It uses advanced techniques to sample data, collect statistics on data and queries, and build machine-learning models to model memory usage, network load, and execution time. MySQL Autopilot automates many of the most important and often challenging aspects of achieving high query performance at scale-including provisioning, data loading, query execution, and failure handling. Oracle MySQL Autopilot: machine-learning–based automation For OLTP queries, HeatWave has the same performance as Amazon Aurora and is 2.4X less cost, resulting in 2.4X better price performance. Using industry standard CH-benCHmark on a 100 GB dataset for OLAP queries, HeatWave is 18X faster, provides 110X better throughput, and 2.4X less cost than Amazon Aurora, resulting in 42X better price performance. For such workloads too, MySQL HeatWave is much faster and costs a fraction of Amazon Aurora.
Most real world applications have a mix of OLTP and complex OLAP queries. 5400X faster than Amazon RDS at two-thirds the cost, resulting in 8100X better price performanceįaster mixed workload than Amazon Aurora at a fraction of the cost.1400X faster than Amazon Aurora at half the cost, resulting in 2800X better price performance (4 TB TPC-H).3 times faster than Azure Synapse at one-fifth the cost, resulting in 15X better price performance (30 TB TPC-H).9X faster than Google BigQuery at one-fourth the cost, resulting in 36 times better price/performance (30 TB TPC-H).6.8X faster than Amazon Redshift with AQUA at half the cost, resulting in 13X better price performance (10 TB TPC-H).6.8X faster than Snowflake on AWS at one-fifth the cost, resulting in 35X better price performance (10 TB TPC-H).Results from other benchmarks are here: Faster analytics than Snowflake, Amazon Redshift, Amazon Aurora, Amazon RDS, Google BigQuery, and Azure Synapse at a fraction of the cost See the 30 TB TPC-H benchmark results shown above. This has been demonstrated by multiple standard industry benchmarks like TPC-H, TPC-DS, CH-benCHmark-and on real-world customer workloads. MySQL HeatWave is faster than all the competing database services- and at a fraction of the cost. Let’s say I want to store all docker-related messages in /var/log/docker/.Ĭreate /etc/rsyslog.d/docker.Faster than Amazon and Snowflake at a fraction of the cost In this step, we are going to create a dedicate log config for docker. Step 2: Configure rsyslog to filter docker log messages The next step is to tell rsyslog to store messages like this at a different location. Now, let’s run a docker container and check the log message:Īs you can see, we ran an nginx container and view the content of /var/log/syslog and sure enough, the new log format is there with image name and container’s ID. You can see the list of parameters you can passed in here. So, create/edit your /etc/docker/daemon.json file and paste the following content: is the ID of the container (short ID, 12 chars).
#Toad mysql client free driver
We are going to change this default behavior by specifying a different log driver for docker, which is rsyslog. json file inside the containers folder (default at /var/lib/docker/containers/). Step 1: Configure docker loggingīy default, docker containers’ log is written to a. Configure docker to log to a custom location using rsyslog.So, after reading this tutorial, you can: That includes dockerd logging and containers’ logging. The desired goalīased on a requirement from a client, they want to store all docker logging to a custom file location. However, the following configurations helped me achieve my goal. I, by no mean, say that this is the ultimate guide for docker & rsyslog configuration. If you have problem configure Docker logging with rsyslog, this post may provide a quick solution.
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