Per-flow traffic measurement is critical for usage accounting, traffic
engineering, and anomaly detection.  Previous methodologies are either
based on random sampling (e.g., Cisco's NetFlow), which is inaccurate,
or only account for the ``elephants''.  We introduce a novel technique
for measuring per-flow traffic approximately, for all flows regardless
of their sizes, at very high-speed (say, OC768).  The core of this
technique is a novel data structure called Space Code Bloom Filter
(SCBF).  A SCBF is an approximate representation of a {\it multiset}; each
element in this multiset is a traffic flow and its multiplicity is the
number of packets in the flow.  The multiplicity of an element in the
multiset represented by SCBF can be estimated through either of two
mechanisms -- Maximum Likelihood Estimation (MLE) or Mean Value
Estimation (MVE). Through parameter tuning, SCBF allows for graceful
tradeoff between measurement accuracy and computational and storage
complexity.  SCBF also contributes to the foundation of data streaming
by introducing a new paradigm called blind streaming.  We evaluate the
performance of SCBF through mathematical analysis and through
experiments on packet traces gathered from a tier-1 ISP backbone.  Our
results demonstrate that SCBF achieves reasonable
measurement accuracy with very low storage and computational
complexity.