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| |
| #ifndef IMPALA_EXEC_GROUPING_AGGREGATOR_H |
| #define IMPALA_EXEC_GROUPING_AGGREGATOR_H |
| |
| #include <deque> |
| #include <memory> |
| #include <vector> |
| |
| #include "exec/aggregator.h" |
| #include "exec/hash-table.h" |
| #include "runtime/buffered-tuple-stream.h" |
| #include "runtime/bufferpool/suballocator.h" |
| #include "runtime/descriptors.h" |
| #include "runtime/mem-pool.h" |
| #include "runtime/reservation-manager.h" |
| |
| namespace impala { |
| |
| class AggFnEvaluator; |
| class LlvmCodeGen; |
| class RowBatch; |
| class RuntimeState; |
| class TAggregator; |
| class Tuple; |
| |
| /// Aggregator for doing grouping aggregations. Input is passed to the aggregator through |
| /// AddBatch(), or AddBatchStreaming() if this is a pre-agg. Then: |
| /// 1. Each row is hashed and we pick a dst partition (hash_partitions_). |
| /// 2. If the dst partition is not spilled, we probe into the partitions hash table |
| /// to aggregate/insert the row. |
| /// 3. If the partition is already spilled, the input row is spilled. |
| /// 4. When all the input is consumed, we walk hash_partitions_, put the spilled ones |
| /// into spilled_partitions_ and the non-spilled ones into aggregated_partitions_. |
| /// aggregated_partitions_ contain partitions that are fully processed and the result |
| /// can just be returned. Partitions in spilled_partitions_ need to be repartitioned |
| /// and we just repeat these steps. |
| // |
| /// Each partition contains these structures: |
| /// 1) Hash Table for aggregated rows. This contains just the hash table directory |
| /// structure but not the rows themselves. This is NULL for spilled partitions when |
| /// we stop maintaining the hash table. |
| /// 2) MemPool for var-len result data for rows in the hash table. If the aggregate |
| /// function returns a string, we cannot append it to the tuple stream as that |
| /// structure is immutable. Instead, when we need to spill, we sweep and copy the |
| /// rows into a tuple stream. |
| /// 3) Aggregated tuple stream for rows that are/were in the hash table. This stream |
| /// contains rows that are aggregated. When the partition is not spilled, this stream |
| /// is pinned and contains the memory referenced by the hash table. |
| /// In the case where the aggregate function does not return a string (meaning the |
| /// size of all the slots is known when the row is constructed), this stream contains |
| /// all the memory for the result rows and the MemPool (2) is not used. |
| /// 4) Unaggregated tuple stream. Stream to spill unaggregated rows. |
| /// Rows in this stream always have child(0)'s layout. |
| /// |
| /// Buffering: Each stream and hash table needs to maintain at least one buffer when |
| /// it is being read or written. The streams for a given agg use a uniform buffer size, |
| /// except when processing rows larger than that buffer size. In that case, the agg uses |
| /// BufferedTupleStream's variable buffer size support to handle larger rows up to the |
| /// maximum row size. Only two max-sized buffers are needed for the agg to spill: one |
| /// to hold rows being read from a spilled input stream and another for a temporary write |
| /// buffer when adding a row to an output stream. |
| /// |
| /// Two-phase aggregation: we support two-phase distributed aggregations, where |
| /// pre-aggregrations attempt to reduce the size of data before shuffling data across the |
| /// network to be merged by the merge aggregation node. This aggregator supports a |
| /// streaming mode for pre-aggregations where it maintains a hash table of aggregated |
| /// rows, but can pass through unaggregated rows (after transforming them into the |
| /// same tuple format as aggregated rows) when a heuristic determines that it is better |
| /// to send rows across the network instead of consuming additional memory and CPU |
| /// resources to expand its hash table. The planner decides whether a given |
| /// pre-aggregation should use the streaming preaggregation algorithm or the same |
| /// blocking aggregation algorithm as used in merge aggregations. |
| /// TODO: make this less of a heuristic by factoring in the cost of the exchange vs the |
