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Qbeast-spark: DataSource enabling multi-dimensional indexing and efficient data sampling. Big Data, free from the unnecessary!

qbeast-spark 최신버젼 다운로드

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---

**Qbeast Spark** is an Apache Spark extension that enhances data processing in [**Data Lakehouses**](http://cidrdb.org/cidr2021/papers/cidr2021_paper17.pdf). It provides advanced **multi-dimensional filtering** and **efficient data sampling**, enabling faster and more accurate queries. The extension also maintains ACID properties for data integrity and reliability, making it ideal for handling large-scale data efficiently.

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</div>

## Features

1. **Data Lakehouse** - Data lake with **ACID** properties, thanks to the underlying [Delta Lake](https://delta.io/) architecture

2. **Multi-column indexing**:  **Filter** your data with **multiple columns** using the Qbeast Format.

3. **Improved Sampling operator** - **Read** statistically significant **subsets** of files.

4. **Table Tolerance** - Model for sampling fraction and **query accuracy** trade-off.

## Query example with Qbeast

| ![Demo for Delta format GIF](https://raw.githubusercontent.com/Qbeast-io/qbeast-spark/main/docs/images/spark_delta_demo.gif) | ![Demo for Qbeast format GIF](https://raw.githubusercontent.com/Qbeast-io/qbeast-spark/main/docs/images/spark_qbeast_demo.gif) |
|:---------------------------------------------------------------:|:---------------------------------------------------------------:|

As you can see above, the Qbeast Spark extension allows **faster** queries with statistically **accurate** sampling.

| Format | Execution Time |   Result  |
|--------|:--------------:|:---------:|
| Delta  |  ~ 151.3 sec.  | 37.869383 |
| Qbeast |   ~ 6.6 sec.   | 37.856333 |

In this example, **1% sampling** provides the result **x22 times faster** compared to using Delta format, with an **error of 0,034%**.

## Documentation
Explore the documentation for more details:
- [Quickstart for Qbeast-Spark](./docs/Quickstart.md)
- [Data Lakehouse with Qbeast Format](./docs/QbeastFormat.md)
- [OTree Algorithm](./docs/OTreeAlgorithm.md)
- [QbeastTable](./docs/QbeastTable.md)
- [Columns To Index Selector](./docs/ColumnsToIndexSelector.md)
- [Recommendations for different Cloud Storage systems](./docs/CloudStorages.md)
- [Advanced configurations](./docs/AdvancedConfiguration.md)
- [Qbeast Metadata](./docs/QbeastFormat.md)
- [FAQ: Frequently Asked Questions](./docs/FAQ.md)

# Quickstart
You can run the qbeast-spark application locally on your computer, or using a Docker image we already prepared with the dependencies.
You can find it in the [Packages section](https://github.com/orgs/Qbeast-io/packages?repo_name=qbeast-spark).

### Pre: Install **Spark**
Download **Spark 3.5.0 with Hadoop 3.3.4**, unzip it, and create the `SPARK_HOME` environment variable:<br />

>:information_source: **Note**: You can use Hadoop 2.7 if desired, but you could have some troubles with different cloud providers' storage, read more about it [here](docs/CloudStorages.md).

```bash
wget https://archive.apache.org/dist/spark/spark-3.5.0/spark-3.5.0-bin-hadoop3.tgz

tar -xzvf spark-3.5.0-bin-hadoop3.tgz

export SPARK_HOME=$PWD/spark-3.5.0-bin-hadoop3
 ```
### 1. Launch a spark-shell

**Inside the project folder**, launch a **spark shell** with the required dependencies:

```bash
$SPARK_HOME/bin/spark-shell \
--packages io.qbeast:qbeast-spark_2.12:0.7.0,io.delta:delta-spark_2.12:3.1.0 \
--conf spark.sql.extensions=io.qbeast.spark.internal.QbeastSparkSessionExtension \
--conf spark.sql.catalog.spark_catalog=io.qbeast.spark.internal.sources.catalog.QbeastCatalog
```

### 2. Indexing a dataset

**Read** the **CSV** source file placed inside the project.

```scala
val csvDF = spark.read.format("csv").
  option("header", "true").
  option("inferSchema", "true").
  load("./src/test/resources/ecommerce100K_2019_Oct.csv")
```

