feat(query): Async query execution by returning early from long running queries

README.md

@@ -138,11 +138,15 @@ hotdata datasets create --url "https://example.com/data.parquet" --label "My Dat
 ## Query
 
 ```sh
-hotdata query "<sql>" [--workspace-id <id>] [--connection <connection_id>] [--format table|json|csv]
+hotdata query "<sql>" [-w <id>] [--connection <connection_id>] [-o table|json|csv]
+hotdata query status <query_run_id> [-o table|json|csv]
 ```
 
-- Default format is `table`, which prints results with row count and execution time.
+- Default output is `table`, which prints results with row count and execution time.
 - Use `--connection` to scope the query to a specific connection.
+- Long-running queries automatically fall back to async execution and return a `query_run_id`.
+- Use `hotdata query status <query_run_id>` to poll for results.
+- Exit codes for `query status`: `0` = succeeded, `1` = failed, `2` = still running (poll again).
 
 ## Saved Queries
 
@@ -163,13 +167,21 @@ hotdata queries run <query_id> [--format table|json|csv]
 ## Search
 
 ```sh
-hotdata search "<query>" --table <connection.schema.table> --column <column> [--select <columns>] [--limit <n>] [--format table|json|csv]
+# BM25 full-text search
+hotdata search "query text" --table <connection.schema.table> --column <column> [--select <columns>] [--limit <n>] [-o table|json|csv]
+
+# Vector search with --model (calls OpenAI to embed the query)
+hotdata search "query text" --table <table> --column <vector_column> --model text-embedding-3-small [--limit <n>]
+
+# Vector search with piped embedding
+echo '[0.1, -0.2, ...]' | hotdata search --table <table> --column <vector_column> [--limit <n>]
 ```
 
-- Full-text search using BM25 across a table column.
-- Requires a BM25 index on the target column (see `indexes create`).
-- Results are ordered by relevance score (descending).
-- `--select` specifies which columns to return (comma-separated, defaults to all). The `score` column is automatically appended when `--select` is used.
+- Without `--model` and with query text: BM25 full-text search. Requires a BM25 index on the target column.
+- With `--model`: generates an embedding via OpenAI and performs vector search using `l2_distance`. Requires `OPENAI_API_KEY` env var.
+- Without query text and with piped stdin: reads a vector (raw JSON array or OpenAI embedding response) and performs vector search.
+- BM25 results are ordered by relevance score (descending). Vector results are ordered by distance (ascending).
+- `--select` specifies which columns to return (comma-separated, defaults to all).
 
 ## Indexes
 

skills/hotdata-cli/SKILL.md

@@ -163,16 +163,21 @@ Use `hotdata datasets <dataset_id>` to look up the `table_name` before writing q
 
 ### Execute SQL Query
 ```
-hotdata query "<sql>" [--workspace-id <workspace_id>] [--connection <connection_id>] [--format table|json|csv]
+hotdata query "<sql>" [-w <workspace_id>] [--connection <connection_id>] [-o table|json|csv]
+hotdata query status <query_run_id> [-o table|json|csv]
 ```
-- Default format is `table`, which prints results with row count and execution time.
+- Default output is `table`, which prints results with row count and execution time.
 - Use `--connection` to scope the query to a specific connection.
 - Use `hotdata tables list` to discover tables and columns — do not query `information_schema` directly.
 - **Always use PostgreSQL dialect SQL.**
+- Long-running queries automatically fall back to async execution and return a `query_run_id`.
+- Use `hotdata query status <query_run_id>` to poll for results.
+- Exit codes for `query status`: `0` = succeeded, `1` = failed, `2` = still running (poll again).
+- **When a query returns a `query_run_id`, use `query status` to poll rather than re-running the query.**
 
 ### Get Query Result
 ```
-hotdata results <result_id> [--workspace-id <workspace_id>] [--format table|json|csv]
+hotdata results <result_id> [-w <workspace_id>] [-o table|json|csv]
 ```
 - Retrieves a previously executed query result by its result ID.
 - Query results include a `result-id` in the footer (e.g. `[result-id: rslt...]`).
@@ -195,23 +200,31 @@ hotdata queries run <query_id> [--format table|json|csv]
 