| /// cost of the pre-aggregation. |
| /// |
| /// Handling memory pressure: the node uses two different strategies for responding to |
| /// memory pressure, depending on whether it is a streaming pre-aggregation or not. If |
| /// the node is a streaming preaggregation, it stops growing its hash table further by |
| /// converting unaggregated rows into the aggregated tuple format and passing them |
| /// through. If the node is not a streaming pre-aggregation, it responds to memory |
| /// pressure by spilling partitions to disk. |
| /// |
| /// TODO: Buffer rows before probing into the hash table? |
| /// TODO: After spilling, we can still maintain a very small hash table just to remove |
| /// some number of rows (from likely going to disk). |
| /// TODO: Consider allowing to spill the hash table structure in addition to the rows. |
| /// TODO: Do we want to insert a buffer before probing into the partition's hash table? |
| /// TODO: Use a prefetch/batched probe interface. |
| /// TODO: Return rows from the aggregated_row_stream rather than the HT. |
| /// TODO: Think about spilling heuristic. |
| /// TODO: When processing a spilled partition, we have a lot more information and can |
| /// size the partitions/hash tables better. |
| /// TODO: Start with unpartitioned (single partition) and switch to partitioning and |
| /// spilling only if the size gets large, say larger than the LLC. |
| /// TODO: Simplify or cleanup the various uses of agg_fn_ctx, agg_fn_ctx_, and ctx. |
| /// There are so many contexts in use that a plain "ctx" variable should never be used. |
| /// Likewise, it's easy to mixup the agg fn ctxs, there should be a way to simplify this. |
| /// TODO: support an Init() method with an initial value in the UDAF interface. |
| class GroupingAggregator : public Aggregator { |
| public: |
| GroupingAggregator(ExecNode* exec_node, ObjectPool* pool, |
| const TAggregator& taggregator, const DescriptorTbl& descs, |
| int64_t estimated_input_cardinality, int agg_idx); |
| |
| virtual Status Init(const TAggregator& taggregator, RuntimeState* state, |
| const std::vector<TExpr>& conjuncts) override; |
| virtual Status Prepare(RuntimeState* state) override; |
| virtual void Codegen(RuntimeState* state) override; |
| virtual Status Open(RuntimeState* state) override; |
| virtual Status GetNext(RuntimeState* state, RowBatch* row_batch, bool* eos) override; |
| virtual Status Reset(RuntimeState* state, RowBatch* row_batch) override; |
| virtual void Close(RuntimeState* state) override; |
| |
| virtual Status AddBatch(RuntimeState* state, RowBatch* batch) override; |
| virtual Status AddBatchStreaming(RuntimeState* state, RowBatch* out_batch, |
| RowBatch* child_batch, bool* eos) override; |
| virtual Status InputDone() override; |
| |
| virtual int GetNumGroupingExprs() override { return grouping_exprs_.size(); } |
| |
| virtual void SetDebugOptions(const TDebugOptions& debug_options) override; |
| |
| virtual std::string DebugString(int indentation_level = 0) const override; |
| virtual void DebugString(int indentation_level, std::stringstream* out) const override; |
| |
| private: |
| struct Partition; |
| |
| /// Number of initial partitions to create. Must be a power of 2. |
| static const int PARTITION_FANOUT = 16; |
| |
| /// Needs to be the log(PARTITION_FANOUT). |
| /// We use the upper bits to pick the partition and lower bits in the HT. |
| /// TODO: different hash functions here too? We don't need that many bits to pick |
| /// the partition so this might be okay. |
| static const int NUM_PARTITIONING_BITS = 4; |
| |
| /// Maximum number of times we will repartition. The maximum build table we can process |
| /// (if we have enough scratch disk space) in case there is no skew is: |
| /// MEM_LIMIT * (PARTITION_FANOUT ^ MAX_PARTITION_DEPTH). |
| /// In the case where there is skew, repartitioning is unlikely to help (assuming a |
| /// reasonable hash function). |
| /// Note that we need to have at least as many SEED_PRIMES in HashTableCtx. |
| /// TODO: we can revisit and try harder to explicitly detect skew. |
| static const int MAX_PARTITION_DEPTH = 16; |
| |
| /// Default initial number of buckets in a hash table. |
