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+- Feature Name: Limit handling in DistSQL
+- Status: draft
+- Start Date: 2017-06-02
+- Authors: Radu Berinde
+- RFC PR: (PR # after acceptance of initial draft)
+- Cockroach Issue: (one or more # from the issue tracker)
+
+# Summary
+
+This RFC proposes a set of mechanisms for efficiently dealing with `LIMIT` in
+DistSQL.
+
+# Motivation
+
+First, we describe the current state of things.
+
+### Classic SQL ###
+
+There are two types of limits that can apply to a planNode:
+ - a *hard limit* is one where we know we only need **X** rows from a node.
+   Example: `SELECT * FROM t LIMIT 10`
+ - a *soft limit* is one where we have only an optimistic estimation for how
+   many rows we need. Notable cases which lead to soft limits:
+
+   * limits with filters. `SELCT * FROM t WHERE color = 'red' LIMIT 10`. Here
+     the limit of 10 at the scanNode is soft because we may need more rows if
+     some don't pass the filter.
+
+   * partial orders: `SELECT * FROM t ORDER BY pk1,val1 LIMIT 10`: we need to
+     sort but we already have a partial order (let's assume the table is ordered
+     by `pk1,pk2`). If our sort can take advantage of the partial order (and
+     only sort groups of rows that are equal on `pk1`), we only need to read 10
+     rows plus any rows that have the same `pk1` value as the 10th row. This
+     would ideally lead to a soft limit of 10 or slightly more on the
+     `scanNode`.
+
+     *Note: The DistSQL sorter is aware of partial orders but the classic SQL
+     sortNode is not, so this is not actually implemented yet.*
+
+How are the limits actually handled at the scanNode level? The row fetcher
+receives a "hint" about how many rows we need. If we have a hard limit of **X**,
+this is simply **X**; if we have a soft limit of **Y** this is currently **2Y**.
+In the future the multiplier here needs to be determined based on statistics
+(e.g. an estimation of the selectivity of a filter). The row fetcher calculates
+the maximum number of keys per row, based on the family configuration (or simply
+1 key per row for secondary indexes) and sizes the first KV batch accordingly.
+The scanNode returns all rows before scanning another batch. In the case of hard
+limits, we never need to scan another batch. In the case of a soft limit, we
+could; if that happens, the second batch is significantly larger. The third
+and subsequent batches have the maximum batch size (like unlimited scans). The
+growth of the batch sizes is a trade-off between performance in "good" scenarios
+(when we are close to the soft limit) and in "bad" scenarios (if the soft limit
+estimation turns out to be way off).
+
+### Distributed SQL ###
+
+DistSQL supports limits semantically: it can run any query with limits and it
+will produce the correct results. However, there are hurdles in implementing the
+kind of optimizations explained above, which can lead to dramatic performance
+degradation in many cases:
+ - DistSQL processors (a processor is roughly one of potentially multiple
+   instances corresponding to a planNode) output rows to streams, in parallel
+   with the consumers of those streams. There is no sequencing step where a
+   processor can stop and wait and see if the consumer needs more rows. In
+   addition, there are buffering effects on the streams which can cause a
+   processor to emit more rows than needed before the consumer even has a chance
+   to receive any of them. The TableReaders use the same row fetching mechanism
+   explained above, which means we will almost always read a second batch even
+   if we don't need to.
+ - there are multiple instances of TableReaders for a given table, each working
+   on the ranges are held by that node. Without a special way to plan limits, we
+   would read **X** rows from every node.
+ - the planning process itself has to go through all the ranges in a table,
+   which may be comparatively expensive if we only need to read a few rows
+   (especially as we scale up in cluster and table size).
+
+As a consequence, by default we don't use DistSQL for statements which involve
+limits that get propagated all the way to the scanNode. Note that there are 
+useful classes of statements that have limits but where we can't put an (even
+optimistic) limit on scanNodes; for example
+`SELECT * FROM t ORDER BY v LIMIT 10`, where we have to read the entire table in
+all cases. These classes of queries are run via DistSQL.
