micromegas-analytics 0.19.0

use crate::{
    lakehouse::blocks_view::BlocksView,
    metadata::find_process,
    metrics_table::metrics_table_schema,
    time::{TimeRange, datetime_to_scalar},
};

use super::{
    batch_update::PartitionCreationStrategy,
    block_partition_spec::BlockPartitionSpec,
    dataframe_time_bounds::{DataFrameTimeBounds, NamedColumnsTimeBounds},
    jit_partitions::{
        JitPartitionConfig, generate_process_jit_partitions, is_jit_partition_up_to_date,
        write_partition_from_blocks,
    },
    lakehouse_context::LakehouseContext,
    metrics_block_processor::MetricsBlockProcessor,
    partition_cache::PartitionCache,
    partition_source_data::fetch_partition_source_data,
    view::{PartitionSpec, View, ViewMetadata},
    view_factory::ViewMaker,
};
use anyhow::{Context, Result};
use async_trait::async_trait;
use chrono::{DateTime, TimeDelta, Utc};
use datafusion::{
    arrow::datatypes::Schema,
    logical_expr::{Between, Expr, col},
};
use micromegas_tracing::info;
use std::sync::Arc;
use uuid::Uuid;

const VIEW_SET_NAME: &str = "measures";
const SCHEMA_VERSION: u8 = 5;
lazy_static::lazy_static! {
    static ref TIME_COLUMN: Arc<String> = Arc::new( String::from("time"));
}

#[derive(Debug)]
pub struct MetricsViewMaker {}

impl ViewMaker for MetricsViewMaker {
    fn make_view(&self, view_instance_id: &str) -> Result<Arc<dyn View>> {
        Ok(Arc::new(MetricsView::new(view_instance_id)?))
    }

    fn get_schema_hash(&self) -> Vec<u8> {
        vec![SCHEMA_VERSION]
    }

    fn get_schema(&self) -> Arc<Schema> {
        Arc::new(metrics_table_schema())
    }
}

#[derive(Debug)]
pub struct MetricsView {
    view_set_name: Arc<String>,
    view_instance_id: Arc<String>,
    process_id: Option<sqlx::types::Uuid>,
}

impl MetricsView {
    pub fn new(view_instance_id: &str) -> Result<Self> {
        let process_id = if view_instance_id == "global" {
            None
        } else {
            Some(Uuid::parse_str(view_instance_id).with_context(|| "Uuid::parse_str")?)
        };
        Ok(Self {
            view_set_name: Arc::new(String::from(VIEW_SET_NAME)),
            view_instance_id: Arc::new(view_instance_id.into()),
            process_id,
        })
    }
}

#[async_trait]
impl View for MetricsView {
    fn get_view_set_name(&self) -> Arc<String> {
        self.view_set_name.clone()
    }

    fn get_view_instance_id(&self) -> Arc<String> {
        self.view_instance_id.clone()
    }

    async fn make_batch_partition_spec(
        &self,
        lakehouse: Arc<LakehouseContext>,
        existing_partitions: Arc<PartitionCache>,
        insert_range: TimeRange,
    ) -> Result<Arc<dyn PartitionSpec>> {
        if *self.view_instance_id != "global" {
            anyhow::bail!("not supported for jit queries... should it?");
        }
        let source_data = Arc::new(
            fetch_partition_source_data(
                lakehouse.clone(),
                existing_partitions,
                insert_range,
                "metrics",
            )
            .await
            .with_context(|| "fetch_partition_source_data")?,
        );
        Ok(Arc::new(BlockPartitionSpec {
            view_metadata: ViewMetadata {
                view_set_name: self.view_set_name.clone(),
                view_instance_id: self.view_instance_id.clone(),
                file_schema_hash: self.get_file_schema_hash(),
            },
            schema: self.get_file_schema(),
            insert_range,
            source_data,
            block_processor: Arc::new(MetricsBlockProcessor {}),
        }))
    }

    fn get_file_schema_hash(&self) -> Vec<u8> {
        vec![SCHEMA_VERSION]
    }

    fn get_file_schema(&self) -> Arc<Schema> {
        Arc::new(metrics_table_schema())
    }

    async fn jit_update(
        &self,
        lakehouse: Arc<LakehouseContext>,
        query_range: Option<TimeRange>,
    ) -> Result<()> {
        if *self.view_instance_id == "global" {
            // this view instance is updated using the deamon
            return Ok(());
        }
        info!("find_process");
        let process = Arc::new(
            find_process(
                &lakehouse.lake().db_pool,
                &self
                    .process_id
                    .with_context(|| "getting a view's process_id")?,
            )
            .await
            .with_context(|| "find_process")?,
        );

        //todo: use last_update_time in process
        let query_range =
            query_range.unwrap_or_else(|| TimeRange::new(process.start_time, chrono::Utc::now()));

        let blocks_view = BlocksView::new()?;
        let all_partitions = generate_process_jit_partitions(
            &JitPartitionConfig::default(),
            lakehouse.clone(),
            &blocks_view,
            &query_range,
            process.clone(),
            "metrics",
        )
        .await
        .with_context(|| "generate_process_jit_partitions")?;
        let view_meta = ViewMetadata {
            view_set_name: self.get_view_set_name(),
            view_instance_id: self.get_view_instance_id(),
            file_schema_hash: self.get_file_schema_hash(),
        };

        for part in all_partitions {
            if !is_jit_partition_up_to_date(&lakehouse.lake().db_pool, view_meta.clone(), &part)
                .await?
            {
                write_partition_from_blocks(
                    lakehouse.lake().clone(),
                    view_meta.clone(),
                    self.get_file_schema(),
                    part,
                    Arc::new(MetricsBlockProcessor {}),
                )
                .await
                .with_context(|| "write_partition_from_blocks")?;
            }
        }
        Ok(())
    }

    fn make_time_filter(&self, begin: DateTime<Utc>, end: DateTime<Utc>) -> Result<Vec<Expr>> {
        Ok(vec![Expr::Between(Between::new(
            col("time").into(),
            false,
            Expr::Literal(datetime_to_scalar(begin), None).into(),
            Expr::Literal(datetime_to_scalar(end), None).into(),
        ))])
    }

    fn get_time_bounds(&self) -> Arc<dyn DataFrameTimeBounds> {
        Arc::new(NamedColumnsTimeBounds::new(
            TIME_COLUMN.clone(),
            TIME_COLUMN.clone(),
        ))
    }

    fn get_update_group(&self) -> Option<i32> {
        if *(self.get_view_instance_id()) == "global" {
            Some(2000)
        } else {
            None
        }
    }

    fn get_max_partition_time_delta(&self, _strategy: &PartitionCreationStrategy) -> TimeDelta {
        TimeDelta::hours(1)
    }
}