| /// TODO: rethink this ? |
| static const int64_t PAGG_DEFAULT_HASH_TABLE_SZ = 1024; |
| |
| /// Codegen doesn't allow for automatic Status variables because then exception |
| /// handling code is needed to destruct the Status, and our function call substitution |
| /// doesn't know how to deal with the LLVM IR 'invoke' instruction. Workaround that by |
| /// placing the Status here so exceptions won't need to destruct it. |
| /// TODO: fix IMPALA-1948 and remove this. |
| Status add_batch_status_; |
| |
| /// Row with the intermediate tuple as its only tuple. |
| /// Construct a new row desc for preparing the build exprs because neither the child's |
| /// nor this node's output row desc may contain the intermediate tuple, e.g., |
| /// in a single-node plan with an intermediate tuple different from the output tuple. |
| /// Lives in the query state's obj_pool. |
| RowDescriptor intermediate_row_desc_; |
| |
| /// True if this is first phase of a two-phase distributed aggregation for which we |
| /// are doing a streaming preaggregation. |
| const bool is_streaming_preagg_; |
| |
| /// True if any of the evaluators require the serialize step. |
| bool needs_serialize_ = false; |
| |
| /// Exprs used to evaluate input rows |
| std::vector<ScalarExpr*> grouping_exprs_; |
| |
| /// Exprs used to insert constructed aggregation tuple into the hash table. |
| /// All the exprs are simply SlotRefs for the intermediate tuple. |
| std::vector<ScalarExpr*> build_exprs_; |
| |
| /// Indices of grouping exprs with var-len string types in grouping_exprs_. |
| /// We need to do more work for var-len expressions when allocating and spilling rows. |
| /// All var-len grouping exprs have type string. |
| std::vector<int> string_grouping_exprs_; |
| |
| RuntimeState* state_; |
| |
| /// Allocator for hash table memory. |
| std::unique_ptr<Suballocator> ht_allocator_; |
| |
| /// MemPool used to allocate memory during Close() when creating new output tuples. The |
| /// pool should not be Reset() to allow amortizing memory allocation over a series of |
| /// Reset()/Open()/GetNext()* calls. |
| std::unique_ptr<MemPool> tuple_pool_; |
| |
| /// The current partition and iterator to the next row in its hash table that we need |
| /// to return in GetNext(). If 'output_iterator_' is not AtEnd() then |
| /// 'output_partition_' is not nullptr. |
| Partition* output_partition_ = nullptr; |
| HashTable::Iterator output_iterator_; |
| |
| /// Resource information sent from the frontend. |
| const TBackendResourceProfile resource_profile_; |
| |
| std::unique_ptr<ReservationTracker> reservation_tracker_; |
| ReservationManager reservation_manager_; |
| BufferPool::ClientHandle* buffer_pool_client(); |
| |
| /// The number of rows that have been passed to AddBatch() or AddBatchStreaming(). |
| int64_t num_input_rows_ = 0; |
| |
| /// True if this aggregator is being executed in a subplan. |
| const bool is_in_subplan_; |
| |
| int64_t limit_; // -1: no limit |
| bool ReachedLimit() { return limit_ != -1 && num_rows_returned_ >= limit_; } |
| |
| typedef Status (*AddBatchImplFn)( |
| GroupingAggregator*, RowBatch*, TPrefetchMode::type, HashTableCtx*); |
| /// Jitted AddBatchImpl function pointer. Null if codegen is disabled. |
| AddBatchImplFn add_batch_impl_fn_ = nullptr; |
| |
| typedef Status (*AddBatchStreamingImplFn)(GroupingAggregator*, int, bool, |
| TPrefetchMode::type, RowBatch*, RowBatch*, HashTableCtx*, int[PARTITION_FANOUT]); |
| /// Jitted AddBatchStreamingImpl function pointer. Null if codegen is disabled. |
| AddBatchStreamingImplFn add_batch_streaming_impl_fn_ = nullptr; |
| |
| /// Total time spent resizing hash tables. |
| RuntimeProfile::Counter* ht_resize_timer_ = nullptr; |
| |
| /// Time spent returning the aggregated rows |
| RuntimeProfile::Counter* get_results_timer_ = nullptr; |
| |
| /// Counters and profile objects for HashTable stats |
| std::unique_ptr<HashTableStatsProfile> ht_stats_profile_; |
| |
| /// Total number of partitions created. |
| RuntimeProfile::Counter* partitions_created_ = nullptr; |
| |
| /// Level of max partition (i.e. number of repartitioning steps). |
| RuntimeProfile::HighWaterMarkCounter* max_partition_level_ = nullptr; |