+
+The goal for this RFC is to find ways to work around the hurdles above to
+achieve performance comparable with Classic SQL in all limit cases.
+Specifically:
+ - read no more than **X** rows in hard limit cases, and only involve the
+   respective hosts in the computation;
+ - don't execute more batches than needed in soft limit cases.
+
+In addition, we want our solution to work well for the partial ordering case
+described above.
+
+
+# Detailed design
+
+The proposal has two main aspects: new ways to plan limit queries, and a
+mechanism for limiting batches for soft limits.
+
+## Planning ##
+
+We have two proposals here: on is simple and should be implemented in the
+short-term; the other is more complex and should be considered in the long
+(medium?) term.
+
+### Short-term proposal ###
+
+The simple proposal is to not distribute TableReaders for limited scans.
+Instead, plan a single TableReader on the host that holds the first range to
+scan. In the best case, we will only need to scan that first range. In the worst
+case, we may end up reading the entire table, and many of the reads will be
+remote reads.
+
+We believe that this solution is good for many common cases. It is at least as
+good as classic SQL for queries that only read rows from a single range. It also
+has the advantage that we only need to resolve one range during planning. The
+main problem is that in bad cases the table data will be "routed" through that
+first node; this can be very bad if that first node happens to be in a far away
+zone. This will need to be addressed as we become more optimized for widely
+distributed scenarios.
+
+### Long-term proposal ###
+
+The second proposal is to build a new *planning synchronizer*. A synchronizer is
+an existing concept in DistSQL: it merges multiple input streams into a single
+stream that a processor can use. This new synchronizer will operate as follows:
+ - it initially has one input stream (normally connected to a TableReader
+   configured to read the first block of ranges held by a node);
+ - once this stream completes (i.e. producer has no more rows), the synchronizer
+   (which is pre-configured with a list of spans) resolves the spans into the
+   next batch of ranges, and sets up a processor on the corresponding node;
+ - the synchronizer can proceed in this manner, setting up a new processor when
+   the last one completes; or it can do more advanced things like set up new
+   processors before the last stream completes, or set up multiple new
+   processors at the same time. It can monitor the number of rows passing
+   through in relation to the limit to make these decisions.
+
+Implementing this will require infrastructure for augmenting running flows with
+new processors (or running multiple flows per node for the same query). Note
+that we already need something along those lines for tolerating dying hosts.
+
+This solution comes with a planning question: on which node do we put this
+planning synchronizer? A reasonable first implementation is to always use the
+gateway node. More advanced decisions are possible here though (e.g. if the
+table is confined to a datacenter, the synchronizer should be in that
+datacenter).
+
+## Soft limit mechanism ##
+
+In addition to the new planning methods, we introduce a mechanism that allows
+processors to "pause" their work until the consumer tells us that it needs more
+data. This works as follows:
+ - once a TableReader with a soft limit sends all the rows in the current KV
+   batch, it sends a special control message that means "I may have more rows,
+   but I'm waiting for you to tell me you need them".
+ - a processor that receives this message can send back a control message
+   informing the producer that more rows are needed.
+ - all processors need to be able to handle this message and forward it to their
+   consumers as necessary. In many cases the processors can forward this to
+   their consumer and then react to the consumer's request for more rows. In
+   other cases (e.g. sorting with no partial order, aggregation), the processor
+   will always need to request more rows (this can also be used as a simple but
+   correct "default" implementation to allow incremental implementation.
+
+Note that we already have infrastructure to send control messages over processor
+streams (in both directions).
+
+One thing that we will need for implementing this is to enrich the RowFetcher
+interface to give the caller hints about batch boundaries. This is useful for
+classic SQL as well: index-joins would benefit from aligning primary table batch
+boundaries with index batch boundaries.
+
+# Drawbacks
+
+# Alternatives
+
+# Unresolved questions