How to pass very large ID sets (500K–5M) to ClickHouse queries using clickhouse-cs (.NET)? any industry guidlines?

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Summary

A production engineering team encountered a scalability bottleneck when attempting to filter large datasets (500K to 5M IDs) in ClickHouse via the clickhouse-cs .NET driver. The initial approach of using a massive IN (...) clause failed due to HTTP payload limits and request size constraints. Furthermore, attempts to use Temporary Tables for staging IDs were undermined by an Application Load Balancer (ALB) that lacked session affinity, causing queries to land on different cluster nodes and resulting in “table not found” errors.

Root Cause

The failure stems from three architectural mismatches:

  • Protocol Limitations: Standard SQL IN clauses are sent as part of the query string via HTTP. A list of 5M integers exceeds the maximum allowed HTTP POST body size or header limits for most proxies and load balancers.
  • Distributed State Mismatch: Temporary tables in ClickHouse are local to the specific node that processed the CREATE statement. In a distributed cluster behind a stateless Load Balancer, there is no guarantee that the subsequent SELECT query will land on the same node.
  • Lack of Session Affinity: The .NET driver does not natively inject the SessionId into the HTTP headers in a way that allows an ALB to perform Sticky Session routing, breaking the lifecycle of session-based operations.

Why This Happens in Real Systems

This is a classic case of Stateful Logic vs. Stateless Infrastructure.

  • Load Balancer Abstraction: Modern infrastructure (ALBs, Nginx, Envoy) is designed to be stateless and distribute traffic evenly. Any attempt to use “session-based” database features without Layer 7 sticky sessions will fail intermittently.
  • The “Large Parameter” Trap: Developers often design for small sets (dozens or hundreds of IDs) where IN clauses are convenient. When scale shifts to millions, the O(N) complexity of query parsing becomes a bottleneck for the database engine itself.
  • Driver/Proxy Disconnect: There is often a gap between what a database driver provides (a session ID) and what the network infrastructure requires (a cookie or header) to maintain affinity.

Real-World Impact

  • Query Timeouts: Attempting to send massive strings causes high latency in the parsing stage of the ClickHouse query engine.
  • Intermittent Failures: Using temporary tables without session affinity leads to non-deterministic errors that are difficult to reproduce in local environments.
  • Resource Exhaustion: Large IN clauses consume significant RAM on the coordinator node just to hold the unparsed query string.

Example or Code (if necessary and relevant)

Instead of the failing IN clause pattern, the industry standard is the Staging Table + Join pattern.

// INCORRECT: Will fail for 5M IDs due to HTTP size and parsing overhead
var query = $"SELECT * FROM telemetry WHERE id IN ({largeIdList})";

// CORRECT: The "Staging Table" Pattern
// 1. Create a permanent or TTL-based staging table
// 2. Use ClickHouse's native block insertion (Bulk Insert)
// 3. Perform a JOIN between the main table and the staging table

public async Task<IEnumerable> GetLargeDataSetAsync(IEnumerable ids)
{
    var stagingTableName = $"filter_ids_{Guid.NewGuid().ToString("N")}";

    // Step 1: Create a table with a short TTL so it cleans itself up
    await _connection.ExecuteAsync($"CREATE TABLE {stagingTableName} (id UInt64) ENGINE = Memory TTL id + INTERVAL 1 HOUR");

    // Step 2: Bulk Insert (This uses the efficient ClickHouse protocol, not a giant string)
    using (var bulkCopy = _connection.CreateBulkCopy(stagingTableName))
    {
        await bulkCopy.WriteToServerAsync(ids);
    }

    // Step 3: Join with the main table
    var resultQuery = $@"
        SELECT t.* 
        FROM main_telemetry_table AS t
        INNER JOIN {stagingTableName} AS f ON t.id = f.id";

    return await _connection.QueryAsync(resultQuery);
}

How Senior Engineers Fix It

To build a generic, robust library, senior engineers move away from session-dependence and toward explicit state management:

  • Use Permanent Staging Tables: Instead of TEMPORARY tables, use standard tables with a TTL (Time To Live) clause (e.g., TTL event_date + INTERVAL 1 HOUR). This ensures the data is visible across the entire cluster, regardless of which node the query hits.
  • Bulk Insertion over String Building: Use the native ClickHouse bulk insert capabilities provided by the driver. This sends data in binary format in optimized chunks rather than as a massive, escaped SQL string.
  • Join Optimization: Ensure the JOIN is performed on the primary key or a well-indexed column. For massive datasets, consider using the GLOBAL JOIN keyword in ClickHouse to ensure the data is distributed correctly across the cluster.
  • Decouple Identity from Session: Stop relying on the Load Balancer to “remember” the connection. The library should treat every request as stateless and manage its own data lifecycle via uniquely named tables.

Why Juniors Miss It

  • Local vs. Distributed Mindset: Juniors often test against a single-node ClickHouse instance where TEMPORARY tables work perfectly, failing to realize that production is a distributed cluster.
  • The “SQL is SQL” Fallacy: They assume that if a query works in a management UI (like DBeaver), it will work via an API. They overlook the transport layer (HTTP) constraints.
  • Ignoring the Network: Juniors focus on the Database Logic but forget that the Network Infrastructure (ALB/Proxy) is a participant in the transaction that can strip or ignore session metadata.

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