| |
| /// Number of rows that have been repartitioned. |
| RuntimeProfile::Counter* num_row_repartitioned_ = nullptr; |
| |
| /// Number of partitions that have been repartitioned. |
| RuntimeProfile::Counter* num_repartitions_ = nullptr; |
| |
| /// Number of partitions that have been spilled. |
| RuntimeProfile::Counter* num_spilled_partitions_ = nullptr; |
| |
| /// The largest fraction after repartitioning. This is expected to be |
| /// 1 / PARTITION_FANOUT. A value much larger indicates skew. |
| RuntimeProfile::HighWaterMarkCounter* largest_partition_percent_ = nullptr; |
| |
| /// Time spent in streaming preagg algorithm. |
| RuntimeProfile::Counter* streaming_timer_ = nullptr; |
| |
| /// The number of rows passed through without aggregation. |
| RuntimeProfile::Counter* num_passthrough_rows_ = nullptr; |
| |
| /// The estimated reduction of the preaggregation. |
| RuntimeProfile::Counter* preagg_estimated_reduction_ = nullptr; |
| |
| /// Expose the minimum reduction factor to continue growing the hash tables. |
| RuntimeProfile::Counter* preagg_streaming_ht_min_reduction_ = nullptr; |
| |
| /// The estimated number of input rows from the planner. |
| int64_t estimated_input_cardinality_; |
| |
| TDebugOptions debug_options_; |
| |
| ///////////////////////////////////////// |
| /// BEGIN: Members that must be Reset() |
| |
| /// If true, no more rows to output from partitions. |
| bool partition_eos_ = false; |
| |
| /// When streaming rows through unaggregated, if the out batch reaches capacity before |
| /// the input batch is fully processed, 'streaming_idx_' indicates the position within |
| /// the input batch to resume at in the next call to AddBatchStreaming(). This is used |
| /// in the case where there are multiple aggregators, as the out batch passed to |
| /// AddBatchStreaming() may already have rows passed through by another aggregator. |
| int32_t streaming_idx_ = 0; |
| |
| /// Used for hash-related functionality, such as evaluating rows and calculating hashes. |
| /// It also owns the evaluators for the grouping and build expressions used during hash |
| /// table insertion and probing. |
| boost::scoped_ptr<HashTableCtx> ht_ctx_; |
| |
| /// Object pool that holds the Partition objects in hash_partitions_. |
| std::unique_ptr<ObjectPool> partition_pool_; |
| |
| /// Current partitions we are partitioning into. IMPALA-5788: For the case where we |
| /// rebuild a spilled partition that fits in memory, all pointers in this vector will |
| /// point to a single in-memory partition. |
| std::vector<Partition*> hash_partitions_; |
| |
| /// Cache for hash tables in 'hash_partitions_'. IMPALA-5788: For the case where we |
| /// rebuild a spilled partition that fits in memory, all pointers in this array will |
| /// point to the hash table that is a part of a single in-memory partition. |
| HashTable* hash_tbls_[PARTITION_FANOUT]; |
| |
| /// All partitions that have been spilled and need further processing. |
| std::deque<Partition*> spilled_partitions_; |
| |
| /// All partitions that are aggregated and can just return the results in GetNext(). |
| /// After consuming all the input, hash_partitions_ is split into spilled_partitions_ |
| /// and aggregated_partitions_, depending on if it was spilled or not. |
| std::deque<Partition*> aggregated_partitions_; |
| |
| /// END: Members that must be Reset() |
| ///////////////////////////////////////// |
| |
| /// The hash table and streams (aggregated and unaggregated) for an individual |
| /// partition. The streams of each partition always (i.e. regardless of level) |
| /// initially use small buffers. Streaming pre-aggregations do not spill and do not |
| /// require an unaggregated stream. |
| struct Partition { |
| Partition(GroupingAggregator* parent, int level, int idx) |
| : parent(parent), is_closed(false), level(level), idx(idx) {} |
| |
| ~Partition(); |
| |
| /// Initializes aggregated_row_stream and unaggregated_row_stream (if a spilling |
| /// aggregation), allocating one buffer for each. Spilling merge aggregations must |
| /// have enough reservation for the initial buffer for the stream, so this should |
