Storage articles

Looking for something else? Check the categories of Storage:

Apache Avro Apache Cassandra Apache Hudi Apache Iceberg Apache Parquet Apache ZooKeeper Delta Lake Elasticsearch Embedded databases HDFS MySQL PostgreSQL Time series

If not, below you can find all articles belonging to Storage.

Table file formats - Change Data Capture: Delta Lake

It's time to start the 4th part of the Table file formats series. This time the topic will be Change Data Capture, so how to stream all changes made on the table. As for the 3rd part, I'm going to start with Delta Lake.

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Table file formats - reading path: Apache Hudi

After Delta Lake and Apache Iceberg it's time to see the reading part of Apache Hudi. Despite an apparent similarity with the aforementioned table formats, Apache Hudi has an interesting reading specificity related to the different table types.

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Table file formats - reading path: Apache Iceberg

Last week you could read about data reading in Delta Lake. Today it's time to cover this part in Apache Iceberg!

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Table formats - reading: Delta Lake

In the previous blog post about Delta Lake you discovered the logic for the writing part. Meantime Delta Lake 2 was released and it's for this brand new version that I'm going to share with you some findings related to the data reading.

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ACID file formats - writing: Delta Lake

It's time for the last data generation part of the ACID file formats series. This time we'll see how Delta Lake writes new files.

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ACID file formats - writing: Apache Iceberg

Last time you discovered data writing in Apache Hudi. Today it's time to see the 2nd file format from my list, Apache Iceberg.

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ACID file formats - writing: Apache Hudi

It's only when I was preparing the 2nd blog post of the series that I realized how bad my initial plan was. The article you're currently reading had been initially planned as the 6th of the series. But indeed, how could we understand more advanced features without discovering the writing path first?

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Data+AI Summit follow-up post: Why RocksDB rocks?

One reason why you can think about using a custom state store is the performance issues, or rather unpredictable execution time due to the shared memory between the default state store implementation and Apache Spark task execution. To overcome that, you can try to switch the state store implementation to an off-heap-based one, like RocksDB.

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Data+AI follow-up: Introduction to MapDB

Since there are already 2 Open Source implementations for RocksDB state store, I decided to use another backend to illustrate how to customize the state store in Structured Streaming. Initially, I wanted to try with Badger which is the store behind DGraph database but didn't find any Java-facing interface and dealing with the Java Native Interface or any other wrapper, was not an option. Fortunately, I ended up by finding MapDB, a Kotlin-based - hence a Java-facing interface - embedded database.

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ALTER DEFAULT PRIVILEGES in PostgreSQL

At first glance, managing users access in PostgreSQL is easy, you simply execute a CREATE USER, give him some grants, assign a role, and often that's all. However, after some time "permission denied" errors can appear as new objects are created and not owned by the user. To mitigate the maintenance burden for that case, PostgreSQL proposes ALTER DEFAULT privileges operator.

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Resources and metrics in Gnocchi

Data processing in Gnocchi is strongly related to the index information. One of such valuable assets are metrics and resources, covered just below.

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Cleaning old measures in Gnocchi

The specificity of Gnocchi is the precomputation of the measures. It doesn't allow ad-hoc queries but in the other side provides pretty good reading performance. However, as new time series points are coming, the old ones aren't kept with them.

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Archive policy in Gnocchi

In the recent posts about Gnocchi we could often meet the concept of archive policy. However, as one of the main points in this system, it merits its own explanation.

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Reading aggregates in Gnocchi

Gnocchi writes data partitioned by split key. But often such splitted data must be merged back for reading operations. This post focuses on "how" and "when" of this process.

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Sacks - data parallelization unit in Gnocchi

To facilitate parallel processing Apache Spark and Apache Kafka have their concept of partitions, Apache Beam works with bundles and Gnocchi deals with sacks. Despite the different naming, the sacks are the same for Gnocchi as the partitions for Spark or Kafka - the unit of work parallelization.

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Carbonara storage format

Even though carbonara is mostly known as an Italian pasta dish, in the context of Gnocchi it means completely different thing. Carbonara is the name of time points storage format in Gnocchi.

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Horizontal scalability in Gnocchi

One of the reasons behind the choice of Gnocchi as time series database to study was its naturally provided horizontal scalability. At the moment of making that choice I was relying only on the official documentation. Now it's a good moment to come back and analyze the horizontal scalability by myself.

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Gnocchi architecture

Understanding the architecture is the key of working properly with any distributed system. It's why the series of post about Gnocchi starts by exploring its components.

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Choosing time-series database for study

In order to learn a new thing, nothing better than try it. However in some cases the choice of the tool to study is not easy. It's especially true in the context of data storage and though also in the context of time-series databases introduced in one of previous posts.

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Time series - general notes

Temporal data is a little bit particular. It can be generated very frequently, as for instance every 500 ms or less. It's then important to store it efficiently and to allow quick and flexible reads. It's also important to know the specificities of time-series as a popular case of temporal data.

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