OmnisciOnNative execution

OmniSciDB is an open-source SQL-based relational database designed for the massive parallelism of modern CPU and GPU hardware. Its execution engine is built on LLVM JIT compiler.

OmniSciDB can be embedded into an application as a dynamic library that provides both C++ and Python APIs. A specialized in-memory storage layer provides an efficient way to import data in Arrow table format.

OmnisciOnNative execution uses OmniSciDB for both as a storage format and for actual data transformation.

Relational engine limitations

Using a relational database engine implies a set of restrictions on operations we can execute on a dataframe.

1. We cannot handle frames that use data types not supported by OmniSciDB. Currently, we allow only integer, float, string, and categorical data types.

2. Column data should be homogeneous.

3. Can only support operations that map to relational algebra. This means most operations are supported over a single axis (axis=0) only. Non-relational operations like transposition and pivot are not supported.

When the unsupported data type is detected or unsupported operations is requested it falls back to the original pandas framework.

Partitions

In Modin, partitioning is used to achieve high parallelism. In the case of OmniSciDB-based execution, parallelism is provided by OmniSciDB execution engine itself and we don’t need to manage multiple partitions. OmnisciOnNativeDataframe always has a single partition.

A partition holds data in either pandas.DataFrame or pyarrow.Table format. pandas.DataFrame is preferred only when we detect unsupported data type and therefore have to use pandas framework for processing. In other cases pyarrow.Table format is preferred. Arrow tables can be zero-copy imported into OmniSciDB. A query execution result is also returned as an Arrow table.

Data Ingress

When users import data in Modin DataFrame (from a file or from some python object like array or dictionary) they invoke one of the modin.pandas.io functions (to read data from a file) or use DataFrame constructor (to create a DataFrame from an iterable object). Both of the paths lead to the FactoryDispatcher that defines a factory that handles the import query. For OmnisciOnNative execution, the factory is accordingly ExperimentalOmnisciOnNativeFactory. The factory dispatches the import query: if the data needs to be read from a file - the query is routed to the OmnisciOnNativeIO class, that uses Arrow Framework to read the file into a PyArrow Table, the resulted table is passed to the DFAlgQueryCompiler. If the factory deals with importing a Python’s iterable object, the query goes straight into the DFAlgQueryCompiler. The Query Compiler sanitizes an input object and passes it to one of the OmnisciOnNativeDataframe factory methods (.from_*). The Dataframe’s build method stores the passed object into a new Dataframe’s partition and returns the resulted Dataframe, which is then wrapped into a Query Compiler, which is wrapped into a high-level Modin DataFrame, which is returned to the user.

Note that during this ingress flow, no data is actually imported to the OmniSciDB. The need for importing to OmniSci is decided later at the execution stage by the Modin Core Dataframe layer. If the query requires for the data to be placed in OmniSciDB, the import is triggered. OmnisciOnNativeDataframe passes partition to import to the OmnisciOnNativeDataframePartitionManager that extracts a partition’s underlying object and sends a request to import it to the OmniSci Server. The response for the request is a unique identifier for the just imported table at OmniSciDB, this identifier is placed in the partition. After that, the partition has a reference to the concrete table in OmniSciDB to query, and the data is considered to be fully imported.

Data Transformation

When a user calls any DataFrame API, a query starts forming at the API layer to be executed at the Execution layer. The API layer is responsible for processing the query appropriately, for example, determining whether the final result should be a DataFrame or Series object, and sanitizing the input to the DFAlgQueryCompiler, e.g. validating a parameter from the query and defining specific intermediate values to provide more context to the query compiler.

The DFAlgQueryCompiler is responsible for reducing the recieved query to the pre-defined Dataframe algebra operators and pass their execution to the OmnisciOnNativeDataframe.

When OmnisciOnNativeDataframe recieves a query it determines whether the operation requires data materialization or can be performed lazily. Depending on that the operation is either appended to a lazy computation tree or executed.

Lazy execution

OmniSciDB has a powerful query optimizer and an execution engine that combines multiple operations into a single execution module. E.g. join, filter and aggregation can be executed in a single data scan.

To utilize this feature and reduce data transformation and transfer overheads, all of the operations that don’t require data materialization are performed lazily.