| /// not fail due to OOM. Preaggregations do not reserve any buffers: if does not |
| /// have enough reservation for the initial buffer, the aggregated row stream is not |
| /// created and an OK status is returned. |
| Status InitStreams() WARN_UNUSED_RESULT; |
| |
| /// Initializes the hash table. 'aggregated_row_stream' must be non-NULL. |
| /// Sets 'got_memory' to true if the hash table was initialised or false on OOM. |
| /// After returning, 'hash_tbl' will be non-null iff 'got_memory' is true and the |
| /// returned status is OK. |
| Status InitHashTable(bool* got_memory) WARN_UNUSED_RESULT; |
| |
| /// Called in case we need to serialize aggregated rows. This step effectively does |
| /// a merge aggregation in this aggregator. |
| Status SerializeStreamForSpilling() WARN_UNUSED_RESULT; |
| |
| /// Closes this partition. If finalize_rows is true, this iterates over all rows |
| /// in aggregated_row_stream and finalizes them (this is only used in the cancellation |
| /// path). |
| void Close(bool finalize_rows); |
| |
| /// Spill this partition. 'more_aggregate_rows' = true means that more aggregate rows |
| /// may be appended to the the partition before appending unaggregated rows. On |
| /// success, one of the streams is left with a write iterator: the aggregated stream |
| /// if 'more_aggregate_rows' is true or the unaggregated stream otherwise. |
| Status Spill(bool more_aggregate_rows) WARN_UNUSED_RESULT; |
| |
| bool is_spilled() const { return hash_tbl.get() == nullptr; } |
| |
| GroupingAggregator* parent; |
| |
| /// If true, this partition is closed and there is nothing left to do. |
| bool is_closed; |
| |
| /// How many times rows in this partition have been repartitioned. Partitions created |
| /// from the aggregator's input is level 0, 1 after the first repartitionining, etc. |
| const int level; |
| |
| /// The index of this partition within 'hash_partitions_' at its level. |
| const int idx; |
| |
| /// Hash table for this partition. |
| /// Can be NULL if this partition is no longer maintaining a hash table (i.e. |
| /// is spilled or we are passing through all rows for this partition). |
| std::unique_ptr<HashTable> hash_tbl; |
| |
| /// Clone of parent's agg_fn_evals_. Permanent allocations come from |
| /// 'agg_fn_perm_pool' and result allocations come from 'expr_results_pool_'. |
| std::vector<AggFnEvaluator*> agg_fn_evals; |
| |
| /// Pool for permanent allocations for this partition's 'agg_fn_evals'. Freed at the |
| /// same times as 'agg_fn_evals' are closed: either when the partition is closed or |
| /// when it is spilled. |
| std::unique_ptr<MemPool> agg_fn_perm_pool; |
| |
| /// Tuple stream used to store aggregated rows. When the partition is not spilled, |
| /// (meaning the hash table is maintained), this stream is pinned and contains the |
| /// memory referenced by the hash table. When it is spilled, this consumes reservation |
| /// for a write buffer only during repartitioning of aggregated rows. |
| /// |
| /// For streaming preaggs, this may be NULL if sufficient memory is not available. |
| /// In that case hash_tbl is also NULL and all rows for the partition will be passed |
| /// through. |
| std::unique_ptr<BufferedTupleStream> aggregated_row_stream; |
| |
| /// Unaggregated rows that are spilled. Always NULL for streaming pre-aggregations. |
| /// Always unpinned. Has a write buffer allocated when the partition is spilled and |
| /// unaggregated rows are being processed. |
| std::unique_ptr<BufferedTupleStream> unaggregated_row_stream; |
| }; |
| |
| /// Stream used to store serialized spilled rows. Only used if needs_serialize_ |
| /// is set. This stream is never pinned and only used in Partition::Spill as a |
| /// a temporary buffer. |
| std::unique_ptr<BufferedTupleStream> serialize_stream_; |
| |
| /// Accessor for 'hash_tbls_' that verifies consistency with the partitions. |
| HashTable* ALWAYS_INLINE GetHashTable(int partition_idx) { |
| HashTable* ht = hash_tbls_[partition_idx]; |
| DCHECK_EQ(ht, hash_partitions_[partition_idx]->hash_tbl.get()); |
| return ht; |
| } |
| |
| /// Copies grouping values stored in 'ht_ctx_' that were computed over 'current_row_' |
| /// using 'grouping_expr_evals_'. Aggregation expr slots are set to their initial |
