-59 is part of a long-term aim to redefine the high-altitude balloon research world, one that will provide a better understanding of the planet we live on. 

        This is the first time that Stanford SSI will be using our technology for their data needs and will provide an excellent use case for demonstrating the capabilities of our technology. 

        But, the idea that our technology can be applied to space development is most thrilling. After all, Aiven may be a database cloud service provider, but we don’t mind flying above them.

        2017-08-23

        Kafka Users and Access Control

        We're happy to announce user and topic level access controls for Aiven Kafka service. You can now create multiple users with separate access credentials each, and control produce and consumer privileges on user and topic basis.

        Both users and access control lists can be managed on the Aiven Console under the Users tab on the service details page.


        Managing users


        All users and the user specific access certificate and key are listed and available on the Users tab. The password is usable with Kafka REST service.

        You can add users with the Add service user... button or remove existing users with Remove...

        Reset password... button on the right both resets Kafka REST password as well as revokes and recreates access key and certificate for the specific user.


        Managing Access Control Lists


        Access Control Lists manage user privileges to consume from or produce to a topic. 


        Users can either be explicit users, or user masks with wildcard characters * and ?. Star matches a string of characters, question mark matches any single character in it's place.

        Similarly, topics can be specified as explicit topics as well as wildcard matches.

        Grants can be either Produce, Consume or Full Access for both.

        By default, the access is allowed for all configured users to both produce and consume on all topics. You can delete ACL entries on row by row basis.



        Give Aiven services a whirl

        Remember that trying Aiven is free: you will receive US$10 worth of free credits at sign-up which you can use to try any of our service plans. The offer works for all of our services: PostgreSQL, Redis, InfluxDB, Grafana, Elasticsearch and Kafka!

        Go to https://aiven.io/ to get started!

        Cheers,
        Team Aiven

        2017-06-20

        Kafka Connect Preview

        We're delighted to announce public preview for Kafka Connect support for Aiven Kafka. During preview, Kafka Connect is available at no extra cost as part of all Aiven Kafka Business and Premium plans. We're launching with support for Elasticsearch connector, and will soon follow with S3 and other connectors.

        Kafka Connect


        Kafka Connect is a framework for linking Kafka with other services. It makes it simple to define and configure connectors to reliably and scalably stream data between different systems. Kafka Connect provides a standard API for integration, handles offset management and workload distribution automatically.

        You can define and configure individual connectors via the Kafka Connect REST interface.

        Case example - IoT Device Shadow


        A customer of ours is using Aiven Kafka for capturing telemetry from a fleet of IoT devices. To that end, Aiven Kafka has proven to be a scalable and flexible pipeline for capturing and distributing traffic for processing.

        During the past month, we've worked together to support a new use case: maintaining a "device shadow" or a latest state update in Elasticsearch. This copy allows developers to query and access device states regardless whether the devices are currently online and connected or not.

        We built this new pipeline together with Kafka Connect and Elasticsearch Connector. You can follow these steps to set up a similar pipeline.

        Getting started: Launching Kafka and Elasticsearch services


        Create Aiven Kafka service and create your topics for the incoming traffic. In this example, we'll be using Business-4 plan for the service and 16 partitions to accommodate for the client load.

        First we'll launch a Kafka cluster from the Aiven web console. This cluster will receive the state updates from the IoT devices. A fairly low-spec cluster will work for this use case and we will launch it in one of the AWS regions:




        Next, we'll create a Kafka topic for our data under the Topics tab.

        We chose 16 partitions in this example, but you should select a number that matches with your workload. A larger number allows for higher throughput to support, but on the other hand increases resource usage on both the cluster as well as the consumer side. Contact us if unsure, we can help you to find a suitable plan.



        We will also need an Elasticsearch cluster for the device shadow data. We'll choose a three-node cluster with 4 GB memory in each node. Make note of the Elasticsearch Service URL, which we'll use with the Kafka Connector configuration in the next steps.



        We'll need to enable Kafka Connect by clicking the "Enable" button next to it in the service view. We also make a note of Kafka Connect access URL, which we will need in the following steps.


        Setting up the pipeline with scripts


        We'll be using a couple of Python code snippets to configure our data pipeline. We've downloaded the project and Kafka access certificates as ca.pem, service.cert and service.key to a local directory from the Kafka service view.

        You can refer to startup guides for both Aiven Kafka and Aiven Elasticsearch for details on setting up the environment.