 ### Search
 ```
-hotdata search "<query>" --table <connection.schema.table> --column <column> [--select <columns>] [--limit <n>] [--format table|json|csv]
+# BM25 full-text search
+hotdata search "query text" --table <connection.schema.table> --column <column> [--select <columns>] [--limit <n>] [-o table|json|csv]
+
+# Vector search with --model (calls OpenAI to embed the query)
+hotdata search "query text" --table <table> --column <vector_column> --model text-embedding-3-small [--limit <n>]
+
+# Vector search with piped embedding
+echo '[0.1, -0.2, ...]' | hotdata search --table <table> --column <vector_column> [--limit <n>]
 ```
-- Full-text search using BM25 across a table column.
-- Requires a BM25 index on the target column (see `indexes create`).
-- Results are ordered by relevance score (descending).
-- `--select` specifies which columns to return (comma-separated, defaults to all). The `score` column is automatically appended when `--select` is used.
+- Without `--model` and with query text: BM25 full-text search. Requires a BM25 index on the target column.
+- With `--model`: generates an embedding via OpenAI and performs vector search using `l2_distance`. Requires `OPENAI_API_KEY` env var. Supported models: `text-embedding-3-small`, `text-embedding-3-large`.
+- Without query text and with piped stdin: reads a vector (raw JSON array or OpenAI embedding response) and performs vector search.
+- BM25 results are ordered by relevance score (descending). Vector results are ordered by distance (ascending).
+- `--select` specifies which columns to return (comma-separated, defaults to all).
 - Default limit is 10.
+- **For BM25 search, create a BM25 index on the target column first. For vector search, create a vector index.**
 
 ### Indexes
 ```
-hotdata indexes list --connection-id <id> --schema <schema> --table <table> [--workspace-id <workspace_id>] [--format table|json|yaml]
-hotdata indexes create --connection-id <id> --schema <schema> --table <table> --name <name> --columns <cols> [--type sorted|bm25|vector] [--metric l2|cosine|dot] [--async]
+hotdata indexes list -c <connection_id> --schema <schema> --table <table> [-w <workspace_id>] [-o table|json|yaml]
+hotdata indexes create -c <connection_id> --schema <schema> --table <table> --name <name> --columns <cols> [--type sorted|bm25|vector] [--metric l2|cosine|dot] [--async]
 ```
 - `list` shows indexes on a table with name, type, columns, status, and creation date.
 - `create` creates an index. Use `--type bm25` for full-text search, `--type vector` for vector search (requires `--metric`).
 - `--async` submits index creation as a background job. Use `hotdata jobs <job_id>` to check status.
-- **Before using `hotdata search`, create a BM25 index on the target column.**
 