| /// values. Returns NULL if there was not enough memory to allocate the tuple or errors |
| /// occurred. In which case, 'status' is set. Allocates tuple and var-len data for |
| /// grouping exprs from stream. Var-len data for aggregate exprs is allocated from the |
| /// FunctionContexts, so is stored outside the stream. If stream's small buffers get |
| /// full, it will attempt to switch to IO-buffers. |
| Tuple* ConstructIntermediateTuple(const std::vector<AggFnEvaluator*>& agg_fn_evals, |
| BufferedTupleStream* stream, Status* status) noexcept; |
| |
| /// Constructs intermediate tuple, allocating memory from pool instead of the stream. |
| /// Returns NULL and sets status if there is not enough memory to allocate the tuple. |
| Tuple* ConstructIntermediateTuple(const std::vector<AggFnEvaluator*>& agg_fn_evals, |
| MemPool* pool, Status* status) noexcept; |
| |
| /// Returns the number of bytes of variable-length data for the grouping values stored |
| /// in 'ht_ctx_'. |
| int GroupingExprsVarlenSize(); |
| |
| /// Initializes intermediate tuple by copying grouping values stored in 'ht_ctx_' that |
| /// that were computed over 'current_row_' using 'grouping_expr_evals_'. Writes the |
| /// var-len data into buffer. 'buffer' points to the start of a buffer of at least the |
| /// size of the variable-length data: 'varlen_size'. |
| void CopyGroupingValues(Tuple* intermediate_tuple, uint8_t* buffer, int varlen_size); |
| |
| /// Processes a batch of rows. This is the core function of the algorithm. We partition |
| /// the rows into hash_partitions_, spilling as necessary. |
| /// If AGGREGATED_ROWS is true, it means that the rows in the batch are already |
| /// pre-aggregated. |
| /// 'prefetch_mode' specifies the prefetching mode in use. If it's not PREFETCH_NONE, |
| /// hash table buckets will be prefetched based on the hash values computed. Note |
| /// that 'prefetch_mode' will be substituted with constants during codegen time. |
| // |
| /// This function is replaced by codegen. We pass in ht_ctx_.get() as an argument for |
| /// performance. |
| template <bool AGGREGATED_ROWS> |
| Status IR_ALWAYS_INLINE AddBatchImpl(RowBatch* batch, TPrefetchMode::type prefetch_mode, |
| HashTableCtx* ht_ctx) WARN_UNUSED_RESULT; |
| |
| /// Evaluates the rows in 'batch' starting at 'start_row_idx' and stores the results in |
| /// the expression values cache in 'ht_ctx'. The number of rows evaluated depends on |
| /// the capacity of the cache. 'prefetch_mode' specifies the prefetching mode in use. |
| /// If it's not PREFETCH_NONE, hash table buckets for the computed hashes will be |
| /// prefetched. Note that codegen replaces 'prefetch_mode' with a constant. |
| template <bool AGGREGATED_ROWS> |
| void EvalAndHashPrefetchGroup(RowBatch* batch, int start_row_idx, |
| TPrefetchMode::type prefetch_mode, HashTableCtx* ht_ctx); |
| |
| /// This function processes each individual row in AddBatchImpl(). Must be inlined into |
| /// AddBatchImpl for codegen to substitute function calls with codegen'd versions. |
| /// May spill partitions if not enough memory is available. |
| template <bool AGGREGATED_ROWS> |
| Status IR_ALWAYS_INLINE ProcessRow( |
| TupleRow* row, HashTableCtx* ht_ctx) WARN_UNUSED_RESULT; |
| |
| /// Create a new intermediate tuple in partition, initialized with row. ht_ctx is |
| /// the context for the partition's hash table and hash is the precomputed hash of |
| /// the row. The row can be an unaggregated or aggregated row depending on |
| /// AGGREGATED_ROWS. Spills partitions if necessary to append the new intermediate |
| /// tuple to the partition's stream. Must be inlined into AddBatchImpl for codegen |
| /// to substitute function calls with codegen'd versions. insert_it is an iterator |
| /// for insertion returned from HashTable::FindBuildRowBucket(). |
| template <bool AGGREGATED_ROWS> |
| Status IR_ALWAYS_INLINE AddIntermediateTuple(Partition* partition, TupleRow* row, |
| uint32_t hash, HashTable::Iterator insert_it) WARN_UNUSED_RESULT; |
| |
| /// Append a row to a spilled partition. The row may be aggregated or unaggregated |
| /// according to AGGREGATED_ROWS. May spill partitions if needed to append the row |