        Here's our first snippet named query_connector_plugins.py for finding out the available connector plugins:

        import requests
        
        AIVEN_KAFKA_CONNECT_URL = "https://avnadmin:m9jyevsaehezqs36@gadget-kafka.htn-aiven-demo.aivencloud.com:22142"
        
        response = requests.get("{}/connector-plugins".format(AIVEN_KAFKA_CONNECT_URL))
        print(response.text)
         
         
        By running the script we can find out the available connector plugins:
         
        $ python3 query_connector_plugins.py
        [{"class":"io.confluent.connect.elasticsearch.ElasticsearchSinkConnector"}]
         

        To get started with the pipeline configuration, we'll pre-create an Elasticsearch index with a schema to meet our needs with script name create_elastic_index.py:

        import json
        import requests
        
        AIVEN_ELASTICSEARCH_URL = "https://avnadmin:in9zvfjaio32m0qy@gadget-elastic.htn-aiven-demo.aivencloud.com:24185"
        
        mapping = {
            "settings": {
                "number_of_shards": 16
            },
            "mappings": {
                "kafka-connect-gadget-telemetry": {
                    "properties": {
                        "location": {
                            "type": "string"
                        },
                        "temperature": {
                            "type": "integer"
                        },
                        "timestamp": {
                            "type": "date"
                        }
                    }
                }
            }
        }
        
        response = requests.put(
            "{}/gadget-telemetry?pretty=true".format(AIVEN_ELASTICSEARCH_URL),
            headers={"content-type": "application/json"},
            data=json.dumps(mapping),
            verify="ca.pem",
        )
        
        print(response.text)
        
        
         
        Next, we'll run the script and the Elasticsearch index is created:

        $ python3 create_elastic_index.py
        {
            "acknowledged" : true,
            "shards_acknowledged" : true
        }
         
         
        Here's how we create and configure the actual Elasticsearch Connector to link our telemetry topic and Elasticsearch with a script named create_es_connector.py:

         
        import requests
        import json
        
        AIVEN_KAFKA_CONNECT_URL = "https://avnadmin:m9jyevsaehezqs36@gadget-kafka.htn-aiven-demo.aivencloud.com:22142"
        AIVEN_ELASTICSEARCH_URL = "https://avnadmin:in9zvfjaio32m0qy@gadget-elastic.htn-aiven-demo.aivencloud.com:24185"
        
        connector_create_request = {
            "name": "gadget-es-sink",
            "config": {
                "connection.url": AIVEN_ELASTICSEARCH_URL,
                "connector.class": "io.confluent.connect.elasticsearch.ElasticsearchSinkConnector",
                "tasks.max": 3,
                "topics": "gadget-telemetry",
                "type.name": "kafka-connect-gadget-telemetry"  # This points to the created ES mapping
            }
        }
        
        response = requests.post(
            "{}/connectors".format(AIVEN_KAFKA_CONNECT_URL),
            headers={"Content-Type": "application/json"},
            data=json.dumps(connector_create_request)
        )
        print(response.text)
        
        
        And enable the Connector by running the script:

         
        $ python3 create_es_connector.py
        {"name":"gadget-es-sink","config":{"topics":"gadget-telemetry", "type.name":"kafka-connect-gadget-telemetry", "tasks.max":"3", "connector.class":"io.confluent.connect.elasticsearch.ElasticsearchSinkConnector", "connection.url":"https://avnadmin:in9zvfjaio32m0qy@gadget-elastic.htn-aiven-demo.aivencloud.com:24185", "name":"gadget-es-sink"}, "tasks":[]}
        


        Next, we're going to send some simulated telemetry data to test everything out:

        from kafka import KafkaProducer
        import datetime
        import json
        import random
        
        AIVEN_KAFKA_URL = "gadget-kafka.htn-aiven-demo.aivencloud.com:22144"
        LOCATIONS = ["arizona", "california", "nevada", "utah"]
        
        producer = KafkaProducer(
            bootstrap_servers=AIVEN_KAFKA_URL,
            security_protocol="SSL",
            ssl_cafile="ca.pem",
            ssl_certfile="service.cert",
            ssl_keyfile="service.key",
        )
        
        for i in range(10):
            device_name = "gadget_{}".format(i)
            telemetry = {
                "location": random.choice(LOCATIONS),
                "temperature": random.randint(40, 120),
                "timestamp": datetime.datetime.utcnow().strftime("%Y-%m-%dT%H:%M:%SZ")
            }
            key = device_name.encode("utf-8")
            payload = json.dumps(telemetry).encode("utf-8")
            producer.send("gadget-telemetry", key=key, value=payload)
        
        # Wait for all messages to be sent
        producer.flush()
        print("Done, sent {} messages".format(i))
        
        $ python3 submit_telemetry.py
        Done, sent 10 messages
        

        Exploring the data with Kibana

        All of our Elasticsearch plans include integrated Kibana, which can be a handy tool for exploring and/or visualizing the data too. We can easily verify that our telemetry is flowing all the way to our Elasticsearch instance.