Indexing the dataset by writing it into the **qbeast** format, specifying the columns to index.

```scala
val tmpDir = "/tmp/qbeast-spark"

csvDF.write.
  mode("overwrite").
  format("qbeast").
  option("columnsToIndex", "user_id,product_id").
  save(tmpDir)
```

#### SQL Syntax.
You can create a table with Qbeast with the help of `QbeastCatalog`.

```scala
spark.sql(
  "CREATE TABLE student (id INT, name STRING, age INT) " +
    "USING qbeast OPTIONS ('columnsToIndex'='id')")

```

Use **`INSERT INTO`** to add records to the new table. It will update the index in a **dynamic** fashion when new data is inserted.

```scala
val studentsDF = Seq((1, "Alice", 34), (2, "Bob", 36)).toDF("id", "name", "age")

studentsDF.write.mode("overwrite").saveAsTable("visitor_students")

// AS SELECT FROM
spark.sql("INSERT INTO table student SELECT * FROM visitor_students")

// VALUES
spark.sql("INSERT INTO table student VALUES (3, 'Charlie', 37)")

// SHOW
spark.sql("SELECT * FROM student").show()
+---+-------+---+
| id|   name|age|
+---+-------+---+
|  1|  Alice| 34| 
|  2|    Bob| 36|
|  3|Charlie| 37|
+---+-------+---+
```

###  3. Load the dataset
Load the newly indexed dataset.
```scala
val qbeastDF =
  spark.
    read.
    format("qbeast").
    load(tmpDir)
```

### 4. Examine the Query plan for sampling
**Sampling the data**, notice how the sampler is converted into filters and pushed down to the source!

```scala
qbeastDF.sample(0.1).explain(true)
```
Go to the [Quickstart](./docs/Quickstart.md) or [notebook](docs/sample_pushdown_demo.ipynb) for more details.

### 5. Interact with the format

Get **insights** to the data using the `QbeastTable` interface!

```scala
import io.qbeast.spark.QbeastTable

val qbeastTable = QbeastTable.forPath(spark, tmpDir)

qbeastTable.getIndexMetrics()

```

### 6. Optimize the table

**Optimize** is an expensive operation that consist on **rewriting part of the files** to accomplish **better layout** and **improving query performance**.

To minimize write amplification of this command, **we execute it based on subsets of the table**, like `Revision ID's` or specific files.

> Read more about `Revision` and find an example [here](./docs/QbeastFormat.md).

#### Optimize API
These are the 3 ways of executing the `optimize` operation:

```scala
qbeastTable.optimize() // Optimizes the last Revision Available.
// This does NOT include previous Revision's optimizations.

qbeastTable.optimize(2L) // Optimizes the Revision number 2.

qbeastTable.optimize(Seq("file1", "file2")) // Optimizes the specific files
```

**If you want to optimize the full table, you must loop through `revisions`**:

```scala
val revisions = qbeastTable.revisionsIDs() // Get all the Revision ID's available in the table.
revisions.foreach(revision => 
  qbeastTable.optimize(revision)
)
```

Go to [QbeastTable documentation](./docs/QbeastTable.md) for more detailed information.

### 7. Visualize index
Use [Python index visualizer](./utils/visualizer/README.md) for your indexed table to visually examine index structure and gather sampling metrics.

# Dependencies and Version Compatibility
| Version |   Spark   |  Hadoop   | Delta Lake |
|-------|:---------:|:---------:|:----------:|
| 0.1.0 |   3.0.0   |   3.2.0   |   0.8.0    |
| 0.2.0 |   3.1.x   |   3.2.0   |   1.0.0    |
| 0.3.x |   3.2.x   |   3.3.x   |   1.2.x    |
| 0.4.x |   3.3.x   |   3.3.x   |   2.1.x    |
| 0.5.x |   3.4.x   |   3.3.x   |   2.4.x    |
| 0.6.x |   3.5.x   |   3.3.x   |   3.1.x    |
| **0.7.x** | **3.5.x** | **3.3.x** | **3.1.x**  |

Check [here](https://docs.delta.io/latest/releases.html) for **Delta Lake** and **Apache Spark** version compatibility.

# Contribution Guide

See [Contribution Guide](./CONTRIBUTING.md) for more information.

# License
See [LICENSE](./LICENSE).

# Code of conduct

See [Code of conduct](./CODE_OF_CONDUCT.md)

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