Lazy operations on a frame build a tree which is later translated into a query executed by OmniSci. Each of the tree nodes has its input node(s) - a frame argument(s) of the operation. When a new node is appended to the tree, it becomes its root. The leaves of the tree are always a special node type, whose input is an actual materialized frame to execute operations from the tree on.

There are two types of trees. The first one describes operations on frames that map to relational operations like projection, union, etc. Nodes in this tree are derived from DFAlgNode class. Leaf nodes are instances of the FrameNode class. The second type of tree is used to describe operations on columns, including arithmetic operations, type casts, datetime operations, etc. Nodes of this tree are derived from BaseExpr class. Leaf nodes are instances of the InputRefExpr class.

Visit the corresponding sections to go through all of the types of nodes:

• Frame nodes

• Expression nodes

Execution of a computation tree

Frames are materialized (executed) when their data is accessed. E.g. it happens when we try to access the frame’s index or shape. There are two ways to execute required operations: through Arrow or through OmniSciDB.

Arrow execution

For simple operations which don’t include actual computations, execution can use Arrow API. We can use it to rename columns, drop columns and concatenate frames. Arrow execution is preferable since it doesn’t require actual data import/export to the OmniSciDB.

OmniSciDB execution

To execute query in OmniSciDB engine we need to import data first. We should find all leaves of an operation tree and import their Arrow tables. Partitions with imported tables hold corresponding table names used to refer to them in queries.

OmniSciDB is SQL-based. SQL query parsing is done in a separate process using the Apache Calcite framework. A parsed query is serialized into JSON format and is transferred back to OmniSciDB. In Modin, we don’t generate SQL queries for OmniSciDB but use this JSON format instead. Such queries can be directly executed by OmniSciDB and also they can be transferred to Calcite server for optimizations.

Operations used by Calcite in its intermediate representation are implemented in classes derived from CalciteBaseNode. CalciteBuilder is used to translate DFAlgNode-based trees into CalciteBaseNode-based sequences. It also translates BaseExpr-based trees by replacing InputRefExpr nodes with either CalciteInputRefExpr or CalciteInputIdxExpr depending on context.

CalciteSerializer is used to serialize the resulting sequence into JSON format. This JSON becomes a query by simply adding ‘execute relalg’ or ‘execute calcite’ prefix (the latter is used if we want to use Calcite for additional query optimization).

The building of Calcite query (starting from the conversion to the Calcite Algebra and up to the forming JSON query) is orchestrated by OmnisciOnNativeDataframePartitionManager.

An execution result is a new Arrow table which is used to form a new partition. This partition is assigned to the executed frame. The frame’s operation tree is replaced with FrameNode operation.

Rowid column and sub-queries

A special case of an index is the default index - 0-based numeric sequence. In our representation, such an index is represented by the absence of index columns. If we need to access the index value we can use the virtual rowid column provided by OmniSciDB. Unfortunately, this special column is available for physical tables only. That means we cannot access it for a node that is not a tree leaf. That makes us execute trees with such nodes in several steps. First, we materialize all frames that require rowid column and only after that we can materialize the root of the tree.

OmnisciOnNative Dataframe Implementation

Modin implements Dataframe, PartitionManager and Partition classes specific for OmnisciOnNative execution:

• OmnisciOnNativeDataframe

• OmnisciOnNativeDataframePartition

• OmnisciOnNativeDataframePartitionManager

To support lazy execution Modin uses two types of trees. Operations on frames are described by DFAlgNode based trees. Scalar computations are described by BaseExpr based tree.

• Frame nodes

• Expression nodes

Interactions with OmniSci engine are done using OmnisciServer class. Queries use serialized Calcite relational algebra format. Calcite algebra nodes are based on CalciteBaseNode class. Translation is done by CalciteBuilder class. Serialization is performed by CalciteSerializer class.

• CalciteBaseNode

• CalciteBuilder

• CalciteSerializer

• OmnisciServer

Column name mangling

In pandas.DataFrame columns might have names not allowed in SQL (e. g. an empty string). To handle this we simply add ‘F_’ prefix to column names. Index labels are more tricky because they might be non-unique. Indexes are represented as regular columns, and we have to perform a special mangling to get valid and unique column names. Demangling is done when we transform our frame (i.e. its Arrow table) into pandas.DataFrame format.