        Clicking the Kibana link under the Elasticsearch service information page opens a view to Kibana. We are greeted with a configuration page where we enter the name of our Elasticsearch index created in one of the earlier steps:



        Default discovery view on our sample data. The default view lists our entries. Since we're using keyed messages and the entry is always replaced with the latest entry, the timeline view will show only the timestamp of the last reception.


        Accessing data in Elasticsearch

        The real value of the new pipeline is realized with the ability to query for device information from Elasticsearch. In the Elasticsearch example query script (query_elasticsearch.py) below, we'll query for all devices that last reported from Arizona:

        import requests
        import json
        
        AIVEN_ELASTICSEARCH_URL = "https://avnadmin:in9zvfjaio32m0qy@gadget-elastic.htn-aiven-demo.aivencloud.com:24185"
        
        response = requests.get(
            "{}/_search?q=location:arizona&pretty=true".format(AIVEN_ELASTICSEARCH_URL),
            verify="ca.pem"
        )
        print(response.text)
        
        
        

        Running the script show the list of active gadgets in the target region:


        $ python3 query_elasticsearch.py
        {
            "took" : 7,
            "timed_out" : false,
            "_shards" : {
                "total" : 4,
                "successful" : 4,
                "failed" : 0
            },
            "hits" : {
                "total" : 2,
                "max_score" : 1.3862944,
                "hits" : [
                    {
                        "_index" : "gadget-telemetry",
                        "_type" : "kafka-connect-gadget-telemetry",
                        "_id" : "gadget_2",
                        "_score" : 1.3862944,
                        "_source" : {
                            "temperature" : 114,
                            "location" : "arizona",
                            "timestamp" : "2017-12-06T13:55:01Z"
                        }
                    },
                    {
                        "_index" : "gadget-telemetry",
                        "_type" : "kafka-connect-gadget-telemetry",
                        "_id" : "gadget_5",
                        "_score" : 1.2039728,
                        "_source" : {
                            "temperature" : 45,
                            "location" : "arizona",
                            "timestamp" : "2017-12-06T13:55:01Z"
                        }
                    }
                ]
            }
        }
        

        The above example is easily extended to query data by a certain temperature threshold, location or time of the last update. Or, if we want to check the state of a single device, we now have the latest state available by its ID.

        Summary

        In this example, we built a simple telemetry pipeline with Kafka, Kafka Connect and Elasticsearch. We used Elasticsearch connector, which is the first connector we support with Aiven Kafka. We'll be following up with S3 connector shortly with others to follow.

        Get in touch if we could help you with your business requirement!

        Give Aiven services a whirl

        Remember that trying Aiven is free: you will receive US$10 worth of free credits at sign-up which you can use to try any of our service plans. The offer works for all of our services: PostgreSQL, Redis, InfluxDB, Grafana, Elasticsearch and Kafka!

        Go to https://aiven.io/ to get started!

        Cheers,
        Team Aiven

        2017-05-31

        Larger Aiven Postgresql and Aiven Kafka plans in Azure


        New larger Aiven PostgreSQL plans in Azure

        We're happy to announce immediate availability of the larger 120GB and 160GB Aiven PostgreSQL plan tiers in Azure. These plans come with 1.2TB and 1.6TB storage capability respectively. These plans are available in the following regions: Australia East, Canada Central, Canada East, East Asia, East US 2, Japan West, North Central US, North Europe, UK South, UK West and West US.

        Plan Dedicated
        VMs
        CPUs per VM † Memory per VM † Storage per VM †
        Startup-120 1 16 120 GB 1200 GB
        Startup-160 1 32 160 GB 1600 GB
        Business-120 2 16 120 GB 1200 GB
        Business-160 2 32 160 GB 1600 GB
        Premium-120 3 16 120 GB 1200 GB
        Premium-160 3 32 160 GB 1600 GB
        Actual amounts may vary slightly between different Cloud providers.

        New larger Aiven Kafka plans in Azure


        We've included larger 32GB and 64GB plan tiers to our Aiven Kafka offerings in Azure with increased total storage, core counts and larger memory. These plans are immediately available in all Azure regions.
        Plan Cluster
        Nodes
        CPU per VM † Memory per VM † Total Storage † Data Retention
        Business-32 3 8 32 GB 4200 GB 12 weeks
        Business-64 3 16 64 GB 6000 GB 18 weeks
        Premium-32 5 8 32 GB 8000 GB 20 weeks
        Premium-64 5 16 64 GB 10000 GB 30 weeks
        Actual amounts may vary slightly between different Cloud providers.

        Give Aiven services a whirl


        Remember that trying Aiven is free: you will receive US$10 worth of free credits at sign-up which you can use to try any of our service plans. The offer works for all of our services: PostgreSQL, Redis, InfluxDB, Grafana, Elasticsearch and Kafka!

        Go to https://aiven.io/ to get started!

        Cheers,
        Team Aiven