 ### Jobs
 ```

src/command.rs

@@ -25,10 +25,10 @@ pub enum Commands {
         command: Option<DatasetsCommands>,
     },
 
-    /// Execute a SQL query
+    /// Execute a SQL query, or check status of a running query
     Query {
-        /// SQL query string
-        sql: String,
+        /// SQL query string (omit when using a subcommand)
+        sql: Option<String>,
 
         /// Workspace ID (defaults to first workspace from login)
         #[arg(long, short = 'w')]
@@ -41,6 +41,9 @@ pub enum Commands {
         /// Output format
         #[arg(long = "output", short = 'o', default_value = "table", value_parser = ["table", "json", "csv"])]
         output: String,
+
+        #[command(subcommand)]
+        command: Option<QueryCommands>,
     },
 
     /// Manage workspaces
@@ -187,6 +190,16 @@ impl From<ShellChoice> for clap_complete::Shell {
     }
 }
 
+#[derive(Subcommand)]
+pub enum QueryCommands {
+    /// Check the status of a running query and retrieve results.
+    /// Exit codes: 0 = succeeded, 1 = failed, 2 = still running (poll again)
+    Status {
+        /// Query run ID
+        id: String,
+    },
+}
+
 #[derive(Subcommand)]
 pub enum AuthCommands {
     /// Remove authentication for a profile

src/main.rs

@@ -19,7 +19,7 @@ mod workspace;
 
 use anstyle::AnsiColor;
 use clap::{Parser, builder::Styles};
-use command::{AuthCommands, Commands, ConnectionsCommands, ConnectionsCreateCommands, DatasetsCommands, IndexesCommands, JobsCommands, QueriesCommands, ResultsCommands, SkillCommands, TablesCommands, WorkspaceCommands};
+use command::{AuthCommands, Commands, ConnectionsCommands, ConnectionsCreateCommands, DatasetsCommands, IndexesCommands, JobsCommands, QueriesCommands, QueryCommands, ResultsCommands, SkillCommands, TablesCommands, WorkspaceCommands};
 
 #[derive(Parser)]
 #[command(name = "hotdata", version, about = concat!("Hotdata CLI - Command line interface for Hotdata (v", env!("CARGO_PKG_VERSION"), ")"), long_about = None, disable_version_flag = true)]
@@ -109,9 +109,24 @@ fn main() {
                     }
                 }
             }
-            Commands::Query { sql, workspace_id, connection, output } => {
+            Commands::Query { sql, workspace_id, connection, output, command } => {
                 let workspace_id = resolve_workspace(workspace_id);
-                query::execute(&sql, &workspace_id, connection.as_deref(), &output)
+                match command {
+                    Some(QueryCommands::Status { id }) => {
+                        query::poll(&id, &workspace_id, &output)
+                    }
+                    None => {
+                        match sql {
+                            Some(sql) => query::execute(&sql, &workspace_id, connection.as_deref(), &output),
+                            None => {
+                                use clap::CommandFactory;
+                                let mut cmd = Cli::command();
+                                cmd.build();
+                                cmd.find_subcommand_mut("query").unwrap().print_help().unwrap();
+                            }
+                        }
+                    }
+                }
             }
             Commands::Workspaces { command } => match command {
                 WorkspaceCommands::List { output } => workspace::list(&output),

src/query.rs

@@ -13,6 +13,21 @@ pub struct QueryResponse {
     pub warning: Option<String>,
 }
 
+#[derive(Deserialize)]
+struct AsyncResponse {
+    query_run_id: String,
+    status: String,
+}
+
+#[derive(Deserialize)]
+struct QueryRunResponse {
+    id: String,
+    status: String,
+    result_id: Option<String>,
+    #[serde(default)]
+    error: Option<String>,
+}
+
 fn value_to_string(v: &Value) -> String {
     match v {
         Value::Null => "NULL".to_string(),
@@ -33,12 +48,40 @@ fn value_to_string(v: &Value) -> String {
 pub fn execute(sql: &str, workspace_id: &str, connection: Option<&str>, format: &str) {
     let api = ApiClient::new(Some(workspace_id));
 
-    let mut body = serde_json::json!({ "sql": sql });
+    let mut body = serde_json::json!({
+        "sql": sql,
+        "async": true,
+        "async_after_ms": 1000,
+    });
     if let Some(conn) = connection {
         body["connection_id"] = Value::String(conn.to_string());
     }
 
+    let spinner = indicatif::ProgressBar::new_spinner();
+    spinner.set_style(
+        indicatif::ProgressStyle::with_template("{spinner:.cyan} {msg}")
+            .unwrap(),
+    );
+    spinner.set_message("running query...");
+    spinner.enable_steady_tick(std::time::Duration::from_millis(80));
+
     let (status, resp_body) = api.post_raw("/query", &body);
+    spinner.finish_and_clear();
+
+    if status.as_u16() == 202 {
+        let async_resp: AsyncResponse = match serde_json::from_str(&resp_body) {
+            Ok(r) => r,
+            Err(e) => {
+                eprintln!("error parsing async response: {e}");
+                std::process::exit(1);
+            }
+        };
+        use crossterm::style::Stylize;
+        eprintln!("{}", format!("query still running (status: {})", async_resp.status).yellow());
+        eprintln!("query_run_id: {}", async_resp.query_run_id);
+        eprintln!("{}", format!("Poll with: hotdata query status {}", async_resp.query_run_id).dark_grey());
+        std::process::exit(2);
+    }
 
     if !status.is_success() {
         let message = serde_json::from_str::<Value>(&resp_body)
@@ -61,6 +104,41 @@ pub fn execute(sql: &str, workspace_id: &str, connection: Option<&str>, format:
     print_result(&result, format);
 }
 
+/// Poll a query run by ID. If succeeded and has a result_id, fetch and display the result.
+pub fn poll(query_run_id: &str, workspace_id: &str, format: &str) {
+    let api = ApiClient::new(Some(workspace_id));
+
+    let run: QueryRunResponse = api.get(&format!("/query-runs/{query_run_id}"));
+
+    match run.status.as_str() {
+        "succeeded" => {
+            match run.result_id {
+                Some(ref result_id) => {
+                    let result: QueryResponse = api.get(&format!("/results/{result_id}"));
+                    print_result(&result, format);
+                }
+                None => {
+                    use crossterm::style::Stylize;
+                    println!("{}", "Query succeeded but no result available.".yellow());
+                }
+            }
+        }
+        "failed" => {
+            use crossterm::style::Stylize;
+            let err = run.error.as_deref().unwrap_or("unknown error");
+            eprintln!("{}", format!("query failed: {err}").red());
+            std::process::exit(1);
+        }
+        status => {
+            use crossterm::style::Stylize;
+            eprintln!("{}", format!("query status: {status}").yellow());
+            eprintln!("query_run_id: {}", run.id);
+            eprintln!("{}", format!("Poll again with: hotdata query status {}", run.id).dark_grey());
+            std::process::exit(2);
+        }
+    }
+}
+
 pub fn print_result(result: &QueryResponse, format: &str) {
     if let Some(ref warning) = result.warning {
         eprintln!("warning: {warning}");