 In this work we  completely characterize the approximability of all Boolean CSPs in the streaming model . Specifically, given f, \gamma and \beta we show that either (1) the (\gamma,\beta)-approximation version of Max-CSP(f) has a probabilistic  streaming algorithm using O(\log n) space, or (2) for every \varepsilon > 0 the (\gamma-\varepsilon,\beta+\varepsilon)-approximation version of Max-CSP(f) requires \Omega(n) space for probabilistic  streaming algorithms.
<meaning-changed>  In this work we  completely characterize the approximability of all Boolean CSPs in the streaming model . Specifically, given f, \gamma and \beta we show that either (1) the (\gamma,\beta)-approximation version of Max-CSP(f) has a probabilistic  streaming algorithm using O(\log n) space, or (2) for every \varepsilon > 0 the (\gamma-\varepsilon,\beta+\varepsilon)-approximation version of Max-CSP(f) requires \Omega(n) space for probabilistic  streaming algorithms.
 In this work we completely characterize the approximability of all Boolean CSPs in the streaming model . Specifically, given f, \gamma and \beta we show that either (1) the (\gamma,\beta)-approximation version of Max-CSP(f) has a probabilistic dynamic streaming algorithm using O(\log n) space, or (2) for every \varepsilon > 0 the (\gamma-\varepsilon,\beta+\varepsilon)-approximation version of Max-CSP(f) requires \Omega(n) space for probabilistic streaming algorithms.