| /// buffers. |
| template <bool AGGREGATED_ROWS> |
| Status IR_ALWAYS_INLINE AppendSpilledRow( |
| Partition* partition, TupleRow* row) WARN_UNUSED_RESULT; |
| |
| /// Reads all the rows from input_stream and process them by calling AddBatchImpl(). |
| template <bool AGGREGATED_ROWS> |
| Status ProcessStream(BufferedTupleStream* input_stream) WARN_UNUSED_RESULT; |
| |
| /// Get rows for the next rowbatch from the next partition. Sets 'partition_eos_' to |
| /// true if all rows from all partitions have been returned or the limit is reached. |
| Status GetRowsFromPartition( |
| RuntimeState* state, RowBatch* row_batch) WARN_UNUSED_RESULT; |
| |
| /// Return true if we should keep expanding hash tables in the preagg. If false, |
| /// the preagg should pass through any rows it can't fit in its tables. |
| bool ShouldExpandPreaggHashTables() const; |
| |
| /// Streaming processing of in_batch from child. Rows from child are either aggregated |
| /// into the hash table or added to 'out_batch' in the intermediate tuple format. |
| /// 'in_batch' is processed entirely, and 'out_batch' must have enough capacity to |
| /// store all of the rows in 'in_batch'. |
| /// 'agg_idx' and 'needs_serialize' are arguments so that codegen can replace them with |
| /// constants, rather than using the member variables of the same names. |
| /// 'prefetch_mode' specifies the prefetching mode in use. If it's not PREFETCH_NONE, |
| /// hash table buckets will be prefetched based on the hash values computed. Note |
| /// that 'prefetch_mode' will be substituted with constants during codegen time. |
| /// 'remaining_capacity' is an array with PARTITION_FANOUT entries with the number of |
| /// additional rows that can be added to the hash table per partition. It is updated |
| /// by AddBatchStreamingImpl() when it inserts new rows. |
| /// 'ht_ctx' is passed in as a way to avoid aliasing of 'this' confusing the optimiser. |
| Status AddBatchStreamingImpl(int agg_idx, bool needs_serialize, |
| TPrefetchMode::type prefetch_mode, RowBatch* in_batch, RowBatch* out_batch, |
| HashTableCtx* ht_ctx, int remaining_capacity[PARTITION_FANOUT]) WARN_UNUSED_RESULT; |
| |
| /// Tries to add intermediate to the hash table 'hash_tbl' of 'partition' for streaming |
| /// aggregation. The input row must have been evaluated with 'ht_ctx', with 'hash' set |
| /// to the corresponding hash. If the tuple already exists in the hash table, update |
| /// the tuple and return true. Otherwise try to create a new entry in the hash table, |
| /// returning true if successful or false if the table is full. 'remaining_capacity' |
| /// keeps track of how many more entries can be added to the hash table so we can avoid |
| /// retrying inserts. It is decremented if an insert succeeds and set to zero if an |
| /// insert fails. If an error occurs, returns false and sets 'status'. |
| bool IR_ALWAYS_INLINE TryAddToHashTable(HashTableCtx* ht_ctx, Partition* partition, |
| HashTable* hash_tbl, TupleRow* in_row, uint32_t hash, int* remaining_capacity, |
| Status* status) WARN_UNUSED_RESULT; |
| |
| /// Initializes hash_partitions_. 'level' is the level for the partitions to create. |
| /// If 'single_partition_idx' is provided, it must be a number in range |
| /// [0, PARTITION_FANOUT), and only that partition is created - all others point to it. |
| /// Also sets ht_ctx_'s level to 'level'. |
| Status CreateHashPartitions( |
| int level, int single_partition_idx = -1) WARN_UNUSED_RESULT; |
| |
| /// Ensure that hash tables for all in-memory partitions are large enough to fit |
| /// 'num_rows' additional hash table entries. If there is not enough memory to |
| /// resize the hash tables, may spill partitions. 'aggregated_rows' is true if |
| /// we're currently partitioning aggregated rows. |
| Status CheckAndResizeHashPartitions( |
| bool aggregated_rows, int num_rows, const HashTableCtx* ht_ctx) WARN_UNUSED_RESULT; |
| |
| /// Prepares the next partition to return results from. On return, this function |
| /// initializes output_iterator_ and output_partition_. This either removes |
| /// a partition from aggregated_partitions_ (and is done) or removes the next |
| /// partition from aggregated_partitions_ and repartitions it. |
| Status NextPartition() WARN_UNUSED_RESULT; |
| |
| /// Tries to build the first partition in 'spilled_partitions_'. |
| /// If successful, set *built_partition to the partition. The caller owns the partition |
| /// and is responsible for closing it. If unsuccessful because the partition could not |
| /// fit in memory, set *built_partition to NULL and append the spilled partition to the |
| /// head of 'spilled_partitions_' so it can be processed by |
| /// RepartitionSpilledPartition(). |
| Status BuildSpilledPartition(Partition** built_partition) WARN_UNUSED_RESULT; |
| |
| /// Repartitions the first partition in 'spilled_partitions_' into PARTITION_FANOUT |
| /// output partitions. On success, each output partition is either: |
| /// * closed, if no rows were added to the partition. |
| /// * in 'spilled_partitions_', if the partition spilled. |
| /// * in 'aggregated_partitions_', if the output partition was not spilled. |
| Status RepartitionSpilledPartition() WARN_UNUSED_RESULT; |
| |
| /// Picks a partition from 'hash_partitions_' to spill. 'more_aggregate_rows' is passed |
| /// to Partition::Spill() when spilling the partition. See the Partition::Spill() |
| /// comment for further explanation. |
| Status SpillPartition(bool more_aggregate_rows) WARN_UNUSED_RESULT; |
| |
| /// Moves the partitions in hash_partitions_ to aggregated_partitions_ or |
| /// spilled_partitions_. Partitions moved to spilled_partitions_ are unpinned. |
| /// input_rows is the number of input rows that have been repartitioned. |
| /// Used for diagnostics. |
| Status MoveHashPartitions(int64_t input_rows) WARN_UNUSED_RESULT; |
| |
| /// Adds a partition to the front of 'spilled_partitions_' for later processing. |
| /// 'spilled_partitions_' uses LIFO so more finely partitioned partitions are processed |
| /// first). This allows us to delete pages earlier and bottom out the recursion |
| /// earlier and also improves time locality of access to spilled data on disk. |
| Status PushSpilledPartition(Partition* partition) WARN_UNUSED_RESULT; |
| |
| /// Calls Close() on 'output_partition_' and every Partition in |
| /// 'aggregated_partitions_', 'spilled_partitions_', and 'hash_partitions_' and then |
| /// resets the lists, the vector, the partition pool, and 'output_iterator_'. |
| void ClosePartitions(); |
| |
| /// Calls finalizes on all tuples starting at 'it'. |
| void CleanupHashTbl( |
| const std::vector<AggFnEvaluator*>& agg_fn_evals, HashTable::Iterator it); |
| |
| /// Codegen the non-streaming add row batch loop. The loop has already been compiled to |
| /// IR and loaded into the codegen object. UpdateAggTuple has also been codegen'd to IR. |
| /// This function will modify the loop subsituting the statically compiled functions |
| /// with codegen'd ones. 'add_batch_impl_fn_' will be updated with the codegened |
| // function. |
| /// Assumes AGGREGATED_ROWS = false. |
| Status CodegenAddBatchImpl( |
| LlvmCodeGen* codegen, TPrefetchMode::type prefetch_mode) WARN_UNUSED_RESULT; |
| |
| /// Codegen the materialization loop for streaming preaggregations. |
| /// 'add_batch_streaming_impl_fn_' will be updated with the codegened function. |
| Status CodegenAddBatchStreamingImpl( |
| LlvmCodeGen* codegen, TPrefetchMode::type prefetch_mode) WARN_UNUSED_RESULT; |
| |
| /// Compute minimum buffer reservation for grouping aggregations. |
| /// We need one buffer per partition, which is used either as the write buffer for the |
| /// aggregated stream or the unaggregated stream. We need an additional buffer to read |
| /// the stream we are currently repartitioning. The read buffer needs to be a max-sized |
| /// buffer to hold a max-sized row and we need one max-sized write buffer that is used |
| /// temporarily to append a row to any stream. |
| /// |
| /// If we need to serialize, we need an additional buffer while spilling a partition |
| /// as the partitions aggregate stream needs to be serialized and rewritten. |
| /// We do not spill streaming preaggregations, so we do not need to reserve any buffers. |
| int64_t MinReservation() const; |
| }; |
| } // namespace impala |
| |
| #endif // IMPALA_EXEC_GROUPING_AGGREGATOR_H |